Tanley-Wood-Project2
Jordan Tanley and Jonathan Wood 2022-07-05
Introduction - Jonathan
Data
The data in this analysis will be the online news popularity dataset. This data has a set of features on articles from Mashable.com over a two year period.
The goal of this project is to determine the number of shares (how many times the article was shared over social media) the article has. We will use this information to predict if an article can be popular by the number of shares.
Notable Variables
While there are 61 variables in the data set, we will not use all of them for this project. The notable variables are the following:
- “shares” - The number of shares the article has gotten over social media. This is the label or variable we want our models to predict for new articles
- “data_channel_is” - A set of variables that tells if the article is in a particular category, such as business, sports, or lifestyle.
- “weekday_is” - A set of variables that tells what day of the week the article was published on.
- “num_images” - The number of images within the article
- “num_videos” - The number of videos within the article
- “n_tokens_content” - The number of tokens (words) within the article’s contents
- “rate_positive_words” - The rate of the positive words in the article’s contents
- “rate_negative_words” - The rate of the negative words in the article’s contents
Methods
Multiple methods will be used for this project to predict the number of shares a new article can generate, including
- Linear regression
- Tree-based models
- Random forest
- Boosted tree
Data - Jordan
In order to read in the data using a relative path, be sure to have the data file saved in your working directory.
# read in the data
news <- read_csv("OnlineNewsPopularity/OnlineNewsPopularity.csv")
## Rows: 39644 Columns: 61
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): url
## dbl (60): timedelta, n_tokens_title, n_tokens_content, n_unique_tokens, n_non_stop_words, n_non_stop_unique_tokens, num_hrefs, num_self_hrefs, num_img...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# sneek peek at the dataset
news %>%
head() %>%
kable()
| url | timedelta | n_tokens_title | n_tokens_content | n_unique_tokens | n_non_stop_words | n_non_stop_unique_tokens | num_hrefs | num_self_hrefs | num_imgs | num_videos | average_token_length | num_keywords | data_channel_is_lifestyle | data_channel_is_entertainment | data_channel_is_bus | data_channel_is_socmed | data_channel_is_tech | data_channel_is_world | kw_min_min | kw_max_min | kw_avg_min | kw_min_max | kw_max_max | kw_avg_max | kw_min_avg | kw_max_avg | kw_avg_avg | self_reference_min_shares | self_reference_max_shares | self_reference_avg_sharess | weekday_is_monday | weekday_is_tuesday | weekday_is_wednesday | weekday_is_thursday | weekday_is_friday | weekday_is_saturday | weekday_is_sunday | is_weekend | LDA_00 | LDA_01 | LDA_02 | LDA_03 | LDA_04 | global_subjectivity | global_sentiment_polarity | global_rate_positive_words | global_rate_negative_words | rate_positive_words | rate_negative_words | avg_positive_polarity | min_positive_polarity | max_positive_polarity | avg_negative_polarity | min_negative_polarity | max_negative_polarity | title_subjectivity | title_sentiment_polarity | abs_title_subjectivity | abs_title_sentiment_polarity | shares |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| http://mashable.com/2013/01/07/amazon-instant-video-browser/ | 731 | 12 | 219 | 0.6635945 | 1 | 0.8153846 | 4 | 2 | 1 | 0 | 4.680365 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 496 | 496 | 496.000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5003312 | 0.3782789 | 0.0400047 | 0.0412626 | 0.0401225 | 0.5216171 | 0.0925620 | 0.0456621 | 0.0136986 | 0.7692308 | 0.2307692 | 0.3786364 | 0.1000000 | 0.7 | -0.3500000 | -0.600 | -0.2000000 | 0.5000000 | -0.1875000 | 0.0000000 | 0.1875000 | 593 |
| http://mashable.com/2013/01/07/ap-samsung-sponsored-tweets/ | 731 | 9 | 255 | 0.6047431 | 1 | 0.7919463 | 3 | 1 | 1 | 0 | 4.913725 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7997557 | 0.0500467 | 0.0500963 | 0.0501007 | 0.0500007 | 0.3412458 | 0.1489478 | 0.0431373 | 0.0156863 | 0.7333333 | 0.2666667 | 0.2869146 | 0.0333333 | 0.7 | -0.1187500 | -0.125 | -0.1000000 | 0.0000000 | 0.0000000 | 0.5000000 | 0.0000000 | 711 |
| http://mashable.com/2013/01/07/apple-40-billion-app-downloads/ | 731 | 9 | 211 | 0.5751295 | 1 | 0.6638655 | 3 | 1 | 1 | 0 | 4.393365 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 918 | 918 | 918.000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2177923 | 0.0333345 | 0.0333514 | 0.0333335 | 0.6821883 | 0.7022222 | 0.3233333 | 0.0568720 | 0.0094787 | 0.8571429 | 0.1428571 | 0.4958333 | 0.1000000 | 1.0 | -0.4666667 | -0.800 | -0.1333333 | 0.0000000 | 0.0000000 | 0.5000000 | 0.0000000 | 1500 |
| http://mashable.com/2013/01/07/astronaut-notre-dame-bcs/ | 731 | 9 | 531 | 0.5037879 | 1 | 0.6656347 | 9 | 0 | 1 | 0 | 4.404896 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0285732 | 0.4192996 | 0.4946508 | 0.0289047 | 0.0285716 | 0.4298497 | 0.1007047 | 0.0414313 | 0.0207156 | 0.6666667 | 0.3333333 | 0.3859652 | 0.1363636 | 0.8 | -0.3696970 | -0.600 | -0.1666667 | 0.0000000 | 0.0000000 | 0.5000000 | 0.0000000 | 1200 |
| http://mashable.com/2013/01/07/att-u-verse-apps/ | 731 | 13 | 1072 | 0.4156456 | 1 | 0.5408895 | 19 | 19 | 20 | 0 | 4.682836 | 7 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 545 | 16000 | 3151.158 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0286328 | 0.0287936 | 0.0285752 | 0.0285717 | 0.8854268 | 0.5135021 | 0.2810035 | 0.0746269 | 0.0121269 | 0.8602151 | 0.1397849 | 0.4111274 | 0.0333333 | 1.0 | -0.2201923 | -0.500 | -0.0500000 | 0.4545455 | 0.1363636 | 0.0454545 | 0.1363636 | 505 |
| http://mashable.com/2013/01/07/beewi-smart-toys/ | 731 | 10 | 370 | 0.5598886 | 1 | 0.6981982 | 2 | 2 | 0 | 0 | 4.359459 | 9 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8500 | 8500 | 8500.000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0222453 | 0.3067176 | 0.0222313 | 0.0222243 | 0.6265816 | 0.4374086 | 0.0711842 | 0.0297297 | 0.0270270 | 0.5238095 | 0.4761905 | 0.3506100 | 0.1363636 | 0.6 | -0.1950000 | -0.400 | -0.1000000 | 0.6428571 | 0.2142857 | 0.1428571 | 0.2142857 | 855 |
# Creating a weekday variable (basically undoing the 7 dummy variables that came with the data) for EDA
news$weekday <- ifelse(news$weekday_is_friday == 1, "Friday",
ifelse(news$weekday_is_monday == 1, "Monday",
ifelse(news$weekday_is_tuesday == 1, "Tuesday",
ifelse(news$weekday_is_wednesday == 1, "Wednesday",
ifelse(news$weekday_is_thursday == 1, "Thursday",
ifelse(news$weekday_is_saturday == 1, "Saturday",
"Sunday"))))))
Next, let’s subset the data so that we can only look at the data channel of interest. We will look at articles with the “Social Media” data channel.
# Subset the data to one of the parameterized data channels and drop unnecessary variables
chan <- paste0("data_channel_is_", params$channel)
print(chan)
## [1] "data_channel_is_lifestyle"
filtered_channel <- news %>%
as_tibble() %>%
filter(news[chan] == 1) %>%
select(-c(url, timedelta))
# take a peek at the data
filtered_channel %>%
select(ends_with(chan))
Summarizations - Both (at least 3 plots each)
For the numerical summaries, we can look at several aspects. Contingency tables allow us to examine frequencies of categorical variables. The first output below, for example, shows the counts for each weekday. Similarly, the fifth table outputted shows the frequencies of number of tokens in the article content. Another set of summary statistics to look at are the 5 Number Summaries. These provide the minmum, 1st quantile, median, 3rd quantile, and maximum for a particular variable. Additionally, it may also be helful to look at the average. These are helpful in determining the skewness (if mean = median vs. mean < or > median) and helps in looking for outliers (anything outside (Q3 - Q1)1.5 from the median is generally considered an outlier). Below, the 5 Number summaries (plus mean) are shown for Shares, Number of words in the content, Number of words in the content for the upper quantile of Shares, number of images in the article, number of videos in the article, positive word rate, and negative word rate.
# Contingency table of frequencies for days of the week, added caption for clarity
kable(table(filtered_channel$weekday),
col.names = c("Weekday", "Frequency"),
caption = "Contingency table of frequencies for days of the week")
| Weekday | Frequency |
|---|---|
| Friday | 305 |
| Monday | 322 |
| Saturday | 182 |
| Sunday | 210 |
| Thursday | 358 |
| Tuesday | 334 |
| Wednesday | 388 |
Contingency table of frequencies for days of the week
# Numerical Summary of Shares, added caption for clarity
filtered_channel %>% summarise(Minimum = min(shares),
Q1 = quantile(shares, prob = 0.25),
Average = mean(shares),
Median = median(shares),
Q3 = quantile(shares, prob = 0.75),
Maximum = max(shares)) %>%
kable(caption = "Numerical Summary of Shares")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 28 | 1100 | 3682.123 | 1700 | 3250 | 208300 |
Numerical Summary of Shares
# Numerical Summary of Number of words in the content, added caption for clarity
filtered_channel %>% summarise(Minimum = min(n_tokens_content),
Q1 = quantile(n_tokens_content, prob = 0.25),
Average = mean(n_tokens_content),
Median = median(n_tokens_content),
Q3 = quantile(n_tokens_content, prob = 0.75),
Maximum = max(n_tokens_content)) %>%
kable(caption = "Numerical Summary of Number of words in the content")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 308.5 | 621.3273 | 502 | 795 | 8474 |
Numerical Summary of Number of words in the content
# Numerical Summary of Number of words in the content for the upper quantile of Shares, added caption for clarity
filtered_channel %>% filter(shares > quantile(shares, prob = 0.75)) %>%
summarise(Minimum = min(n_tokens_content),
Q1 = quantile(n_tokens_content, prob = 0.25),
Average = mean(n_tokens_content),
Median = median(n_tokens_content),
Q3 = quantile(n_tokens_content, prob = 0.75),
Maximum = max(n_tokens_content)) %>%
kable(caption = "Numerical Summary of Number of words in the content for the upper quantile of Shares")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 306 | 679.1752 | 505 | 883 | 8474 |
Numerical Summary of Number of words in the content for the upper quantile of Shares
kable(table(filtered_channel$n_tokens_content),
col.names = c("Tokens", "Frequency"),
caption = "Contingency table of frequencies for number of tokens in the article content")
| Tokens | Frequency |
|---|---|
| 0 | 22 |
| 68 | 1 |
| 77 | 1 |
| 81 | 2 |
| 85 | 1 |
| 89 | 1 |
| 91 | 2 |
| 96 | 1 |
| 97 | 1 |
| 98 | 1 |
| 99 | 2 |
| 101 | 1 |
| 102 | 1 |
| 103 | 1 |
| 105 | 2 |
| 106 | 1 |
| 107 | 1 |
| 110 | 2 |
| 112 | 1 |
| 113 | 2 |
| 114 | 2 |
| 115 | 3 |
| 116 | 5 |
| 117 | 2 |
| 118 | 1 |
| 120 | 1 |
| 121 | 3 |
| 123 | 6 |
| 124 | 1 |
| 126 | 1 |
| 128 | 1 |
| 129 | 1 |
| 130 | 1 |
| 131 | 2 |
| 132 | 1 |
| 135 | 1 |
| 137 | 3 |
| 138 | 3 |
| 139 | 2 |
| 140 | 2 |
| 141 | 3 |
| 143 | 1 |
| 144 | 2 |
| 145 | 1 |
| 146 | 2 |
| 147 | 3 |
| 149 | 4 |
| 150 | 3 |
| 151 | 1 |
| 153 | 1 |
| 154 | 3 |
| 155 | 1 |
| 156 | 2 |
| 157 | 2 |
| 158 | 3 |
| 159 | 4 |
| 160 | 1 |
| 161 | 2 |
| 163 | 3 |
| 165 | 1 |
| 166 | 2 |
| 167 | 1 |
| 169 | 4 |
| 171 | 4 |
| 172 | 1 |
| 173 | 3 |
| 174 | 4 |
| 175 | 4 |
| 178 | 3 |
| 179 | 3 |
| 180 | 2 |
| 181 | 1 |
| 182 | 2 |
| 183 | 2 |
| 184 | 3 |
| 187 | 3 |
| 188 | 1 |
| 189 | 2 |
| 191 | 2 |
| 192 | 1 |
| 193 | 2 |
| 194 | 2 |
| 195 | 5 |
| 196 | 1 |
| 197 | 5 |
| 198 | 2 |
| 199 | 4 |
| 200 | 2 |
| 202 | 4 |
| 203 | 5 |
| 204 | 5 |
| 205 | 1 |
| 206 | 4 |
| 207 | 3 |
| 208 | 1 |
| 209 | 2 |
| 210 | 6 |
| 211 | 3 |
| 212 | 1 |
| 213 | 3 |
| 214 | 6 |
| 215 | 4 |
| 216 | 4 |
| 217 | 3 |
| 218 | 2 |
| 219 | 1 |
| 221 | 2 |
| 222 | 7 |
| 223 | 2 |
| 224 | 3 |
| 225 | 1 |
| 226 | 4 |
| 228 | 2 |
| 229 | 3 |
| 230 | 4 |
| 231 | 2 |
| 232 | 3 |
| 234 | 2 |
| 235 | 4 |
| 236 | 2 |
| 237 | 4 |
| 238 | 2 |
| 239 | 3 |
| 240 | 4 |
| 241 | 3 |
| 242 | 3 |
| 243 | 7 |
| 244 | 1 |
| 246 | 3 |
| 247 | 4 |
| 249 | 6 |
| 250 | 1 |
| 251 | 1 |
| 252 | 3 |
| 253 | 2 |
| 254 | 4 |
| 255 | 2 |
| 256 | 7 |
| 257 | 1 |
| 258 | 6 |
| 259 | 2 |
| 260 | 1 |
| 261 | 7 |
| 262 | 2 |
| 263 | 3 |
| 264 | 2 |
| 265 | 6 |
| 266 | 6 |
| 267 | 4 |
| 268 | 6 |
| 269 | 4 |
| 270 | 5 |
| 271 | 3 |
| 272 | 5 |
| 273 | 5 |
| 275 | 1 |
| 276 | 9 |
| 277 | 3 |
| 278 | 3 |
| 279 | 4 |
| 280 | 2 |
| 281 | 2 |
| 282 | 4 |
| 283 | 5 |
| 285 | 1 |
| 286 | 2 |
| 287 | 3 |
| 289 | 2 |
| 290 | 1 |
| 291 | 6 |
| 292 | 5 |
| 293 | 4 |
| 294 | 5 |
| 295 | 2 |
| 296 | 1 |
| 297 | 5 |
| 298 | 2 |
| 299 | 3 |
| 300 | 4 |
| 301 | 2 |
| 303 | 1 |
| 304 | 1 |
| 305 | 4 |
| 306 | 3 |
| 307 | 2 |
| 308 | 3 |
| 309 | 3 |
| 310 | 2 |
| 311 | 2 |
| 312 | 3 |
| 313 | 4 |
| 314 | 1 |
| 315 | 5 |
| 316 | 3 |
| 317 | 4 |
| 318 | 6 |
| 319 | 3 |
| 320 | 3 |
| 322 | 3 |
| 323 | 4 |
| 324 | 1 |
| 325 | 7 |
| 326 | 5 |
| 327 | 3 |
| 328 | 2 |
| 329 | 2 |
| 330 | 3 |
| 331 | 4 |
| 332 | 6 |
| 333 | 1 |
| 334 | 4 |
| 335 | 5 |
| 336 | 3 |
| 337 | 5 |
| 338 | 5 |
| 339 | 3 |
| 340 | 4 |
| 341 | 2 |
| 342 | 8 |
| 344 | 4 |
| 345 | 3 |
| 346 | 2 |
| 347 | 4 |
| 348 | 4 |
| 349 | 2 |
| 350 | 4 |
| 351 | 2 |
| 352 | 5 |
| 353 | 4 |
| 354 | 5 |
| 355 | 4 |
| 356 | 3 |
| 357 | 2 |
| 358 | 2 |
| 359 | 1 |
| 360 | 3 |
| 361 | 2 |
| 362 | 3 |
| 363 | 1 |
| 364 | 3 |
| 365 | 3 |
| 366 | 3 |
| 367 | 5 |
| 368 | 5 |
| 369 | 1 |
| 370 | 4 |
| 371 | 1 |
| 372 | 1 |
| 373 | 1 |
| 374 | 2 |
| 375 | 2 |
| 376 | 4 |
| 377 | 1 |
| 378 | 5 |
| 379 | 5 |
| 380 | 3 |
| 381 | 3 |
| 382 | 5 |
| 383 | 5 |
| 385 | 1 |
| 386 | 1 |
| 387 | 3 |
| 388 | 3 |
| 389 | 1 |
| 390 | 2 |
| 391 | 2 |
| 393 | 3 |
| 394 | 4 |
| 395 | 5 |
| 396 | 2 |
| 397 | 5 |
| 398 | 4 |
| 399 | 6 |
| 400 | 3 |
| 401 | 2 |
| 402 | 3 |
| 403 | 3 |
| 404 | 2 |
| 405 | 3 |
| 406 | 3 |
| 407 | 3 |
| 408 | 4 |
| 409 | 6 |
| 410 | 4 |
| 411 | 4 |
| 412 | 4 |
| 413 | 3 |
| 414 | 4 |
| 415 | 2 |
| 416 | 3 |
| 417 | 2 |
| 418 | 3 |
| 419 | 2 |
| 420 | 3 |
| 421 | 1 |
| 422 | 1 |
| 423 | 3 |
| 424 | 1 |
| 425 | 1 |
| 427 | 2 |
| 428 | 4 |
| 429 | 3 |
| 430 | 3 |
| 432 | 2 |
| 434 | 4 |
| 436 | 2 |
| 437 | 1 |
| 438 | 1 |
| 439 | 2 |
| 440 | 5 |
| 441 | 4 |
| 442 | 2 |
| 443 | 2 |
| 444 | 1 |
| 445 | 4 |
| 446 | 6 |
| 447 | 2 |
| 448 | 10 |
| 451 | 1 |
| 452 | 3 |
| 453 | 5 |
| 454 | 2 |
| 455 | 1 |
| 456 | 2 |
| 457 | 6 |
| 458 | 2 |
| 459 | 4 |
| 460 | 1 |
| 461 | 2 |
| 463 | 3 |
| 464 | 2 |
| 465 | 1 |
| 466 | 2 |
| 467 | 1 |
| 468 | 3 |
| 469 | 1 |
| 470 | 1 |
| 471 | 3 |
| 473 | 2 |
| 475 | 1 |
| 476 | 2 |
| 477 | 1 |
| 478 | 2 |
| 480 | 4 |
| 481 | 2 |
| 482 | 2 |
| 483 | 3 |
| 484 | 1 |
| 485 | 5 |
| 486 | 4 |
| 487 | 3 |
| 488 | 2 |
| 489 | 2 |
| 490 | 4 |
| 491 | 2 |
| 492 | 1 |
| 494 | 1 |
| 495 | 2 |
| 496 | 2 |
| 497 | 2 |
| 498 | 4 |
| 499 | 4 |
| 500 | 2 |
| 501 | 2 |
| 502 | 2 |
| 503 | 4 |
| 504 | 2 |
| 505 | 4 |
| 506 | 3 |
| 507 | 1 |
| 508 | 2 |
| 509 | 3 |
| 510 | 4 |
| 511 | 4 |
| 512 | 1 |
| 513 | 3 |
| 514 | 4 |
| 516 | 3 |
| 517 | 4 |
| 518 | 4 |
| 519 | 1 |
| 521 | 2 |
| 522 | 2 |
| 524 | 1 |
| 525 | 1 |
| 526 | 1 |
| 527 | 4 |
| 528 | 1 |
| 529 | 4 |
| 530 | 5 |
| 531 | 2 |
| 532 | 1 |
| 533 | 2 |
| 534 | 3 |
| 535 | 6 |
| 536 | 3 |
| 537 | 1 |
| 538 | 2 |
| 539 | 3 |
| 540 | 2 |
| 541 | 3 |
| 542 | 2 |
| 543 | 2 |
| 544 | 4 |
| 545 | 2 |
| 546 | 1 |
| 547 | 1 |
| 548 | 7 |
| 549 | 5 |
| 550 | 2 |
| 551 | 2 |
| 552 | 1 |
| 554 | 3 |
| 555 | 3 |
| 556 | 1 |
| 557 | 4 |
| 558 | 1 |
| 560 | 2 |
| 561 | 2 |
| 562 | 3 |
| 563 | 1 |
| 564 | 1 |
| 565 | 3 |
| 566 | 1 |
| 567 | 3 |
| 568 | 4 |
| 569 | 2 |
| 570 | 3 |
| 571 | 6 |
| 572 | 2 |
| 573 | 3 |
| 574 | 2 |
| 575 | 3 |
| 576 | 4 |
| 577 | 2 |
| 578 | 3 |
| 579 | 1 |
| 580 | 3 |
| 581 | 1 |
| 582 | 3 |
| 583 | 3 |
| 584 | 3 |
| 586 | 1 |
| 587 | 1 |
| 588 | 2 |
| 589 | 4 |
| 590 | 2 |
| 591 | 4 |
| 592 | 2 |
| 593 | 1 |
| 594 | 3 |
| 595 | 2 |
| 596 | 2 |
| 598 | 1 |
| 599 | 3 |
| 600 | 1 |
| 601 | 3 |
| 602 | 2 |
| 603 | 1 |
| 604 | 2 |
| 605 | 1 |
| 606 | 2 |
| 607 | 1 |
| 608 | 1 |
| 609 | 2 |
| 610 | 2 |
| 611 | 3 |
| 612 | 3 |
| 613 | 2 |
| 614 | 3 |
| 615 | 3 |
| 616 | 1 |
| 619 | 2 |
| 620 | 2 |
| 621 | 1 |
| 622 | 2 |
| 623 | 1 |
| 624 | 2 |
| 625 | 2 |
| 626 | 3 |
| 627 | 2 |
| 628 | 2 |
| 629 | 1 |
| 630 | 1 |
| 631 | 1 |
| 632 | 2 |
| 633 | 1 |
| 634 | 1 |
| 635 | 1 |
| 636 | 3 |
| 637 | 3 |
| 638 | 4 |
| 639 | 5 |
| 640 | 2 |
| 641 | 2 |
| 642 | 1 |
| 643 | 1 |
| 645 | 3 |
| 646 | 2 |
| 647 | 2 |
| 651 | 2 |
| 652 | 2 |
| 653 | 2 |
| 654 | 1 |
| 655 | 1 |
| 659 | 2 |
| 660 | 3 |
| 661 | 3 |
| 662 | 1 |
| 663 | 5 |
| 664 | 2 |
| 665 | 1 |
| 666 | 1 |
| 667 | 2 |
| 668 | 1 |
| 670 | 3 |
| 671 | 2 |
| 672 | 3 |
| 673 | 3 |
| 674 | 1 |
| 675 | 3 |
| 676 | 1 |
| 677 | 2 |
| 679 | 1 |
| 680 | 1 |
| 681 | 1 |
| 682 | 1 |
| 683 | 4 |
| 687 | 2 |
| 689 | 2 |
| 690 | 3 |
| 691 | 3 |
| 692 | 1 |
| 695 | 4 |
| 696 | 2 |
| 697 | 2 |
| 698 | 4 |
| 699 | 3 |
| 702 | 3 |
| 703 | 1 |
| 704 | 2 |
| 706 | 1 |
| 707 | 4 |
| 708 | 3 |
| 710 | 1 |
| 712 | 3 |
| 713 | 2 |
| 715 | 2 |
| 716 | 3 |
| 717 | 2 |
| 719 | 3 |
| 720 | 3 |
| 723 | 2 |
| 724 | 1 |
| 725 | 3 |
| 726 | 3 |
| 727 | 2 |
| 728 | 1 |
| 729 | 1 |
| 730 | 2 |
| 731 | 3 |
| 732 | 5 |
| 733 | 1 |
| 734 | 4 |
| 736 | 1 |
| 739 | 3 |
| 743 | 2 |
| 744 | 1 |
| 745 | 2 |
| 746 | 1 |
| 747 | 1 |
| 748 | 1 |
| 749 | 3 |
| 752 | 1 |
| 754 | 2 |
| 755 | 1 |
| 756 | 1 |
| 757 | 2 |
| 759 | 2 |
| 760 | 1 |
| 762 | 5 |
| 763 | 3 |
| 764 | 1 |
| 768 | 4 |
| 769 | 2 |
| 771 | 1 |
| 772 | 1 |
| 773 | 1 |
| 774 | 3 |
| 776 | 2 |
| 781 | 1 |
| 783 | 2 |
| 784 | 2 |
| 785 | 1 |
| 788 | 1 |
| 791 | 1 |
| 792 | 2 |
| 793 | 3 |
| 794 | 2 |
| 795 | 2 |
| 796 | 1 |
| 799 | 1 |
| 801 | 2 |
| 802 | 1 |
| 803 | 2 |
| 804 | 2 |
| 806 | 2 |
| 807 | 3 |
| 808 | 2 |
| 809 | 3 |
| 810 | 1 |
| 811 | 1 |
| 812 | 2 |
| 814 | 2 |
| 816 | 1 |
| 817 | 2 |
| 818 | 2 |
| 819 | 3 |
| 820 | 1 |
| 822 | 1 |
| 823 | 1 |
| 824 | 3 |
| 825 | 1 |
| 826 | 1 |
| 827 | 1 |
| 828 | 2 |
| 830 | 2 |
| 831 | 2 |
| 832 | 2 |
| 837 | 1 |
| 839 | 1 |
| 840 | 2 |
| 841 | 1 |
| 842 | 1 |
| 843 | 1 |
| 847 | 2 |
| 851 | 2 |
| 852 | 1 |
| 854 | 2 |
| 855 | 2 |
| 857 | 2 |
| 858 | 1 |
| 859 | 5 |
| 860 | 2 |
| 861 | 2 |
| 863 | 7 |
| 864 | 2 |
| 865 | 1 |
| 868 | 1 |
| 869 | 1 |
| 870 | 1 |
| 871 | 1 |
| 872 | 2 |
| 873 | 3 |
| 874 | 1 |
| 877 | 1 |
| 878 | 2 |
| 879 | 1 |
| 880 | 1 |
| 881 | 2 |
| 883 | 3 |
| 886 | 1 |
| 888 | 1 |
| 889 | 1 |
| 890 | 2 |
| 891 | 2 |
| 893 | 2 |
| 894 | 1 |
| 895 | 4 |
| 897 | 1 |
| 899 | 4 |
| 900 | 1 |
| 901 | 1 |
| 902 | 2 |
| 903 | 1 |
| 906 | 2 |
| 907 | 3 |
| 908 | 1 |
| 909 | 1 |
| 910 | 1 |
| 911 | 2 |
| 912 | 2 |
| 913 | 2 |
| 914 | 1 |
| 916 | 1 |
| 917 | 3 |
| 918 | 1 |
| 919 | 2 |
| 920 | 3 |
| 921 | 1 |
| 926 | 1 |
| 927 | 1 |
| 928 | 2 |
| 930 | 2 |
| 931 | 1 |
| 932 | 3 |
| 934 | 1 |
| 935 | 3 |
| 937 | 1 |
| 939 | 2 |
| 940 | 2 |
| 941 | 1 |
| 942 | 1 |
| 947 | 1 |
| 948 | 3 |
| 950 | 1 |
| 953 | 2 |
| 955 | 3 |
| 957 | 1 |
| 958 | 1 |
| 960 | 2 |
| 962 | 1 |
| 963 | 1 |
| 964 | 1 |
| 965 | 3 |
| 968 | 1 |
| 970 | 2 |
| 971 | 1 |
| 972 | 2 |
| 974 | 1 |
| 977 | 4 |
| 983 | 3 |
| 984 | 1 |
| 986 | 1 |
| 988 | 1 |
| 992 | 2 |
| 999 | 2 |
| 1000 | 1 |
| 1002 | 1 |
| 1005 | 1 |
| 1006 | 1 |
| 1007 | 2 |
| 1009 | 1 |
| 1010 | 1 |
| 1011 | 1 |
| 1013 | 1 |
| 1014 | 1 |
| 1015 | 2 |
| 1018 | 2 |
| 1019 | 1 |
| 1020 | 3 |
| 1021 | 2 |
| 1022 | 2 |
| 1023 | 1 |
| 1024 | 3 |
| 1026 | 1 |
| 1028 | 3 |
| 1029 | 1 |
| 1030 | 1 |
| 1034 | 4 |
| 1035 | 1 |
| 1037 | 1 |
| 1041 | 2 |
| 1044 | 1 |
| 1045 | 2 |
| 1046 | 1 |
| 1050 | 1 |
| 1053 | 2 |
| 1058 | 1 |
| 1059 | 1 |
| 1060 | 2 |
| 1064 | 1 |
| 1067 | 1 |
| 1069 | 1 |
| 1074 | 1 |
| 1075 | 1 |
| 1076 | 1 |
| 1079 | 1 |
| 1080 | 1 |
| 1084 | 2 |
| 1085 | 1 |
| 1089 | 1 |
| 1091 | 1 |
| 1092 | 1 |
| 1094 | 1 |
| 1095 | 1 |
| 1096 | 1 |
| 1099 | 2 |
| 1100 | 1 |
| 1103 | 2 |
| 1105 | 3 |
| 1106 | 1 |
| 1107 | 1 |
| 1110 | 1 |
| 1111 | 1 |
| 1117 | 1 |
| 1118 | 1 |
| 1119 | 1 |
| 1122 | 1 |
| 1127 | 1 |
| 1132 | 1 |
| 1133 | 2 |
| 1134 | 1 |
| 1135 | 1 |
| 1138 | 1 |
| 1140 | 2 |
| 1141 | 3 |
| 1144 | 2 |
| 1145 | 1 |
| 1146 | 1 |
| 1147 | 2 |
| 1148 | 1 |
| 1151 | 1 |
| 1153 | 1 |
| 1158 | 2 |
| 1159 | 1 |
| 1160 | 1 |
| 1167 | 1 |
| 1168 | 2 |
| 1172 | 1 |
| 1175 | 1 |
| 1177 | 1 |
| 1180 | 1 |
| 1181 | 1 |
| 1182 | 1 |
| 1183 | 2 |
| 1184 | 2 |
| 1185 | 1 |
| 1190 | 1 |
| 1191 | 2 |
| 1197 | 1 |
| 1198 | 1 |
| 1200 | 2 |
| 1205 | 1 |
| 1210 | 1 |
| 1215 | 2 |
| 1223 | 2 |
| 1224 | 1 |
| 1225 | 1 |
| 1226 | 1 |
| 1227 | 2 |
| 1229 | 1 |
| 1238 | 1 |
| 1239 | 1 |
| 1240 | 1 |
| 1245 | 1 |
| 1247 | 1 |
| 1248 | 1 |
| 1249 | 1 |
| 1256 | 1 |
| 1257 | 1 |
| 1266 | 2 |
| 1267 | 1 |
| 1271 | 1 |
| 1278 | 1 |
| 1279 | 2 |
| 1281 | 1 |
| 1282 | 1 |
| 1284 | 1 |
| 1285 | 2 |
| 1286 | 1 |
| 1287 | 1 |
| 1293 | 1 |
| 1294 | 2 |
| 1295 | 1 |
| 1303 | 1 |
| 1307 | 1 |
| 1311 | 2 |
| 1314 | 1 |
| 1316 | 3 |
| 1318 | 1 |
| 1322 | 1 |
| 1323 | 1 |
| 1326 | 1 |
| 1327 | 1 |
| 1331 | 1 |
| 1338 | 1 |
| 1344 | 1 |
| 1347 | 1 |
| 1348 | 1 |
| 1349 | 1 |
| 1359 | 1 |
| 1361 | 1 |
| 1365 | 1 |
| 1368 | 1 |
| 1370 | 1 |
| 1375 | 1 |
| 1376 | 1 |
| 1378 | 2 |
| 1381 | 1 |
| 1388 | 1 |
| 1393 | 1 |
| 1398 | 1 |
| 1401 | 1 |
| 1411 | 1 |
| 1416 | 1 |
| 1418 | 1 |
| 1427 | 1 |
| 1434 | 1 |
| 1435 | 1 |
| 1437 | 1 |
| 1441 | 1 |
| 1444 | 1 |
| 1445 | 1 |
| 1447 | 1 |
| 1460 | 1 |
| 1471 | 2 |
| 1477 | 1 |
| 1487 | 2 |
| 1494 | 1 |
| 1498 | 1 |
| 1506 | 1 |
| 1508 | 1 |
| 1519 | 1 |
| 1524 | 1 |
| 1525 | 1 |
| 1528 | 1 |
| 1530 | 1 |
| 1539 | 1 |
| 1542 | 2 |
| 1546 | 2 |
| 1547 | 1 |
| 1549 | 1 |
| 1556 | 1 |
| 1557 | 1 |
| 1559 | 1 |
| 1572 | 1 |
| 1578 | 1 |
| 1579 | 1 |
| 1582 | 1 |
| 1587 | 1 |
| 1601 | 1 |
| 1611 | 1 |
| 1613 | 1 |
| 1618 | 1 |
| 1643 | 1 |
| 1671 | 1 |
| 1672 | 2 |
| 1701 | 1 |
| 1727 | 1 |
| 1752 | 1 |
| 1784 | 2 |
| 1798 | 1 |
| 1809 | 1 |
| 1821 | 1 |
| 1822 | 1 |
| 1834 | 1 |
| 1866 | 1 |
| 1889 | 1 |
| 1901 | 1 |
| 1906 | 1 |
| 1939 | 2 |
| 1963 | 1 |
| 1971 | 1 |
| 1976 | 1 |
| 1987 | 1 |
| 2034 | 1 |
| 2055 | 1 |
| 2061 | 1 |
| 2071 | 1 |
| 2119 | 1 |
| 2147 | 1 |
| 2168 | 1 |
| 2182 | 1 |
| 2199 | 1 |
| 2301 | 1 |
| 2392 | 1 |
| 2405 | 1 |
| 2448 | 1 |
| 2454 | 1 |
| 2509 | 1 |
| 2542 | 1 |
| 2571 | 1 |
| 2781 | 1 |
| 2873 | 1 |
| 2907 | 1 |
| 3007 | 1 |
| 3083 | 1 |
| 3233 | 1 |
| 3727 | 1 |
| 4089 | 1 |
| 4130 | 1 |
| 7004 | 1 |
| 7034 | 1 |
| 7185 | 1 |
| 7413 | 1 |
| 7764 | 1 |
| 8474 | 1 |
Contingency table of frequencies for number of tokens in the article content
# Summarizing the number of images in the article
filtered_channel %>%
summarise(Minimum = min(num_imgs),
Q1 = quantile(num_imgs, prob = 0.25),
Average = mean(num_imgs),
Median = median(num_imgs),
Q3 = quantile(num_imgs, prob = 0.75),
Maximum = max(num_imgs)) %>%
kable(caption = "Numerical summary of number of images in an article")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 1 | 4.904717 | 1 | 8 | 111 |
Numerical summary of number of images in an article
# Summarizing the number of videos in the article
filtered_channel %>%
summarise(Minimum = min(num_videos),
Q1 = quantile(num_videos, prob = 0.25),
Average = mean(num_videos),
Median = median(num_videos),
Q3 = quantile(num_videos, prob = 0.75),
Maximum = max(num_videos)) %>%
kable(caption = "Numerical summary of number of videos in an article")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 0 | 0.4749881 | 0 | 0 | 50 |
Numerical summary of number of videos in an article
# Summarizing the number of positive word rate
filtered_channel %>%
summarise(Minimum = min(rate_positive_words),
Q1 = quantile(rate_positive_words, prob = 0.25),
Average = mean(rate_positive_words),
Median = median(rate_positive_words),
Q3 = quantile(rate_positive_words, prob = 0.75),
Maximum = max(rate_positive_words)) %>%
kable(caption = "Numerical Summary of the rate of positive words in an article")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 0.6624941 | 0.7226337 | 0.7377049 | 0.8125 | 1 |
Numerical Summary of the rate of positive words in an article
# Summarizing the number of negative word rate
filtered_channel %>%
summarise(Minimum = min(rate_negative_words),
Q1 = quantile(rate_negative_words, prob = 0.25),
Average = mean(rate_negative_words),
Median = median(rate_negative_words),
Q3 = quantile(rate_negative_words, prob = 0.75),
Maximum = max(rate_negative_words)) %>%
kable(caption = "Numerical Summary of the rate of negative words in an article")
| Minimum | Q1 | Average | Median | Q3 | Maximum |
|---|---|---|---|---|---|
| 0 | 0.1836735 | 0.2668851 | 0.2580645 | 0.3333333 | 1 |
Numerical Summary of the rate of negative words in an article
The graphical summaries more dramatically show the trends in the data, including skewness and outliers. The boxplots below show a visual representation of the 5 Number summaries for Shares, split up by weekday, and shares split up by text sentiment polarity. Boxplots make it even easier to look out for outliers (look for the dots separated from the main boxplot). Next, we can examine several scatterplots. Scatterplots allow us to look at one numerical variable vs another to see if there is any correlation between them. Look out for any plots that have most of the points on a diagonal line! There are four scatterplots below, investigating shares vs Number of words in the content, Number of words in the title, rate of positive words, and rate of negative words. Finally, a histogram can show the overall distribution of a numerical variable, including skewness. The histogram below sows the distribution of the shares variable. Look for a left or right tail to signify skewness, and look out for multiple peaks to signify a multi-modal variable.
# Boxplot of Shares for Each Weekday, colored gray with classic theme, added labels and title
ggplot(filtered_channel, aes(x = weekday, y = shares)) +
geom_boxplot(fill = "grey") +
labs(x = "Weekday", title = "Boxplot of Shares for Each Weekday", y = "Shares") +
theme_classic()
# Scatterplot of Number of words in the content vs Shares, colored gray with classic theme, added labels and title
ggplot(filtered_channel, aes(x = n_tokens_content, y = shares)) +
geom_point(color = "grey") +
labs(x = "Number of words in the content", y = "Shares",
title = "Scatterplot of Number of words in the content vs Shares") +
theme_classic()
# Scatterplot of Number of words in the title vs Shares, colored gray with classic theme, added labels and title
ggplot(filtered_channel, aes(x = n_tokens_title, y = shares)) +
geom_point(color = "grey") +
labs(x = "Number of words in the title", y = "Shares",
title = "Scatterplot of Number of words in the title vs Shares") +
theme_classic()
ggplot(filtered_channel, aes(x=shares)) +
geom_histogram(color="grey", binwidth = 2000) +
labs(x = "Shares",
title = "Histogram of number of shares") +
theme_classic()
ggplot(filtered_channel, aes(x=rate_positive_words, y=shares)) +
geom_point(color="grey") +
labs(x = "rate of positive words in an article", y = "Shares",
title = "Scatterplot of rate of positive words in an article vs shares") +
theme_classic()
ggplot(filtered_channel, aes(x=rate_negative_words, y=shares)) +
geom_point(color="grey") +
labs(x = "rate of negative words in an article", y = "Shares",
title = "Scatterplot of rate of negative words in an article vs shares") +
theme_classic()
ggplot(filtered_channel, aes(x=global_sentiment_polarity, y=shares)) +
geom_point(color="grey") +
labs(x = "global sentiment polarity in an article", y = "Shares",
title = "Scatterplot of global sentiment polarity in an article vs shares") +
theme_classic()
# drop the weekday variable created for EDA (will get in the way for our models if we don't drop it)
filtered_channel <- subset(filtered_channel, select = -c(weekday))
Modeling
Splitting the Data
First, let’s split up the data into a testing set and a training set using the proportions: 70% training and 30% testing.
set.seed(9876)
# Split the data into a training and test set (70/30 split)
# indices
train <- sample(1:nrow(filtered_channel), size = nrow(filtered_channel)*.70)
test <- setdiff(1:nrow(filtered_channel), train)
# training and testing subsets
Training <- filtered_channel[train, ]
Testing <- filtered_channel[test, ]
Linear Models
Linear regression models allow us to look at relationships between one
response variable and several explanatory variables. A model can also
include interaction terms and even higher order terms. The general form
for a linear model is
,
where each
represents a predictor variable and the “…” can include more predictors,
interactions and/or higher order terms. Since our goal is to predict
shares, we will be using these models to predict of a subset of the data
created for training, and then we will later test the models on the
other subsetted data set aside for testing.
Linear Model #1: - Jordan
# linear model on training dataset with 5-fold cv
fit1 <- train(shares ~ . , data = Training, method = "lm",
preProcess = c("center", "scale"),
trControl = trainControl(method = "cv", number = 5))
Linear Model #2: - Jonathan
lm_fit <- train(
shares ~ .^2,
data=Training,
method="lm",
preProcess = c("center", "scale"),
trControl = trainControl(method = "cv", number = 5)
)
Random Forest - Jordan
Random Forest is a tree based method for fitting predictive models, that
averages across all trees. One may choose to use a tree based method due
to their prediction accuracy, the fact that predictors do not need to be
scaled, no statistical assumptions, and a built-in variable selection
process. Random forest, in particular, randomly selects a subset of
predictors. This corrects the bagging issue where every bootstrap
contains a strong predictor for the first split.
# fandom forest model on training dataset with 5-fold cv
ranfor <- train(shares ~ ., data = Training, method = "rf", preProcess = c("center", "scale"),
trControl = trainControl(method = "cv", number = 5),
tuneGrid = expand.grid(mtry = c(1:round(ncol(Training)/3))))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
ranfor
## Random Forest
##
## 1469 samples
## 58 predictor
##
## Pre-processing: centered (58), scaled (58)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1176, 1175, 1175, 1174, 1176
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 1 8647.401 0.013806063 3339.815
## 2 8670.942 0.016487776 3416.647
## 3 8746.959 0.012640866 3453.693
## 4 8793.486 0.010810707 3490.870
## 5 8820.633 0.010464841 3515.555
## 6 8890.127 0.010299153 3534.856
## 7 8954.947 0.006461325 3577.440
## 8 8950.874 0.009611146 3564.044
## 9 9050.297 0.007999605 3592.140
## 10 9062.713 0.008020117 3603.272
## 11 9140.680 0.008393644 3629.264
## 12 9135.058 0.006962249 3631.516
## 13 9208.666 0.006922996 3654.880
## 14 9254.574 0.006711465 3659.443
## 15 9318.889 0.007400555 3685.630
## 16 9326.816 0.006721992 3683.519
## 17 9394.413 0.006586585 3708.637
## 18 9412.480 0.005168434 3712.670
## 19 9464.970 0.006418984 3694.991
## 20 9507.476 0.005502456 3729.256
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 1.
Boosted Tree - Jonathan
Boosted Trees is an ensemble model similar to bagging where it builds multiple tree models. The previous tree built is used to build a new tree by taking into account the errors of the previous tree.
tune_grid <- expand.grid(
n.trees = c(5, 10, 50, 100),
interaction.depth = c(1,2,3, 4),
shrinkage = 0.1,
n.minobsinnode = 10
)
bt_fit <- train(
shares ~ .,
data=Training,
method="gbm",
preProcess = c("center", "scale"),
tuneGrid = tune_grid,
trControl = trainControl(method = "cv", number = 5)
)
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 105186414.4306 nan 0.1000 -8190.9660
## 2 104635533.6093 nan 0.1000 -119296.8594
## 3 104211064.0922 nan 0.1000 -204272.3253
## 4 103885509.2736 nan 0.1000 -376674.2633
## 5 103703849.9430 nan 0.1000 -177405.1232
## 6 103626315.2741 nan 0.1000 -267489.9525
## 7 103007318.3224 nan 0.1000 -16195.9567
## 8 102922125.1071 nan 0.1000 -270800.8586
## 9 102573079.7617 nan 0.1000 -130064.6672
## 10 102157664.0377 nan 0.1000 -82946.5969
## 20 100203925.4856 nan 0.1000 -168592.0259
## 40 98750209.5697 nan 0.1000 -546520.8452
## 60 97314754.5000 nan 0.1000 65730.8357
## 80 95330791.3015 nan 0.1000 -1018941.3423
## 100 94586741.8314 nan 0.1000 -262842.8154
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 105340130.3730 nan 0.1000 53235.3244
## 2 104485193.8217 nan 0.1000 -124474.1481
## 3 103627616.5294 nan 0.1000 -133949.3479
## 4 102793201.2806 nan 0.1000 -305504.6198
## 5 102254619.1289 nan 0.1000 -13277.7068
## 6 101848289.1047 nan 0.1000 -113815.1896
## 7 101476440.9914 nan 0.1000 -94793.8621
## 8 101087293.8282 nan 0.1000 -431026.8615
## 9 100945281.5334 nan 0.1000 -373295.1814
## 10 100805246.9067 nan 0.1000 -206026.9420
## 20 97163395.4192 nan 0.1000 -511558.1065
## 40 91233727.3673 nan 0.1000 -624421.5774
## 60 86568700.8920 nan 0.1000 -321063.1790
## 80 83918176.6155 nan 0.1000 -441631.8540
## 100 79928494.4332 nan 0.1000 -261104.3991
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 104398436.2349 nan 0.1000 64940.8695
## 2 103935092.4514 nan 0.1000 69670.5743
## 3 102758799.8749 nan 0.1000 -160706.4574
## 4 101819209.8987 nan 0.1000 -433361.2082
## 5 101378461.4125 nan 0.1000 -278735.8525
## 6 100967996.9766 nan 0.1000 -735710.1032
## 7 99855839.9426 nan 0.1000 162389.2990
## 8 99718987.1115 nan 0.1000 -236724.4173
## 9 99350210.5370 nan 0.1000 -80729.5711
## 10 98306067.1087 nan 0.1000 -177829.4290
## 20 94772646.6563 nan 0.1000 -258699.0329
## 40 87117817.9660 nan 0.1000 -927163.4356
## 60 82472883.0719 nan 0.1000 -403617.6339
## 80 77902477.8875 nan 0.1000 -113608.7509
## 100 74058089.0248 nan 0.1000 -634199.0223
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 104579941.9926 nan 0.1000 31785.1807
## 2 103289608.1701 nan 0.1000 -65357.7591
## 3 101911230.2214 nan 0.1000 -426587.8466
## 4 101329251.5305 nan 0.1000 -109293.9873
## 5 100671700.7279 nan 0.1000 -187070.6456
## 6 99897256.8663 nan 0.1000 -158682.7518
## 7 99505374.1655 nan 0.1000 -874378.4669
## 8 99186305.5806 nan 0.1000 -649468.3243
## 9 98966332.5532 nan 0.1000 -174945.4825
## 10 98493572.6948 nan 0.1000 -612405.3488
## 20 93867803.4541 nan 0.1000 -515074.7668
## 40 87110660.0682 nan 0.1000 -996738.2898
## 60 78062657.8536 nan 0.1000 -667097.3612
## 80 71983700.3763 nan 0.1000 -510740.5305
## 100 69124305.1541 nan 0.1000 -459286.9713
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 37669769.9294 nan 0.1000 -41139.4849
## 2 37191170.4604 nan 0.1000 29571.1303
## 3 37094748.8507 nan 0.1000 1997.0348
## 4 36907467.5069 nan 0.1000 76062.0774
## 5 36534542.4517 nan 0.1000 -60508.3187
## 6 36424199.7869 nan 0.1000 -53829.3578
## 7 36306700.5758 nan 0.1000 20151.9112
## 8 35899725.1787 nan 0.1000 183047.8316
## 9 35835652.9430 nan 0.1000 -57478.4418
## 10 35715081.0503 nan 0.1000 38852.0699
## 20 34097426.8243 nan 0.1000 25861.0666
## 40 33015256.8642 nan 0.1000 -17857.7902
## 60 32396059.7287 nan 0.1000 -140506.4840
## 80 32074254.7842 nan 0.1000 -126577.7930
## 100 31643404.0182 nan 0.1000 -36080.4857
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 37830802.9730 nan 0.1000 -28209.0801
## 2 37505202.7995 nan 0.1000 246391.9089
## 3 37346541.6617 nan 0.1000 -6771.5828
## 4 36971537.5699 nan 0.1000 157792.3090
## 5 36474333.0465 nan 0.1000 42508.6152
## 6 36203271.3755 nan 0.1000 16862.2638
## 7 35849822.3426 nan 0.1000 51209.3508
## 8 35494610.2977 nan 0.1000 -72492.1116
## 9 35312086.2465 nan 0.1000 -9227.3600
## 10 35095385.8074 nan 0.1000 -78146.7432
## 20 33057060.2985 nan 0.1000 -186947.4600
## 40 30686944.6303 nan 0.1000 -29295.4968
## 60 28697475.2974 nan 0.1000 -78603.8019
## 80 27567245.7904 nan 0.1000 -109727.1466
## 100 26350082.5531 nan 0.1000 -17334.3729
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 37488430.9317 nan 0.1000 287343.1803
## 2 37176742.3470 nan 0.1000 34434.4850
## 3 36852825.2639 nan 0.1000 49692.1310
## 4 36270138.6879 nan 0.1000 433883.9911
## 5 35679707.3062 nan 0.1000 -146214.4092
## 6 35208681.3663 nan 0.1000 66930.7013
## 7 34769790.5246 nan 0.1000 22808.4487
## 8 34483382.3733 nan 0.1000 -129494.3192
## 9 34253440.8443 nan 0.1000 137599.2808
## 10 33918885.4085 nan 0.1000 -92141.3637
## 20 31832613.4848 nan 0.1000 7097.7731
## 40 28979390.2770 nan 0.1000 -112631.5965
## 60 26123939.3114 nan 0.1000 -145591.0925
## 80 24076495.4634 nan 0.1000 -248799.1608
## 100 22111382.0918 nan 0.1000 -130404.1874
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 37406202.6033 nan 0.1000 -80415.7262
## 2 37034485.7614 nan 0.1000 205448.6962
## 3 36460853.7225 nan 0.1000 186799.2269
## 4 35805911.7725 nan 0.1000 66964.4793
## 5 35227595.2036 nan 0.1000 50551.7148
## 6 34907863.4907 nan 0.1000 -44655.1112
## 7 34665245.0946 nan 0.1000 99611.5146
## 8 34377972.2412 nan 0.1000 49792.9930
## 9 33885203.1464 nan 0.1000 -69594.7312
## 10 33505284.3781 nan 0.1000 -173511.2405
## 20 30609726.3927 nan 0.1000 -46757.1581
## 40 26933919.9015 nan 0.1000 -250140.0771
## 60 24230725.1963 nan 0.1000 -47983.2220
## 80 21854086.1581 nan 0.1000 -92434.5889
## 100 19894419.4192 nan 0.1000 -15845.6149
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 103071525.6410 nan 0.1000 -120243.6853
## 2 102728544.1322 nan 0.1000 -175821.1860
## 3 102374379.8622 nan 0.1000 -20887.8079
## 4 102214308.8572 nan 0.1000 121912.7115
## 5 102174370.3218 nan 0.1000 -79455.7557
## 6 101993144.1361 nan 0.1000 -131610.8986
## 7 101362752.2058 nan 0.1000 -131108.4138
## 8 101037869.9590 nan 0.1000 -31671.7046
## 9 100707942.0132 nan 0.1000 -313281.8899
## 10 100671329.8075 nan 0.1000 -139015.3046
## 20 99394719.1552 nan 0.1000 -302311.9989
## 40 97313970.3357 nan 0.1000 -182962.1236
## 60 95791179.9495 nan 0.1000 -526239.0257
## 80 94506300.2278 nan 0.1000 -393202.6593
## 100 92603575.1175 nan 0.1000 8342.9428
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 103198464.6020 nan 0.1000 -44896.8021
## 2 102744791.0401 nan 0.1000 -91670.4848
## 3 101864383.1589 nan 0.1000 -133153.5701
## 4 100845560.7950 nan 0.1000 -292152.7133
## 5 100498069.4302 nan 0.1000 -80086.5379
## 6 100095297.5131 nan 0.1000 -66595.7967
## 7 99777590.2803 nan 0.1000 -308513.8815
## 8 99479903.1580 nan 0.1000 -205444.4466
## 9 98966904.9331 nan 0.1000 -386725.2999
## 10 97948544.2103 nan 0.1000 -552764.9787
## 20 94744574.0694 nan 0.1000 -464440.5594
## 40 89999486.6643 nan 0.1000 -24286.3604
## 60 86208057.9388 nan 0.1000 -195334.6651
## 80 83269723.2689 nan 0.1000 30283.0117
## 100 80474278.9291 nan 0.1000 -478491.6057
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 101965685.4884 nan 0.1000 -140056.9150
## 2 101410207.0937 nan 0.1000 -73276.1437
## 3 100390371.4320 nan 0.1000 -268780.6081
## 4 99886866.1687 nan 0.1000 32521.1897
## 5 99043422.5329 nan 0.1000 -386666.4730
## 6 98467940.9017 nan 0.1000 -137203.8956
## 7 98120429.8231 nan 0.1000 -592679.5702
## 8 97564605.4316 nan 0.1000 -231804.5849
## 9 96532449.4717 nan 0.1000 -226391.5010
## 10 96270453.7863 nan 0.1000 -627093.1032
## 20 91632866.8200 nan 0.1000 -448069.6605
## 40 82418868.5774 nan 0.1000 15203.7707
## 60 77280757.7329 nan 0.1000 -166045.5178
## 80 74278309.1533 nan 0.1000 -636635.7718
## 100 69996649.7629 nan 0.1000 -321138.0534
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 102661554.1582 nan 0.1000 -305054.1063
## 2 101838268.5188 nan 0.1000 -12562.4477
## 3 100624737.8995 nan 0.1000 46124.4480
## 4 100381139.6218 nan 0.1000 -313793.7931
## 5 99814347.9756 nan 0.1000 -256602.5253
## 6 99332104.3829 nan 0.1000 38143.2162
## 7 98728313.5976 nan 0.1000 -137022.0994
## 8 98070150.7971 nan 0.1000 -500336.2537
## 9 97649326.9801 nan 0.1000 -320449.6776
## 10 97221411.5799 nan 0.1000 -603136.9008
## 20 94179237.1853 nan 0.1000 -901949.9488
## 40 86852262.0194 nan 0.1000 -811800.5931
## 60 81332691.0226 nan 0.1000 -474019.3399
## 80 76274552.0633 nan 0.1000 -570085.7966
## 100 72371654.5670 nan 0.1000 -330039.0923
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 91751282.8415 nan 0.1000 -62755.7044
## 2 91381557.2867 nan 0.1000 -14313.0563
## 3 91178641.1177 nan 0.1000 -64640.9700
## 4 90541849.4373 nan 0.1000 -5364.6618
## 5 90420825.8934 nan 0.1000 -24254.3014
## 6 90104801.5857 nan 0.1000 -61134.6671
## 7 89968414.6215 nan 0.1000 -71861.6740
## 8 89729218.8785 nan 0.1000 -268765.5259
## 9 89188768.2165 nan 0.1000 -228247.9160
## 10 88886086.9884 nan 0.1000 -226078.6434
## 20 87910094.2109 nan 0.1000 -265477.8536
## 40 86402946.4108 nan 0.1000 -394945.4080
## 60 85445439.4697 nan 0.1000 -276914.7774
## 80 84651705.3543 nan 0.1000 -340093.9938
## 100 83516029.4452 nan 0.1000 -552252.9176
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 90831813.4233 nan 0.1000 -131760.1970
## 2 90717128.7155 nan 0.1000 4069.3320
## 3 89779999.8172 nan 0.1000 -309695.9135
## 4 89189084.3790 nan 0.1000 -307699.5097
## 5 89009331.7819 nan 0.1000 -172018.1064
## 6 88674052.0012 nan 0.1000 -192203.3741
## 7 88723207.2231 nan 0.1000 -570073.3855
## 8 88533114.6802 nan 0.1000 18952.4328
## 9 88535076.2451 nan 0.1000 -325341.4256
## 10 88234723.0531 nan 0.1000 -208740.3562
## 20 86186529.3913 nan 0.1000 -154244.4332
## 40 83688490.4550 nan 0.1000 -770536.1796
## 60 79163220.3880 nan 0.1000 -564848.4279
## 80 76767299.2783 nan 0.1000 -115702.2524
## 100 74615367.8139 nan 0.1000 -139295.2548
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 91985268.6189 nan 0.1000 -148323.7785
## 2 90568587.3772 nan 0.1000 -151338.1988
## 3 90058814.1934 nan 0.1000 -69469.2350
## 4 89887826.0475 nan 0.1000 -67049.7609
## 5 89445270.4717 nan 0.1000 -55376.5091
## 6 88702430.8037 nan 0.1000 -212877.8207
## 7 88275239.3778 nan 0.1000 -523764.2191
## 8 87741512.0668 nan 0.1000 -587828.3332
## 9 87572671.4504 nan 0.1000 -10532.7897
## 10 87561440.3497 nan 0.1000 -383927.8826
## 20 85865755.0607 nan 0.1000 -723618.5025
## 40 83259363.3971 nan 0.1000 -711969.4976
## 60 78988566.0543 nan 0.1000 -909520.4924
## 80 75230894.6190 nan 0.1000 -762681.5196
## 100 71774446.8260 nan 0.1000 -340032.0213
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 91550029.2670 nan 0.1000 -8033.8977
## 2 90853152.0484 nan 0.1000 -11138.6566
## 3 90618996.1759 nan 0.1000 -88870.2690
## 4 89456376.7951 nan 0.1000 -327527.4428
## 5 89302835.6352 nan 0.1000 -66192.8558
## 6 88577921.1840 nan 0.1000 -331079.7156
## 7 87418832.8215 nan 0.1000 -270600.3131
## 8 86970903.5005 nan 0.1000 -158648.8559
## 9 86842871.2072 nan 0.1000 -769084.8205
## 10 86646991.3001 nan 0.1000 -255950.3686
## 20 85039652.9717 nan 0.1000 -815788.3424
## 40 78188519.4178 nan 0.1000 -1038188.0156
## 60 73926576.9255 nan 0.1000 -552365.6360
## 80 71588228.2849 nan 0.1000 -29258.9359
## 100 68551900.4689 nan 0.1000 -272014.8627
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 96711704.3748 nan 0.1000 195613.5442
## 2 96196893.5270 nan 0.1000 -66207.6519
## 3 95653181.1448 nan 0.1000 5892.1171
## 4 95289490.3662 nan 0.1000 -62783.5708
## 5 94984094.6632 nan 0.1000 -61706.9291
## 6 94745914.7633 nan 0.1000 -40311.7138
## 7 94546951.7829 nan 0.1000 -307054.7396
## 8 94228640.8934 nan 0.1000 143124.6963
## 9 94079936.3402 nan 0.1000 -88224.3549
## 10 93923531.0946 nan 0.1000 -767730.8557
## 20 92778891.7595 nan 0.1000 -461202.3512
## 40 91337469.4669 nan 0.1000 -372918.8873
## 60 89796812.8914 nan 0.1000 -434719.5770
## 80 88122090.5775 nan 0.1000 -436299.8784
## 100 87537241.3655 nan 0.1000 -627923.6507
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 96282563.0408 nan 0.1000 -37669.7831
## 2 95703573.9601 nan 0.1000 -163635.9166
## 3 95046640.1721 nan 0.1000 -253413.3110
## 4 93488089.8238 nan 0.1000 -466567.5492
## 5 92862906.1518 nan 0.1000 -192794.2785
## 6 92461606.8963 nan 0.1000 -62471.4315
## 7 92120114.5441 nan 0.1000 -476738.8750
## 8 91911186.1512 nan 0.1000 -496220.9269
## 9 91854431.3037 nan 0.1000 -312834.1544
## 10 91654807.6477 nan 0.1000 -286201.5479
## 20 87332422.4312 nan 0.1000 -236268.6436
## 40 83248465.4155 nan 0.1000 -237759.8802
## 60 78910547.2826 nan 0.1000 -159859.8899
## 80 76745991.1648 nan 0.1000 -547596.2134
## 100 74953848.2511 nan 0.1000 -368588.1956
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 95947571.1501 nan 0.1000 18522.9311
## 2 95029993.9922 nan 0.1000 -249907.3378
## 3 94494284.4259 nan 0.1000 -314890.5015
## 4 94025143.1136 nan 0.1000 -412418.8237
## 5 93778188.6797 nan 0.1000 -437106.7982
## 6 92728578.0881 nan 0.1000 -197461.1801
## 7 92559233.8687 nan 0.1000 -354815.9042
## 8 92378871.2837 nan 0.1000 -297440.4800
## 9 92113698.5418 nan 0.1000 -387359.6024
## 10 91931626.3464 nan 0.1000 -610376.1028
## 20 87664044.8855 nan 0.1000 278434.0442
## 40 81731161.2983 nan 0.1000 -597280.3847
## 60 76586004.5095 nan 0.1000 -241732.6696
## 80 73127721.5246 nan 0.1000 -575590.9972
## 100 69088411.2538 nan 0.1000 -249837.0606
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 96198688.4175 nan 0.1000 -338571.7932
## 2 95836475.7136 nan 0.1000 101554.6784
## 3 94785945.1945 nan 0.1000 -355946.1306
## 4 93488524.7324 nan 0.1000 -438747.1385
## 5 92832578.2357 nan 0.1000 -173097.6934
## 6 92393840.2959 nan 0.1000 -209063.0516
## 7 92435728.2033 nan 0.1000 -817789.2433
## 8 92147654.7496 nan 0.1000 -518450.9081
## 9 92013538.7011 nan 0.1000 -355000.6703
## 10 91616373.9813 nan 0.1000 -108446.3450
## 20 87269506.0745 nan 0.1000 -599544.7571
## 40 81676440.2376 nan 0.1000 -552475.6655
## 60 73964555.4514 nan 0.1000 -451791.9225
## 80 69774904.8951 nan 0.1000 -409836.6920
## 100 67010492.7532 nan 0.1000 -587178.0453
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut = 10, : These variables have zero variances:
## data_channel_is_lifestyle, data_channel_is_entertainment, data_channel_is_bus, data_channel_is_socmed, data_channel_is_tech,
## data_channel_is_world
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 12: data_channel_is_lifestyle has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 13: data_channel_is_entertainment has
## no variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 14: data_channel_is_bus has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 15: data_channel_is_socmed has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 16: data_channel_is_tech has no
## variation.
## Warning in (function (x, y, offset = NULL, misc = NULL, distribution = "bernoulli", : variable 17: data_channel_is_world has no
## variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 86698278.8732 nan 0.1000 161152.8424
## 2 86164738.6774 nan 0.1000 2036.7354
## 3 85696456.1746 nan 0.1000 -176233.3639
## 4 85123794.6305 nan 0.1000 -48061.2017
## 5 84597291.6373 nan 0.1000 -351254.3042
bt_fit
## Stochastic Gradient Boosting
##
## 1469 samples
## 58 predictor
##
## Pre-processing: centered (58), scaled (58)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1174, 1175, 1175, 1176, 1176
## Resampling results across tuning parameters:
##
## interaction.depth n.trees RMSE Rsquared MAE
## 1 5 8165.421 0.005096493 3371.056
## 1 10 8196.132 0.007551899 3362.414
## 1 50 8381.475 0.008373412 3407.874
## 1 100 8440.339 0.008902974 3452.295
## 2 5 8148.357 0.008164890 3399.161
## 2 10 8201.933 0.007609891 3389.915
## 2 50 8405.176 0.012781046 3439.845
## 2 100 8562.265 0.010497550 3536.516
## 3 5 8165.285 0.008663343 3377.172
## 3 10 8199.797 0.011038976 3378.280
## 3 50 8383.000 0.014013506 3465.067
## 3 100 8534.508 0.014561547 3554.871
## 4 5 8149.778 0.004361652 3364.159
## 4 10 8190.586 0.010058920 3378.623
## 4 50 8460.894 0.017205681 3559.072
## 4 100 8519.830 0.015968110 3627.882
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
## Tuning parameter 'n.minobsinnode' was held constant at a value
## of 10
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 5, interaction.depth = 2, shrinkage = 0.1 and n.minobsinnode = 10.
Comparison - Jordan
Finally, let’s compare our four models: 2 linear models, 1 random forest model, and 1 boosted tree model.
# random forest prediction on testing model and its performance
predRF <- predict(ranfor, newdata = Testing)
RF <- postResample(predRF, Testing$shares)
# linear model 1 prediction on testing model and its performance
predlm1 <- predict(fit1, newdata = Testing)
LM <- postResample(predlm1, Testing$shares)
# linear model 2 prediction on testing model and its performance
predlm2 <- predict(lm_fit, newdata = Testing)
LM2 <- postResample(predlm2, Testing$shares)
# boosted tree prediction on testing model and its performance
predbt <- predict(bt_fit, newdata = Testing)
BT <- postResample(predbt, Testing$shares)
# combine each of the performance stats for the models and add a column with the model names
dat <- data.frame(rbind(t(data.frame(LM)), t(data.frame(RF)), t(data.frame(LM2)), t(data.frame(BT))))
df <- as_tibble(rownames_to_column(dat, "models"))
# find the model with the lowesr RMSE
best <- df %>% filter(RMSE == min(RMSE)) %>% select(models)
# print "The Best fitting model according to RMSE is [insert model name for lowest RMSE here]"
paste("The Best fitting model according to RMSE is", best$models, sep = " ")
## [1] "The Best fitting model according to RMSE is RF"