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_keywords” - the number of keywords within the article
- “num_images” - the number of images within the article
- “num_videos” - the number of videos within the article
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_token...
##
## 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
head(news)
# 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_socmed"
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 | 332 |
| Monday | 337 |
| Saturday | 180 |
| Sunday | 137 |
| Thursday | 463 |
| Tuesday | 458 |
| Wednesday | 416 |
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 |
|---|---|---|---|---|---|
| 5 | 1400 | 3629.383 | 2100 | 3800 | 122800 |
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 | 253 | 609.6281 | 434 | 761.5 | 4878 |
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 | 274.5 | 633.4116 | 461 | 845 | 4451 |
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 | 12 |
| 31 | 1 |
| 32 | 1 |
| 34 | 2 |
| 35 | 1 |
| 37 | 1 |
| 39 | 1 |
| 40 | 1 |
| 42 | 1 |
| 44 | 1 |
| 45 | 2 |
| 46 | 1 |
| 47 | 4 |
| 48 | 1 |
| 49 | 2 |
| 53 | 2 |
| 56 | 3 |
| 57 | 2 |
| 58 | 1 |
| 59 | 2 |
| 62 | 2 |
| 66 | 2 |
| 68 | 1 |
| 69 | 3 |
| 72 | 1 |
| 73 | 1 |
| 77 | 1 |
| 79 | 1 |
| 80 | 1 |
| 81 | 3 |
| 82 | 1 |
| 85 | 1 |
| 88 | 1 |
| 89 | 3 |
| 90 | 3 |
| 91 | 2 |
| 94 | 2 |
| 95 | 1 |
| 96 | 2 |
| 97 | 1 |
| 98 | 8 |
| 99 | 4 |
| 100 | 4 |
| 101 | 3 |
| 102 | 3 |
| 103 | 2 |
| 104 | 2 |
| 105 | 1 |
| 106 | 2 |
| 107 | 3 |
| 108 | 1 |
| 109 | 1 |
| 110 | 1 |
| 111 | 1 |
| 112 | 3 |
| 113 | 1 |
| 115 | 2 |
| 116 | 2 |
| 117 | 4 |
| 121 | 3 |
| 123 | 4 |
| 124 | 1 |
| 125 | 4 |
| 127 | 2 |
| 128 | 3 |
| 129 | 2 |
| 131 | 1 |
| 132 | 3 |
| 133 | 1 |
| 134 | 1 |
| 135 | 3 |
| 136 | 3 |
| 137 | 2 |
| 138 | 2 |
| 139 | 3 |
| 140 | 4 |
| 141 | 2 |
| 142 | 1 |
| 143 | 3 |
| 144 | 4 |
| 145 | 6 |
| 146 | 4 |
| 147 | 2 |
| 148 | 3 |
| 150 | 7 |
| 151 | 7 |
| 152 | 3 |
| 153 | 2 |
| 154 | 3 |
| 155 | 5 |
| 156 | 5 |
| 157 | 2 |
| 158 | 3 |
| 159 | 3 |
| 160 | 4 |
| 161 | 5 |
| 162 | 3 |
| 163 | 7 |
| 164 | 1 |
| 165 | 3 |
| 166 | 2 |
| 167 | 1 |
| 168 | 5 |
| 169 | 3 |
| 170 | 3 |
| 171 | 3 |
| 172 | 5 |
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| 1244 | 1 |
| 1249 | 1 |
| 1250 | 1 |
| 1252 | 2 |
| 1253 | 1 |
| 1254 | 1 |
| 1256 | 1 |
| 1259 | 1 |
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| 1399 | 2 |
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| 1467 | 2 |
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| 1577 | 2 |
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| 1587 | 1 |
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| 1752 | 2 |
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| 1765 | 1 |
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| 1774 | 2 |
| 1777 | 1 |
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| 1804 | 1 |
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| 1840 | 1 |
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| 1891 | 1 |
| 1895 | 1 |
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| 1897 | 1 |
| 1919 | 1 |
| 1936 | 1 |
| 1951 | 1 |
| 1955 | 1 |
| 1959 | 1 |
| 1965 | 1 |
| 1980 | 1 |
| 2017 | 1 |
| 2049 | 1 |
| 2061 | 1 |
| 2075 | 1 |
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| 2143 | 1 |
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| 2208 | 1 |
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| 2229 | 1 |
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| 2235 | 2 |
| 2236 | 2 |
| 2240 | 1 |
| 2241 | 1 |
| 2249 | 1 |
| 2251 | 1 |
| 2267 | 1 |
| 2270 | 1 |
| 2281 | 1 |
| 2284 | 1 |
| 2293 | 1 |
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| 2300 | 1 |
| 2301 | 1 |
| 2305 | 1 |
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| 2307 | 1 |
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| 2318 | 1 |
| 2347 | 2 |
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| 2353 | 1 |
| 2363 | 1 |
| 2371 | 1 |
| 2388 | 1 |
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| 2545 | 1 |
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| 2563 | 1 |
| 2578 | 1 |
| 2588 | 1 |
| 2628 | 1 |
| 2634 | 1 |
| 2649 | 1 |
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| 2693 | 1 |
| 2695 | 1 |
| 2794 | 1 |
| 2889 | 1 |
| 3040 | 1 |
| 3288 | 1 |
| 3460 | 1 |
| 3506 | 1 |
| 3730 | 1 |
| 3735 | 1 |
| 4451 | 1 |
| 4878 | 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 | 4.290142 | 1 | 3 | 62 |
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 | 1.11752 | 0 | 1 | 73 |
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 | 0.6666667 | 0.7441094 | 0.75 | 0.8333333 | 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 | 0.1666667 | 0.2502944 | 0.2469136 | 0.3288997 | 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 methood
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
##
## 1626 samples
## 58 predictor
##
## Pre-processing: centered (58), scaled (58)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1302, 1301, 1301, 1299, 1301
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 1 5294.448 0.07732259 2576.669
## 2 5221.714 0.08212624 2547.928
## 3 5222.118 0.07907696 2558.006
## 4 5246.706 0.07012279 2576.885
## 5 5266.116 0.06464412 2593.834
## 6 5271.933 0.06718766 2593.405
## 7 5299.716 0.06013859 2613.661
## 8 5316.491 0.06045967 2618.119
## 9 5307.498 0.06396155 2609.483
## 10 5329.267 0.05737103 2627.145
## 11 5369.616 0.05457738 2635.056
## 12 5354.799 0.05528180 2640.184
## 13 5387.632 0.05269245 2643.268
## 14 5387.162 0.05356608 2640.221
## 15 5439.345 0.05021842 2654.333
## 16 5479.860 0.04398097 2665.446
## 17 5428.564 0.04612384 2656.974
## 18 5459.247 0.04692171 2667.258
## 19 5497.034 0.04289867 2677.505
## 20 5521.875 0.04114838 2679.858
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 2.
Boosted Tree - Jonathan
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"),
trControl = trainControl(method = "cv", number = 5)
)
## Warning in preProcess.default(method = c("center", "scale"), x = structure(c(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 (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 34512329.7028 nan 0.1000 -98887.0325
## 2 34417036.3654 nan 0.1000 -55720.6997
## 3 34149297.3303 nan 0.1000 113988.5982
## 4 33947594.6086 nan 0.1000 14199.0207
## 5 33849375.7238 nan 0.1000 66008.1691
## 6 33777776.6579 nan 0.1000 -43909.7435
## 7 33665505.5292 nan 0.1000 45981.8047
## 8 33589320.6718 nan 0.1000 -57636.5067
## 9 33408765.1425 nan 0.1000 50174.2363
## 10 33282074.6736 nan 0.1000 1946.3046
## 20 32560744.3401 nan 0.1000 -14367.2397
## 40 31524021.1588 nan 0.1000 18155.5192
## 60 30910792.1319 nan 0.1000 -227431.9580
## 80 30474970.8547 nan 0.1000 -138640.7481
## 100 30307864.6202 nan 0.1000 -113644.7587
## 120 29936861.1652 nan 0.1000 -47527.6138
## 140 29850901.9405 nan 0.1000 -115291.4326
## 150 29716072.4324 nan 0.1000 -101248.7256
## 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 34352149.8942 nan 0.1000 205494.7768
## 2 33918151.7240 nan 0.1000 -103593.3775
## 3 33649261.4203 nan 0.1000 79686.2053
## 4 33396507.0490 nan 0.1000 122474.2189
## 5 32995975.4695 nan 0.1000 71446.6809
## 6 32843410.4568 nan 0.1000 13696.9118
## 7 32660429.9236 nan 0.1000 -36443.3440
## 8 32561404.4956 nan 0.1000 -11553.8589
## 9 32363790.7470 nan 0.1000 31117.8433
## 10 32220588.5668 nan 0.1000 -56571.4266
## 20 30430222.7739 nan 0.1000 -21659.2691
## 40 28346048.3173 nan 0.1000 -118068.8718
## 60 27006571.2728 nan 0.1000 -85896.3664
## 80 25907315.8006 nan 0.1000 -90472.4636
## 100 25064911.9684 nan 0.1000 -77918.9956
## 120 24440930.8125 nan 0.1000 -205849.8051
## 140 23368397.8528 nan 0.1000 -202238.7708
## 150 23210857.4203 nan 0.1000 12419.0680
## 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 34202668.3212 nan 0.1000 97749.0992
## 2 33444937.6716 nan 0.1000 387253.4565
## 3 32709346.8366 nan 0.1000 151272.4410
## 4 32456055.3734 nan 0.1000 25070.9473
## 5 32240050.8155 nan 0.1000 -51440.1170
## 6 31743358.9036 nan 0.1000 100114.6747
## 7 31492397.5526 nan 0.1000 -207303.7975
## 8 31154853.1737 nan 0.1000 -58467.3755
## 9 30972496.7070 nan 0.1000 46973.7843
## 10 30588188.5881 nan 0.1000 -145591.9224
## 20 28909741.0700 nan 0.1000 -97698.6558
## 40 26684192.4822 nan 0.1000 -90880.6151
## 60 24857906.9826 nan 0.1000 -102025.0076
## 80 24138199.9415 nan 0.1000 -126278.2186
## 100 22981073.8125 nan 0.1000 -145934.7714
## 120 21351556.5442 nan 0.1000 -62695.6445
## 140 20263881.2309 nan 0.1000 -165402.5090
## 150 19620716.6653 nan 0.1000 -102359.4679
## 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 29720566.2048 nan 0.1000 178944.0973
## 2 29605433.9078 nan 0.1000 -74846.2163
## 3 29385051.9338 nan 0.1000 42673.5369
## 4 29288520.1986 nan 0.1000 -166328.5933
## 5 29219885.5480 nan 0.1000 -59906.8367
## 6 29142381.7351 nan 0.1000 -17369.1324
## 7 29026157.2121 nan 0.1000 78939.0299
## 8 28894307.3555 nan 0.1000 81931.2826
## 9 28857787.8742 nan 0.1000 -179025.4411
## 10 28779610.4951 nan 0.1000 -33538.6352
## 20 28151838.1134 nan 0.1000 19365.7048
## 40 27514158.0428 nan 0.1000 -37246.0554
## 60 27102849.9646 nan 0.1000 -222527.8598
## 80 26883370.0540 nan 0.1000 -23512.8122
## 100 26680147.8079 nan 0.1000 -132367.7823
## 120 26578115.5347 nan 0.1000 -117916.7933
## 140 26366553.8413 nan 0.1000 -158022.2177
## 150 26252159.6111 nan 0.1000 -122692.4517
## 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 29629643.1313 nan 0.1000 164760.1874
## 2 29342524.9907 nan 0.1000 136539.8677
## 3 28929302.4157 nan 0.1000 124058.2319
## 4 28741216.0315 nan 0.1000 -6015.2634
## 5 28588825.8231 nan 0.1000 -68155.6598
## 6 28438546.3079 nan 0.1000 -151840.1412
## 7 28301638.0120 nan 0.1000 -81702.1215
## 8 27942323.6268 nan 0.1000 181419.2577
## 9 27723780.2638 nan 0.1000 50184.5923
## 10 27468178.3641 nan 0.1000 -13049.1707
## 20 26705507.0785 nan 0.1000 -146269.2572
## 40 24462650.8483 nan 0.1000 -32782.0657
## 60 23493177.7224 nan 0.1000 -82269.5740
## 80 22915697.8099 nan 0.1000 -74850.1696
## 100 22030453.8503 nan 0.1000 -94240.0846
## 120 21757331.5211 nan 0.1000 -72116.7475
## 140 21287509.0869 nan 0.1000 -24420.7197
## 150 21093223.5678 nan 0.1000 -119479.0643
## 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 29430050.5662 nan 0.1000 87915.7702
## 2 28908988.2484 nan 0.1000 96041.3621
## 3 28413052.3725 nan 0.1000 152822.1775
## 4 28196977.0125 nan 0.1000 -29422.0765
## 5 27937766.8351 nan 0.1000 3907.9304
## 6 27678102.4245 nan 0.1000 -56297.8065
## 7 27504620.1411 nan 0.1000 -78051.9933
## 8 27147086.5564 nan 0.1000 76571.2659
## 9 26957378.8126 nan 0.1000 -15045.2975
## 10 26643115.0648 nan 0.1000 -159296.7359
## 20 24830260.0674 nan 0.1000 -134596.2472
## 40 23045649.9238 nan 0.1000 -99410.9016
## 60 21919483.0254 nan 0.1000 -178166.9997
## 80 20274634.5343 nan 0.1000 -117662.9034
## 100 19708623.1041 nan 0.1000 -94039.6436
## 120 18663813.1034 nan 0.1000 -76644.8729
## 140 17857457.7001 nan 0.1000 -110416.4153
## 150 17271300.2932 nan 0.1000 -79956.6419
## 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 35686880.4514 nan 0.1000 144064.4647
## 2 35571164.3787 nan 0.1000 -66173.0775
## 3 35465431.6614 nan 0.1000 -40653.9017
## 4 35242486.3990 nan 0.1000 70251.2663
## 5 35117567.0360 nan 0.1000 73642.6407
## 6 35052957.8763 nan 0.1000 -133666.7477
## 7 34999370.2829 nan 0.1000 -68628.8916
## 8 34923209.0886 nan 0.1000 -9266.5184
## 9 34807226.6840 nan 0.1000 57795.3484
## 10 34664285.4479 nan 0.1000 84276.0882
## 20 33951837.4660 nan 0.1000 -10512.2614
## 40 32887292.5855 nan 0.1000 -69355.3116
## 60 32258149.9590 nan 0.1000 -51428.1101
## 80 31838005.3576 nan 0.1000 -120892.4887
## 100 31505004.9702 nan 0.1000 -200155.4111
## 120 31341991.1056 nan 0.1000 -33385.5458
## 140 31126384.1631 nan 0.1000 -145464.6168
## 150 30952931.1611 nan 0.1000 14915.7966
## 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 35393389.9446 nan 0.1000 181480.1190
## 2 34950750.2145 nan 0.1000 193404.7870
## 3 34763505.2364 nan 0.1000 -56430.5735
## 4 34629447.9378 nan 0.1000 -89289.9845
## 5 34337617.1861 nan 0.1000 80573.1753
## 6 34175215.5326 nan 0.1000 -93842.1541
## 7 33961016.0406 nan 0.1000 146525.0357
## 8 33737424.7367 nan 0.1000 69690.2911
## 9 33608651.4337 nan 0.1000 -43032.2878
## 10 33446750.1491 nan 0.1000 43223.8856
## 20 31945690.4590 nan 0.1000 -75797.9700
## 40 29702996.1912 nan 0.1000 -131566.2779
## 60 27692553.8895 nan 0.1000 -85909.4092
## 80 26582081.6714 nan 0.1000 -12587.3436
## 100 25349838.8287 nan 0.1000 -77420.9467
## 120 24776456.6462 nan 0.1000 -133994.7715
## 140 24119647.8649 nan 0.1000 -182496.7202
## 150 23768861.8750 nan 0.1000 -27565.3183
## 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 35674421.6569 nan 0.1000 150289.3154
## 2 35308684.5445 nan 0.1000 -35992.4069
## 3 34949041.1334 nan 0.1000 2749.2386
## 4 34497094.3920 nan 0.1000 -86414.4457
## 5 34156991.9843 nan 0.1000 64526.4423
## 6 33983786.4140 nan 0.1000 -55114.8966
## 7 33586280.6867 nan 0.1000 60983.5760
## 8 33267601.2192 nan 0.1000 -21647.7402
## 9 33081511.6996 nan 0.1000 -95309.8285
## 10 32889120.5446 nan 0.1000 -34438.9597
## 20 30017297.8405 nan 0.1000 -117819.8301
## 40 27412899.9721 nan 0.1000 -110714.3663
## 60 25583117.1549 nan 0.1000 -151324.5968
## 80 24396382.4280 nan 0.1000 -98795.0694
## 100 22974176.9395 nan 0.1000 -140463.2819
## 120 22360188.2968 nan 0.1000 -105665.1497
## 140 21054016.9727 nan 0.1000 -138260.3975
## 150 20653093.6875 nan 0.1000 -204403.7095
## 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 32659127.9680 nan 0.1000 -54005.5872
## 2 32571816.5904 nan 0.1000 -58525.6431
## 3 32519584.0732 nan 0.1000 -128920.9984
## 4 32323963.5736 nan 0.1000 200535.0117
## 5 32115151.6113 nan 0.1000 124585.2577
## 6 31950548.8586 nan 0.1000 149679.1097
## 7 31852626.8749 nan 0.1000 48935.8926
## 8 31744082.8665 nan 0.1000 83442.3641
## 9 31671047.2659 nan 0.1000 -21873.4640
## 10 31542221.9247 nan 0.1000 14282.7446
## 20 30940191.9030 nan 0.1000 -5526.3836
## 40 30067511.9636 nan 0.1000 14762.5974
## 60 29381364.9361 nan 0.1000 -91269.1492
## 80 28940335.3301 nan 0.1000 -88213.6093
## 100 28567823.8987 nan 0.1000 -33295.9636
## 120 28342454.1677 nan 0.1000 -61516.2302
## 140 28228476.5595 nan 0.1000 -101454.4789
## 150 28040799.7268 nan 0.1000 -131653.4189
## 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 32506852.2493 nan 0.1000 96017.2233
## 2 32254831.9184 nan 0.1000 10450.1649
## 3 31849210.3362 nan 0.1000 106488.5571
## 4 31592877.4335 nan 0.1000 106738.6189
## 5 31264133.5330 nan 0.1000 202011.3479
## 6 31132020.2425 nan 0.1000 -71404.5004
## 7 30971090.9674 nan 0.1000 108616.7874
## 8 30862020.0232 nan 0.1000 -137705.2344
## 9 30719496.9222 nan 0.1000 43256.5719
## 10 30559625.1129 nan 0.1000 -16086.2724
## 20 28541318.1748 nan 0.1000 27218.3048
## 40 27003482.7646 nan 0.1000 -139061.8512
## 60 25708948.0322 nan 0.1000 -147981.9755
## 80 25002512.9889 nan 0.1000 -121118.6274
## 100 24105163.7721 nan 0.1000 -202199.1078
## 120 23609790.6774 nan 0.1000 -133589.3239
## 140 22995390.8941 nan 0.1000 -188120.2723
## 150 22795462.9130 nan 0.1000 -116096.6622
## 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 32317612.8276 nan 0.1000 188857.6919
## 2 31992876.1970 nan 0.1000 -35260.7650
## 3 31726606.1948 nan 0.1000 6314.9635
## 4 31170707.6670 nan 0.1000 179671.9525
## 5 30899547.8627 nan 0.1000 38566.5653
## 6 30544378.5611 nan 0.1000 5274.9983
## 7 30269166.2923 nan 0.1000 -143986.9473
## 8 30038468.3253 nan 0.1000 -100537.4234
## 9 29926144.9538 nan 0.1000 -190145.7305
## 10 29650292.0791 nan 0.1000 -161002.8530
## 20 27591374.6147 nan 0.1000 -340235.0619
## 40 24699816.5267 nan 0.1000 -107865.8280
## 60 23407098.9073 nan 0.1000 -107622.6855
## 80 21612800.5868 nan 0.1000 -82100.2107
## 100 20307001.7901 nan 0.1000 -28673.7718
## 120 18765759.0758 nan 0.1000 -134404.6943
## 140 18068728.6890 nan 0.1000 -4502.2258
## 150 17573406.6394 nan 0.1000 -79974.9022
## 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 22235838.0587 nan 0.1000 149412.5506
## 2 22063292.2100 nan 0.1000 139845.6721
## 3 22002136.4380 nan 0.1000 -12379.8890
## 4 21876316.6532 nan 0.1000 93745.3354
## 5 21775477.6141 nan 0.1000 80836.0770
## 6 21682232.4337 nan 0.1000 51389.1585
## 7 21589632.7479 nan 0.1000 39936.8988
## 8 21473193.2271 nan 0.1000 30859.6610
## 9 21392567.2254 nan 0.1000 38357.5093
## 10 21320646.0668 nan 0.1000 -63677.3928
## 20 20610536.7070 nan 0.1000 12587.6710
## 40 19679575.8865 nan 0.1000 -17561.1964
## 60 18978541.5366 nan 0.1000 -26059.3100
## 80 18572823.9571 nan 0.1000 -53176.5121
## 100 18189617.2405 nan 0.1000 6763.1151
## 120 17917869.2180 nan 0.1000 -71565.7855
## 140 17577813.0222 nan 0.1000 2767.3811
## 150 17430260.2229 nan 0.1000 12021.8327
## 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 22074911.9058 nan 0.1000 78638.7659
## 2 21834818.8457 nan 0.1000 118048.9960
## 3 21545387.2373 nan 0.1000 92503.7593
## 4 21261144.6012 nan 0.1000 93093.2000
## 5 21121050.4562 nan 0.1000 52436.1100
## 6 20953464.4103 nan 0.1000 100946.7114
## 7 20813318.1975 nan 0.1000 10117.8062
## 8 20656699.8555 nan 0.1000 24039.9421
## 9 20458706.4397 nan 0.1000 83148.3586
## 10 20292827.1397 nan 0.1000 9772.0505
## 20 19053982.4865 nan 0.1000 -86416.0326
## 40 17295088.9350 nan 0.1000 10230.2697
## 60 16230127.2240 nan 0.1000 -24718.5250
## 80 15476800.1606 nan 0.1000 -101310.5313
## 100 14798834.1876 nan 0.1000 -46960.7703
## 120 14238447.0031 nan 0.1000 -24014.0303
## 140 13628497.6720 nan 0.1000 -39585.3304
## 150 13428965.5772 nan 0.1000 -47389.4217
## 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 22189237.6279 nan 0.1000 134819.5337
## 2 21803066.8189 nan 0.1000 44765.0661
## 3 21465485.9555 nan 0.1000 33638.2362
## 4 21174044.5345 nan 0.1000 16073.9549
## 5 20870115.9540 nan 0.1000 122177.3686
## 6 20605630.7045 nan 0.1000 -70146.2870
## 7 20390301.9942 nan 0.1000 56272.6086
## 8 20051629.2135 nan 0.1000 104137.3717
## 9 19902412.1411 nan 0.1000 41813.1716
## 10 19760942.6201 nan 0.1000 82407.2470
## 20 18210630.3378 nan 0.1000 -50421.2268
## 40 16034726.8320 nan 0.1000 15418.1841
## 60 14883831.4305 nan 0.1000 -67894.1151
## 80 13719725.3592 nan 0.1000 -53741.4606
## 100 12798161.5567 nan 0.1000 -32246.8848
## 120 12053463.2354 nan 0.1000 -51475.3451
## 140 11389302.8558 nan 0.1000 -50946.8050
## 150 11133745.2378 nan 0.1000 -59198.9045
## 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 30874489.3858 nan 0.1000 173109.6379
## 2 30383100.0820 nan 0.1000 84371.1510
## 3 30163309.5314 nan 0.1000 -27061.1723
## 4 29954608.9572 nan 0.1000 28050.0723
## 5 29684263.0096 nan 0.1000 53709.4997
## 6 29544403.1843 nan 0.1000 -34458.5274
## 7 29257073.6828 nan 0.1000 9425.2809
## 8 29018735.5866 nan 0.1000 84296.1213
## 9 28779366.6008 nan 0.1000 -46449.6548
## 10 28559688.6550 nan 0.1000 -65132.7781
## 20 27003352.5981 nan 0.1000 -119787.5234
## 40 24903307.2878 nan 0.1000 -76520.8454
## 50 24183739.3304 nan 0.1000 -140847.0051
bt_fit
## Stochastic Gradient Boosting
##
## 1626 samples
## 58 predictor
##
## Pre-processing: centered (58), scaled (58)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1301, 1300, 1302, 1301, 1300
## Resampling results across tuning parameters:
##
## interaction.depth n.trees RMSE Rsquared MAE
## 1 50 5307.278 0.02281856 2579.983
## 1 100 5341.861 0.02300759 2592.613
## 1 150 5337.324 0.02645574 2592.132
## 2 50 5308.731 0.03354737 2551.265
## 2 100 5324.490 0.04169412 2565.615
## 2 150 5342.209 0.04242432 2589.154
## 3 50 5305.882 0.03976201 2541.176
## 3 100 5345.811 0.04668681 2592.966
## 3 150 5404.000 0.04242355 2655.843
##
## 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 = 50, interaction.depth = 3, 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"
Automation - Jonathan
#rmarkdown::render(
# "Tanley-Wood-Project2.Rmd",
# output_format="github_document",
# output_dir="./Analysis",
# output_options = list(
# html_preview = FALSE
# )
#)