Apply 1
Alden Dimick
2025-02-18
Import Data
youtube <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-02/youtube.csv')
skimr::skim(youtube)| Name | youtube |
| Number of rows | 247 |
| Number of columns | 25 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| logical | 7 |
| numeric | 7 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| brand | 0 | 1.00 | 3 | 9 | 0 | 10 | 0 |
| superbowl_ads_dot_com_url | 0 | 1.00 | 34 | 120 | 0 | 244 | 0 |
| youtube_url | 11 | 0.96 | 43 | 43 | 0 | 233 | 0 |
| id | 11 | 0.96 | 11 | 11 | 0 | 233 | 0 |
| kind | 16 | 0.94 | 13 | 13 | 0 | 1 | 0 |
| etag | 16 | 0.94 | 27 | 27 | 0 | 228 | 0 |
| title | 16 | 0.94 | 6 | 99 | 0 | 228 | 0 |
| description | 50 | 0.80 | 3 | 3527 | 0 | 194 | 0 |
| thumbnail | 129 | 0.48 | 48 | 48 | 0 | 118 | 0 |
| channel_title | 16 | 0.94 | 3 | 37 | 0 | 185 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| funny | 0 | 1 | 0.69 | TRU: 171, FAL: 76 |
| show_product_quickly | 0 | 1 | 0.68 | TRU: 169, FAL: 78 |
| patriotic | 0 | 1 | 0.17 | FAL: 206, TRU: 41 |
| celebrity | 0 | 1 | 0.29 | FAL: 176, TRU: 71 |
| danger | 0 | 1 | 0.30 | FAL: 172, TRU: 75 |
| animals | 0 | 1 | 0.37 | FAL: 155, TRU: 92 |
| use_sex | 0 | 1 | 0.27 | FAL: 181, TRU: 66 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1.00 | 2010.19 | 5.86 | 2000 | 2005 | 2010 | 2015.00 | 2020 | ▇▇▇▇▆ |
| view_count | 16 | 0.94 | 1407556.46 | 11971111.01 | 10 | 6431 | 41379 | 170015.50 | 176373378 | ▇▁▁▁▁ |
| like_count | 22 | 0.91 | 4146.03 | 23920.40 | 0 | 19 | 130 | 527.00 | 275362 | ▇▁▁▁▁ |
| dislike_count | 22 | 0.91 | 833.54 | 6948.52 | 0 | 1 | 7 | 24.00 | 92990 | ▇▁▁▁▁ |
| favorite_count | 16 | 0.94 | 0.00 | 0.00 | 0 | 0 | 0 | 0.00 | 0 | ▁▁▇▁▁ |
| comment_count | 25 | 0.90 | 188.64 | 986.46 | 0 | 1 | 10 | 50.75 | 9190 | ▇▁▁▁▁ |
| category_id | 16 | 0.94 | 19.32 | 8.00 | 1 | 17 | 23 | 24.00 | 29 | ▃▁▂▆▇ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| published_at | 16 | 0.94 | 2006-02-06 10:02:36 | 2021-01-27 13:11:29 | 2013-01-31 09:13:55 | 227 |
data <- youtube %>%
# Treat missing values
select(-superbowl_ads_dot_com_url, -youtube_url, -kind, -etag, -channel_title, -comment_count, -thumbnail, -published_at, -description, -dislike_count, -favorite_count, -view_count) %>%
na.omit() %>%
mutate(like_count = log(like_count + 1)) %>%
mutate(category_id = as.factor(category_id)) %>%
mutate(across(where(is.character),as.factor)) %>%
mutate(title = as.character(title)) %>%
mutate(across(where(is.logical),as.factor))Explore Data
Identify good predictors.
celebrity
data %>%
ggplot(aes(like_count, funny)) +
geom_point()
Brand
data %>%
# tokenize title
unnest_tokens(output = word, input = title) %>%
# calculate avg rent per word
group_by(word) %>%
summarise(like_count = mean(like_count),
n = n()) %>%
ungroup() %>%
# Plot
ggplot(aes(like_count, fct_reorder(word, like_count))) +
geom_point() +
labs(y = "Brands")
EDA Shortcut
# Step 1: Prepare Data
data_binarized_tbl <- data %>%
select(-id, -title) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 225
## Columns: 43
## $ `year__-Inf_2005` <dbl> 0, 0, 0, 0, 1, 0, 0, 0, …
## $ year__2005_2010 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, …
## $ year__2010_2015 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2015_Inf <dbl> 1, 1, 0, 1, 0, 1, 1, 1, …
## $ brand__Bud_Light <dbl> 0, 1, 1, 0, 1, 0, 0, 0, …
## $ brand__Budweiser <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `brand__Coca-Cola` <dbl> 0, 0, 0, 0, 0, 0, 1, 0, …
## $ brand__Doritos <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `brand__E-Trade` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand__Hynudai <dbl> 0, 0, 0, 1, 0, 0, 0, 0, …
## $ brand__Kia <dbl> 0, 0, 0, 0, 0, 0, 0, 1, …
## $ brand__NFL <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand__Pepsi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand__Toyota <dbl> 1, 0, 0, 0, 0, 1, 0, 0, …
## $ funny__FALSE <dbl> 1, 0, 0, 1, 0, 0, 0, 1, …
## $ funny__TRUE <dbl> 0, 1, 1, 0, 1, 1, 1, 0, …
## $ show_product_quickly__FALSE <dbl> 1, 0, 1, 0, 0, 0, 1, 1, …
## $ show_product_quickly__TRUE <dbl> 0, 1, 0, 1, 1, 1, 0, 0, …
## $ patriotic__FALSE <dbl> 1, 1, 1, 1, 1, 1, 1, 1, …
## $ patriotic__TRUE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ celebrity__FALSE <dbl> 1, 0, 1, 1, 1, 0, 0, 0, …
## $ celebrity__TRUE <dbl> 0, 1, 0, 0, 0, 1, 1, 1, …
## $ danger__FALSE <dbl> 1, 0, 0, 1, 0, 0, 1, 1, …
## $ danger__TRUE <dbl> 0, 1, 1, 0, 1, 1, 0, 0, …
## $ animals__FALSE <dbl> 1, 1, 0, 1, 0, 0, 0, 1, …
## $ animals__TRUE <dbl> 0, 0, 1, 0, 1, 1, 1, 0, …
## $ use_sex__FALSE <dbl> 1, 1, 1, 1, 0, 1, 1, 1, …
## $ use_sex__TRUE <dbl> 0, 0, 0, 0, 1, 0, 0, 0, …
## $ `like_count__-Inf_2.99573227355399` <dbl> 0, 0, 0, 1, 0, 0, 0, 0, …
## $ like_count__2.99573227355399_4.87519732320115 <dbl> 0, 0, 1, 0, 1, 1, 0, 1, …
## $ like_count__4.87519732320115_6.26909628370626 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, …
## $ like_count__6.26909628370626_Inf <dbl> 1, 0, 0, 0, 0, 0, 1, 0, …
## $ category_id__1 <dbl> 1, 0, 0, 0, 0, 1, 0, 0, …
## $ category_id__2 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, …
## $ category_id__10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id__15 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id__17 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, …
## $ category_id__22 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, …
## $ category_id__23 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id__24 <dbl> 0, 0, 0, 0, 1, 0, 1, 0, …
## $ category_id__25 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id__27 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, …
## $ `category_id__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(like_count__6.26909628370626_Inf)
data_corr_tbl## # A tibble: 43 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 like_count 6.26909628370626_Inf 1
## 2 like_count -Inf_2.99573227355399 -0.339
## 3 like_count 4.87519732320115_6.26909628370626 -0.331
## 4 like_count 2.99573227355399_4.87519732320115 -0.327
## 5 brand Doritos 0.281
## 6 brand NFL 0.250
## 7 brand Bud_Light -0.212
## 8 year 2015_Inf 0.202
## 9 year -Inf_2005 -0.193
## 10 category_id 1 0.133
## # ℹ 33 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Models
Split Data
# Split into train and test dataset
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)
# Further split training dataset for cross-validation
set.seed(2345)
data_cv <- rsample::vfold_cv(data_train)
data_cv## # 10-fold cross-validation
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [151/17]> Fold01
## 2 <split [151/17]> Fold02
## 3 <split [151/17]> Fold03
## 4 <split [151/17]> Fold04
## 5 <split [151/17]> Fold05
## 6 <split [151/17]> Fold06
## 7 <split [151/17]> Fold07
## 8 <split [151/17]> Fold08
## 9 <split [152/16]> Fold09
## 10 <split [152/16]> Fold10library(usemodels)
usemodels::use_xgboost(like_count ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = like_count ~ ., data = data_train) %>%
## step_zv(all_predictors())
##
## xgboost_spec <-
## boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
## loss_reduction = tune(), sample_size = tune()) %>%
## set_mode("classification") %>%
## set_engine("xgboost")
##
## xgboost_workflow <-
## workflow() %>%
## add_recipe(xgboost_recipe) %>%
## add_model(xgboost_spec)
##
## set.seed(18995)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))# Specify Recipe
xgboost_recipe <- recipe(formula = like_count ~ ., data = data_train) %>%
recipes::update_role(id, new_role = "id variables") %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tfidf(title) %>%
step_dummy(all_nominal_predictors(),one_hot = TRUE) %>%
step_zv(all_predictors())
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 168
## Columns: 140
## $ year <dbl> 2013, 2015, 2008, 2010, 2009, 2007, 2010, …
## $ id <fct> WTf0XGpINJI, 7_EfXuGev24, 2_LWZe2BGaE, 6cM…
## $ like_count <dbl> 3.1354942, 0.0000000, 3.6109179, 6.0038871…
## $ tfidf_title_2000 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2001 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2002 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
## $ tfidf_title_2005 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2007 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2009 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_2010 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_2011 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2012 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2013 <dbl> 0.6437752, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_2014 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_2015 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2016 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2018 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_2019 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2020 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_44 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_a <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_ad <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_ads <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_advertisement <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_all <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_and <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_baby <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_bears <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_beer <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_best <dbl> 0.0000000, 0.0000000, 0.0000000, 0.6346645…
## $ tfidf_title_bestbuds <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_big <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_bowl <dbl> 0.2488648, 0.0000000, 0.2488648, 0.1777606…
## $ tfidf_title_britney <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_bud <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_budweiser <dbl> 0.0000000, 0.6812715, 0.0000000, 0.0000000…
## $ tfidf_title_camry <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_car <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_cedric <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_cindy <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_coca <dbl> 0.0000000, 0.0000000, 0.5129899, 0.0000000…
## $ tfidf_title_coke <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
## $ tfidf_title_cola <dbl> 0.0000000, 0.0000000, 0.5002872, 0.0000000…
## $ tfidf_title_commercial <dbl> 0.2135681, 0.3559469, 0.2135681, 0.0000000…
## $ tfidf_title_commercials <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_cool <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_crash <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_date <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_diet <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
## $ tfidf_title_dilly <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_dog <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_dogs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_doritos <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_e <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_elantra <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_etrade <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_extended <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_factory <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_fantasy <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_featuring <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_flavor <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_fly <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_full <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_funny <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_game <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
## $ tfidf_title_genesis <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_girlfriend <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_great <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_happiness <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_hd <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_horse <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_hyundai <dbl> 0.5002872, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_in <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_is <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_island <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_it <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_kia <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_light <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_lighta <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_love <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_new <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_nfl <dbl> 0.0000000, 0.0000000, 0.0000000, 0.4810423…
## $ tfidf_title_of <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_official <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_on <dbl> 0.0000000, 0.0000000, 0.0000000, 0.5775788…
## $ tfidf_title_one <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_pepsi <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_puppy <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_spot <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_starring <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_super <dbl> 0.2488648, 0.0000000, 0.2488648, 0.1777606…
## $ tfidf_title_superbowl <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_the <dbl> 0.0000000, 0.6931472, 0.0000000, 0.2970631…
## $ tfidf_title_toyota <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_trade <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_tv <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_usa <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.…
## $ tfidf_title_version <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_vs <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.…
## $ tfidf_title_winner <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_with <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_title_x <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_xliv <dbl> 0.0000000, 0.0000000, 0.0000000, 0.5775788…
## $ brand_Bud.Light <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand_Budweiser <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ brand_Coca.Cola <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ brand_Doritos <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand_E.Trade <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ brand_Hynudai <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, …
## $ brand_Kia <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ brand_NFL <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ brand_Pepsi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ brand_Toyota <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ funny_FALSE. <dbl> 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, …
## $ funny_TRUE. <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, …
## $ show_product_quickly_FALSE. <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ show_product_quickly_TRUE. <dbl> 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, …
## $ patriotic_FALSE. <dbl> 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, …
## $ patriotic_TRUE. <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, …
## $ celebrity_FALSE. <dbl> 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, …
## $ celebrity_TRUE. <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, …
## $ danger_FALSE. <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, …
## $ danger_TRUE. <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, …
## $ animals_FALSE. <dbl> 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, …
## $ animals_TRUE. <dbl> 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, …
## $ use_sex_FALSE. <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, …
## $ use_sex_TRUE. <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ category_id_X1 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ category_id_X2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ category_id_X10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X15 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X17 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ category_id_X19 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X22 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, …
## $ category_id_X23 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X24 <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ category_id_X25 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X26 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X27 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category_id_X29 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …# Specify Model
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
# Combine recipe and model using workflow
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
# Tune hyperparameters
set.seed(89984)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)