Apply your Data 1
Javony Deleon
2026-02-07
Goal: to predict the Youtube like count Click here for the data
.
Import Data
youtube <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-02/youtube.csv')## Rows: 247 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): brand, superbowl_ads_dot_com_url, youtube_url, id, kind, etag, ti...
## dbl (7): year, view_count, like_count, dislike_count, favorite_count, comm...
## lgl (7): funny, show_product_quickly, patriotic, celebrity, danger, animal...
## dttm (1): published_at
##
## 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.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(-thumbnail, -description, -favorite_count, -comment_count, -published_at, -category_id, -superbowl_ads_dot_com_url, -youtube_url, -id ,-etag, -channel_title) %>%
na.omit() %>%
# log transform variables with pos-skewed distribution
mutate(like_count = log(like_count))Explore Data
Identify good predictors
like_count
data %>%
ggplot(aes(like_count, view_count)) +
scale_y_log10() +
geom_point()
data %>%
ggplot(aes(like_count, as.factor(brand))) +
geom_boxplot() ## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
title
data %>%
# tokenism title
unnest_tokens(output = word, input = brand) %>%
# calculate avg rent per word
group_by(word) %>%
summarise(like_count = mean(like_count),
n = n()) %>%
ungroup() %>%
filter(n > 10, !str_detect(word, "\\d")) %>%
slice_max(order_by = like_count, n = 20) %>%
# plot
ggplot(aes(like_count, fct_reorder(word, like_count))) +
geom_point() +
labs(y = "word in Title")
# step 1: prepare data
data_binarized_tbl <- data %>%
select(-dislike_count, -title) %>%
binarize()
data_binarized_tbl %>% glimpse() ## Rows: 225
## Columns: 36
## $ `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__0 <dbl> 1, 0, 0, 1, 0, 0, 0, 1, …
## $ funny__1 <dbl> 0, 1, 1, 0, 1, 1, 1, 0, …
## $ show_product_quickly__0 <dbl> 1, 0, 1, 0, 0, 0, 1, 1, …
## $ show_product_quickly__1 <dbl> 0, 1, 0, 1, 1, 1, 0, 0, …
## $ patriotic__0 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, …
## $ patriotic__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, …
## $ celebrity__0 <dbl> 1, 0, 1, 1, 1, 0, 0, 0, …
## $ celebrity__1 <dbl> 0, 1, 0, 0, 0, 1, 1, 1, …
## $ danger__0 <dbl> 1, 0, 0, 1, 0, 0, 1, 1, …
## $ danger__1 <dbl> 0, 1, 1, 0, 1, 1, 0, 0, …
## $ animals__0 <dbl> 1, 1, 0, 1, 0, 0, 0, 1, …
## $ animals__1 <dbl> 0, 0, 1, 0, 1, 1, 1, 0, …
## $ use_sex__0 <dbl> 1, 1, 1, 1, 0, 1, 1, 1, …
## $ use_sex__1 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, …
## $ `view_count__-Inf_6641` <dbl> 0, 0, 0, 1, 0, 0, 0, 0, …
## $ view_count__6641_43983 <dbl> 0, 0, 0, 0, 1, 1, 0, 1, …
## $ view_count__43983_175482 <dbl> 1, 1, 1, 0, 0, 0, 0, 0, …
## $ view_count__175482_Inf <dbl> 0, 0, 0, 0, 0, 0, 1, 0, …
## $ `like_count__-Inf_2.94443897916644` <dbl> 0, 0, 0, 1, 0, 0, 0, 0, …
## $ like_count__2.94443897916644_4.86753445045558 <dbl> 0, 0, 1, 0, 1, 1, 0, 1, …
## $ like_count__4.86753445045558_6.26720054854136 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, …
## $ like_count__6.26720054854136_Inf <dbl> 1, 0, 0, 0, 0, 0, 1, 0, …# step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(like_count__6.26720054854136_Inf )
data_corr_tbl## # A tibble: 36 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 like_count 6.26720054854136_Inf 1
## 2 view_count 175482_Inf 0.715
## 3 like_count -Inf_2.94443897916644 -0.339
## 4 view_count -Inf_6641 -0.335
## 5 like_count 4.86753445045558_6.26720054854136 -0.331
## 6 like_count 2.94443897916644_4.86753445045558 -0.327
## 7 view_count 6641_43983 -0.308
## 8 brand Doritos 0.281
## 9 brand NFL 0.250
## 10 brand Bud_Light -0.212
## # ℹ 26 more rows# step 3:
data_corr_tbl %>%
plot_correlation_funnel()## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
## Please report the issue at
## <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
## Please report the issue at
## <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Models
split data
# data <- sample_n(data, 100)
# 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 data set 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_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 <-
xgboost_recipe <-
recipe(formula = like_count ~ ., data = data_train) %>%
recipes::update_role(title, new_role = "id variable") %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tfidf(title) %>%
step_other(brand) %>%
step_dummy(brand, one_hot = TRUE) %>%
step_log(view_count, like_count, dislike_count)
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Warning in bake.step_log(x$steps[[i]], new_data = training): NaNs produced## Rows: 168
## Columns: 120
## $ year <dbl> 2013, 2015, 2008, 2010, 2009, 2007, 2010, 20…
## $ funny <lgl> TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE…
## $ show_product_quickly <lgl> TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, …
## $ patriotic <lgl> FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE…
## $ celebrity <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRU…
## $ danger <lgl> TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALS…
## $ animals <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE…
## $ use_sex <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FAL…
## $ kind <fct> youtube#video, youtube#video, youtube#video,…
## $ view_count <dbl> 8.366835, 4.369448, 9.565704, 11.422760, 10.…
## $ dislike_count <dbl> -Inf, -Inf, 0.6931472, 2.6390573, 0.6931472,…
## $ like_count <dbl> 1.1285084, NaN, 1.2763453, 1.7919953, 1.2517…
## $ tfidf_title_2000 <dbl> 0, 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, 0,…
## $ tfidf_title_2002 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_2005 <dbl> 0, 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, 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, 0,…
## $ tfidf_title_2012 <dbl> 0, 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, 0,…
## $ tfidf_title_2016 <dbl> 0, 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, 0,…
## $ tfidf_title_2020 <dbl> 0, 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, 0,…
## $ tfidf_title_advertisement <dbl> 0, 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, 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, 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, 0,…
## $ tfidf_title_car <dbl> 0, 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, 0,…
## $ tfidf_title_cindy <dbl> 0, 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, 0.00…
## $ 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, 0,…
## $ tfidf_title_cool <dbl> 0, 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, 0,…
## $ tfidf_title_date <dbl> 0, 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, 0.00…
## $ tfidf_title_dilly <dbl> 0, 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, 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, 0,…
## $ tfidf_title_etrade <dbl> 0, 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, 0,…
## $ tfidf_title_featuring <dbl> 0, 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, 0,…
## $ tfidf_title_full <dbl> 0, 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, 0.00…
## $ tfidf_title_genesis <dbl> 0, 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, 0,…
## $ tfidf_title_great <dbl> 0, 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, 0,…
## $ tfidf_title_is <dbl> 0, 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, 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, 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, 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, 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, 0,…
## $ tfidf_title_starring <dbl> 0, 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, 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, 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, 0.00…
## $ tfidf_title_version <dbl> 0, 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, 0.00…
## $ tfidf_title_winner <dbl> 0, 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, 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, 0,…
## $ brand_Budweiser <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,…
## $ brand_Coca.Cola <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,…
## $ brand_Doritos <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ brand_Hynudai <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ brand_Pepsi <dbl> 0, 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, 0,…
## $ brand_other <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,…# specify model
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
# combine recipe and model using workflow
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
# tune hyperprarameters
set.seed(344)
xgboost_tune <-
tune_grid(xgboost_workflow, resamples = data_cv, grid = 5)## i Creating pre-processing data to finalize 1 unknown parameter: "mtry"## Warning in bake.step_log(x$steps[[i]], new_data = training): NaNs produced## → A | warning: NaNs produced## There were issues with some computations A: x1 → B | error: For a classification model, the outcome should be a <factor>, not a
## double vector.
## There were issues with some computations A: x1There were issues with some computations A: x2 B: x1There were issues with some computations A: x3 B: x3There were issues with some computations A: x4 B: x6There were issues with some computations A: x5 B: x10There were issues with some computations A: x6 B: x15There were issues with some computations A: x7 B: x15There were issues with some computations A: x8 B: x16There were issues with some computations A: x9 B: x18There were issues with some computations A: x10 B: x21There were issues with some computations A: x11 B: x25There were issues with some computations A: x12 B: x30There were issues with some computations A: x13 B: x30There were issues with some computations A: x14 B: x31There were issues with some computations A: x15 B: x33There were issues with some computations A: x16 B: x36There were issues with some computations A: x17 B: x40There were issues with some computations A: x18 B: x45There were issues with some computations A: x19 B: x45There were issues with some computations A: x20 B: x46There were issues with some computations A: x21 B: x48There were issues with some computations A: x22 B: x51There were issues with some computations A: x23 B: x55There were issues with some computations A: x25 B: x60There were issues with some computations A: x26 B: x61There were issues with some computations A: x27 B: x63There were issues with some computations A: x28 B: x66There were issues with some computations A: x29 B: x70There were issues with some computations A: x30 B: x75There were issues with some computations A: x31 B: x75There were issues with some computations A: x32 B: x76There were issues with some computations A: x33 B: x78There were issues with some computations A: x34 B: x81There were issues with some computations A: x35 B: x85There were issues with some computations A: x36 B: x90There were issues with some computations A: x37 B: x90There were issues with some computations A: x38 B: x91There were issues with some computations A: x39 B: x93There were issues with some computations A: x40 B: x96There were issues with some computations A: x41 B: x100There were issues with some computations A: x42 B: x105There were issues with some computations A: x43 B: x105There were issues with some computations A: x44 B: x106There were issues with some computations A: x45 B: x108There were issues with some computations A: x46 B: x111There were issues with some computations A: x47 B: x115There were issues with some computations A: x48 B: x120There were issues with some computations A: x49 B: x120There were issues with some computations A: x50 B: x121There were issues with some computations A: x51 B: x123There were issues with some computations A: x52 B: x126There were issues with some computations A: x54 B: x135There were issues with some computations A: x55 B: x135There were issues with some computations A: x56 B: x136There were issues with some computations A: x57 B: x138There were issues with some computations A: x58 B: x141There were issues with some computations A: x59 B: x145There were issues with some computations A: x60 B: x150## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.## There were issues with some computations A: x60 B: x150