Apply to Your Data 2: Horror Movies
Stephen Morris
2024-02-27
#Import Data:
horror <- horror_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-01/horror_movies.csv')## Rows: 32540 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): original_title, title, original_language, overview, tagline, post...
## dbl (8): id, popularity, vote_count, vote_average, budget, revenue, runtim...
## lgl (1): adult
## date (1): release_date
##
## 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(horror)| Name | horror |
| Number of rows | 32540 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| Date | 1 |
| logical | 1 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| original_title | 0 | 1.00 | 1 | 191 | 0 | 30296 | 0 |
| title | 0 | 1.00 | 1 | 191 | 0 | 29563 | 0 |
| original_language | 0 | 1.00 | 2 | 2 | 0 | 97 | 0 |
| overview | 1286 | 0.96 | 1 | 1000 | 0 | 31020 | 0 |
| tagline | 19835 | 0.39 | 1 | 237 | 0 | 12513 | 0 |
| poster_path | 4474 | 0.86 | 30 | 32 | 0 | 28048 | 0 |
| status | 0 | 1.00 | 7 | 15 | 0 | 4 | 0 |
| backdrop_path | 18995 | 0.42 | 29 | 32 | 0 | 13536 | 0 |
| genre_names | 0 | 1.00 | 6 | 144 | 0 | 772 | 0 |
| collection_name | 30234 | 0.07 | 4 | 56 | 0 | 815 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| release_date | 0 | 1 | 1950-01-01 | 2022-12-31 | 2012-12-09 | 10999 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| adult | 0 | 1 | 0 | FAL: 32540 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 445910.83 | 305744.67 | 17 | 146494.8 | 426521.00 | 707534.00 | 1033095.00 | ▇▆▆▅▅ |
| popularity | 0 | 1.00 | 4.01 | 37.51 | 0 | 0.6 | 0.84 | 2.24 | 5088.58 | ▇▁▁▁▁ |
| vote_count | 0 | 1.00 | 62.69 | 420.89 | 0 | 0.0 | 2.00 | 11.00 | 16900.00 | ▇▁▁▁▁ |
| vote_average | 0 | 1.00 | 3.34 | 2.88 | 0 | 0.0 | 4.00 | 5.70 | 10.00 | ▇▂▆▃▁ |
| budget | 0 | 1.00 | 543126.59 | 4542667.81 | 0 | 0.0 | 0.00 | 0.00 | 200000000.00 | ▇▁▁▁▁ |
| revenue | 0 | 1.00 | 1349746.73 | 14430479.15 | 0 | 0.0 | 0.00 | 0.00 | 701842551.00 | ▇▁▁▁▁ |
| runtime | 0 | 1.00 | 62.14 | 41.00 | 0 | 14.0 | 80.00 | 91.00 | 683.00 | ▇▁▁▁▁ |
| collection | 30234 | 0.07 | 481534.88 | 324498.16 | 656 | 155421.0 | 471259.00 | 759067.25 | 1033032.00 | ▇▅▅▅▅ |
data <- horror %>%
# Treat Missing Values
select(-tagline, -release_date) %>%
filter(budget != 0) %>%
filter(revenue != 0) %>%
na.omit() %>%
# Log transform variables with pos-skewed distribution
mutate(vote_average = log(vote_average))Goal: Build a regression model to predict average movie rating (vote_average). Using the horror_movies dataset.
#Explore Data:
Identify Good Predictors.
Budget
data %>%
ggplot(aes(vote_average, budget)) +
scale_y_log10() +
geom_point()
data %>%
ggplot(aes(vote_average, as.factor(runtime))) +
geom_boxplot()
Correlation Plot
# Step 1: Prepare Data
data_binarize_tbl <- data %>%
select(-id, -original_title, -title, -overview) %>%
binarize()
data_binarize_tbl %>%
glimpse()## Rows: 390
## Columns: 76
## $ original_language__en <dbl> 1, 1, 1, 1, …
## $ original_language__es <dbl> 0, 0, 0, 0, …
## $ original_language__hi <dbl> 0, 0, 0, 0, …
## $ original_language__ja <dbl> 0, 0, 0, 0, …
## $ original_language__ko <dbl> 0, 0, 0, 0, …
## $ `original_language__-OTHER` <dbl> 0, 0, 0, 0, …
## $ `poster_path__/11tOuxWiGFzL60bVwoiF9SSoMLc.jpg` <dbl> 0, 0, 0, 0, …
## $ `poster_path__-OTHER` <dbl> 1, 1, 1, 1, …
## $ `popularity__-Inf_14.8335` <dbl> 0, 0, 0, 0, …
## $ popularity__14.8335_27.89 <dbl> 0, 0, 0, 0, …
## $ popularity__27.89_51.0185 <dbl> 0, 0, 0, 0, …
## $ popularity__51.0185_Inf <dbl> 1, 1, 1, 1, …
## $ `vote_count__-Inf_424` <dbl> 1, 0, 0, 0, …
## $ vote_count__424_1095.5 <dbl> 0, 1, 0, 0, …
## $ vote_count__1095.5_2517.5 <dbl> 0, 0, 1, 1, …
## $ vote_count__2517.5_Inf <dbl> 0, 0, 0, 0, …
## $ `vote_average__-Inf_1.7227665977411` <dbl> 0, 0, 0, 0, …
## $ vote_average__1.7227665977411_1.80828877117927 <dbl> 1, 0, 0, 0, …
## $ vote_average__1.80828877117927_1.88706964903238 <dbl> 0, 0, 0, 0, …
## $ vote_average__1.88706964903238_Inf <dbl> 0, 1, 1, 1, …
## $ `budget__-Inf_2500000` <dbl> 0, 0, 0, 0, …
## $ budget__2500000_8150000 <dbl> 0, 0, 0, 0, …
## $ `budget__8150000_1.9e+07` <dbl> 0, 1, 0, 0, …
## $ `budget__1.9e+07_Inf` <dbl> 1, 0, 1, 1, …
## $ `revenue__-Inf_10341767.5` <dbl> 1, 0, 0, 0, …
## $ revenue__10341767.5_33900000 <dbl> 0, 1, 0, 0, …
## $ revenue__33900000_91132596.5 <dbl> 0, 0, 0, 0, …
## $ revenue__91132596.5_Inf <dbl> 0, 0, 1, 1, …
## $ `runtime__-Inf_89` <dbl> 1, 0, 0, 0, …
## $ runtime__89_96 <dbl> 0, 0, 0, 0, …
## $ runtime__96_104.75 <dbl> 0, 0, 0, 0, …
## $ runtime__104.75_Inf <dbl> 0, 1, 1, 1, …
## $ `backdrop_path__/114yPZmKgsQVTSt0BDx5WagBOvW.jpg` <dbl> 0, 0, 0, 0, …
## $ `backdrop_path__-OTHER` <dbl> 1, 1, 1, 1, …
## $ `genre_names__Action,_Adventure,_Horror,_Science_Fiction` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Action,_Fantasy,_Horror` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Action,_Horror,_Science_Fiction` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Action,_Horror,_Thriller` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Adventure,_Horror,_Thriller` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Comedy,_Fantasy,_Horror` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Comedy,_Horror` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Comedy,_Horror,_Science_Fiction` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Crime,_Horror,_Thriller` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Drama,_Horror,_Thriller` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Fantasy,_Horror` <dbl> 0, 0, 0, 0, …
## $ genre_names__Horror <dbl> 0, 0, 0, 0, …
## $ `genre_names__Horror,_Mystery` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Horror,_Mystery,_Thriller` <dbl> 1, 1, 0, 1, …
## $ `genre_names__Horror,_Science_Fiction` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Horror,_Science_Fiction,_Thriller` <dbl> 0, 0, 0, 0, …
## $ `genre_names__Horror,_Thriller` <dbl> 0, 0, 1, 0, …
## $ `genre_names__-OTHER` <dbl> 0, 0, 0, 0, …
## $ `collection__-Inf_12263` <dbl> 0, 0, 0, 1, …
## $ collection__12263_111751 <dbl> 1, 0, 1, 0, …
## $ collection__111751_355090.5 <dbl> 0, 0, 0, 0, …
## $ collection__355090.5_Inf <dbl> 0, 1, 0, 0, …
## $ `collection_name__[REC]_Collection` <dbl> 0, 0, 0, 0, …
## $ collection_name__A_Nightmare_on_Elm_Street_Collection <dbl> 0, 0, 0, 0, …
## $ `collection_name__Child's_Play_Collection` <dbl> 0, 0, 0, 0, …
## $ collection_name__Evil_Dead_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Final_Destination_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Friday_the_13th_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Halloween_Collection <dbl> 0, 0, 1, 0, …
## $ collection_name__Hellraiser_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Insidious_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Jaws_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Jeepers_Creepers_Collection <dbl> 1, 0, 0, 0, …
## $ collection_name__Living_Dead_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Paranormal_Activity_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Resident_Evil_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Saw_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__Scream_Collection <dbl> 0, 0, 0, 1, …
## $ collection_name__Texas_Chainsaw_Massacre_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__The_Exorcist_Collection <dbl> 0, 0, 0, 0, …
## $ collection_name__The_Purge_Collection <dbl> 0, 0, 0, 0, …
## $ `collection_name__-OTHER` <dbl> 0, 1, 0, 0, …# Step 2: Correlate
data_corr_tbl <- data_binarize_tbl %>%
correlate(vote_average__1.88706964903238_Inf)
# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Models
# data <- sample_n(data, 100)
# Split into train and test dataset
set.seed(123)
data_split <- initial_split(horror_movies, strata = vote_average)
data_train <- training(data_split)
data_test <- testing(data_split)
# Further split training dataset for cross-validation
set.seed(234)
data_cv <- vfold_cv(data_train, strata = vote_average)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [21962/2442]> Fold01
## 2 <split [21962/2442]> Fold02
## 3 <split [21963/2441]> Fold03
## 4 <split [21963/2441]> Fold04
## 5 <split [21964/2440]> Fold05
## 6 <split [21964/2440]> Fold06
## 7 <split [21964/2440]> Fold07
## 8 <split [21964/2440]> Fold08
## 9 <split [21965/2439]> Fold09
## 10 <split [21965/2439]> Fold10library(usemodels)
usemodels::use_xgboost(vote_average ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = vote_average ~ ., 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(29793)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))#specify recipe
#Specifiy Recipie
xgboost_recipe <-
recipe(formula = vote_average ~ ., data = data_train) %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tfidf(title) %>%
step_other(status) %>%
step_dummy(status, one_hot = TRUE) %>%
step_log(runtime)
xgboost_recipe %>% prep() %>% juice %>% glimpse()## Rows: 24,404
## Columns: 120
## $ id <dbl> 1014226, 49690, 926762, 666479, 943221, 998340, …
## $ original_title <fct> "Sonríe", "Smile", "Living with Chucky", "Sonríe…
## $ original_language <fct> es, it, en, eo, es, en, en, en, en, en, fr, en, …
## $ overview <fct> NA, "A carefree summer vacation turns into an in…
## $ tagline <fct> NA, "Point. Shoot. Die.", "It Isn't Child's Play…
## $ release_date <date> 2022-08-18, 2009-08-28, 2022-08-13, 2012-10-23,…
## $ poster_path <fct> /wHowKf77gNZbVo9yu5ggiNBFCdJ.jpg, /5Hk4ubepZZNwl…
## $ popularity <dbl> 939.712, 266.502, 163.646, 138.633, 122.085, 106…
## $ vote_count <dbl> 1, 16, 2, 1, 4, 0, 2, 0, 0, 1, 13, 25, 0, 605, 1…
## $ budget <dbl> 0e+00, 0e+00, 0e+00, 0e+00, 0e+00, 0e+00, 0e+00,…
## $ revenue <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16184, 0,…
## $ runtime <dbl> -Inf, 4.7791235, 4.6539604, -Inf, -Inf, 2.708050…
## $ adult <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
## $ backdrop_path <fct> NA, /9t60OgpIzoyLiL4nmQXb0ZNmbQA.jpg, NA, NA, /x…
## $ genre_names <fct> "Horror, Thriller", "Horror, Thriller", "Documen…
## $ collection <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4796…
## $ collection_name <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Rob…
## $ vote_average <dbl> 1.0, 3.9, 2.0, 2.0, 3.3, 0.0, 4.0, 0.0, 0.0, 2.0…
## $ tfidf_title_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_3 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_a <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_alien <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_all <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_an <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_and <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_at <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_bad <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_beast <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_beyond <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_black <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_blood <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_bloody <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_curse <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_dark <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_darkness <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_day <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_de <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.443397…
## $ tfidf_title_dead <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_death <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_demon <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_devil <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ `tfidf_title_devil's` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_die <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_do <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `tfidf_title_don't` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_dracula <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_el <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_end <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_evil <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_eyes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_fear <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_flesh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_for <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_from <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_ghost <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_girl <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_halloween <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_haunted <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_haunting <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_hell <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_home <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_horror <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_house <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_i <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_ii <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_in <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_is <dbl> 0, 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, 0, …
## $ tfidf_title_it <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_kill <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_killer <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_la <dbl> 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.0…
## $ tfidf_title_lake <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_last <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_legend <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_little <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_living <dbl> 0.000000, 0.000000, 2.861874, 0.000000, 0.000000…
## $ tfidf_title_love <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_man <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_massacre <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_me <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_midnight <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_monster <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_movie <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_murder <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_my <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_ni <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_night <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_nightmare <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_no <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_not <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_of <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_on <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_one <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_part <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_red <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_return <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_room <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_school <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_story <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_tales <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_terror <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_the <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_to <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_vampire <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_video <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_vs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_witch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_with <dbl> 0.000000, 0.000000, 2.552322, 0.000000, 0.000000…
## $ tfidf_title_woman <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_woods <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_world <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_you <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.00…
## $ tfidf_title_your <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_title_zombie <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ tfidf_title_zombies <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000…
## $ status_Released <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ status_other <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 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()) %>%
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(344)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)## → A | error: Some columns are non-numeric. The data cannot be converted to numeric matrix: 'original_title', 'original_language', 'overview', 'tagline', 'release_date', 'poster_path', 'adult', 'backdrop_path', 'genre_names', 'collection_name'.##
There were issues with some computations A: x1
There were issues with some computations A: x4
There were issues with some computations A: x6
There were issues with some computations A: x8
There were issues with some computations A: x10
There were issues with some computations A: x11
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There were issues with some computations A: x15
There were issues with some computations A: x16
There were issues with some computations A: x19
There were issues with some computations A: x21
There were issues with some computations A: x24
There were issues with some computations A: x26
There were issues with some computations A: x28
There were issues with some computations A: x31
There were issues with some computations A: x32
There were issues with some computations A: x35
There were issues with some computations A: x36
There were issues with some computations A: x39
There were issues with some computations A: x41
There were issues with some computations A: x44
There were issues with some computations A: x46
There were issues with some computations A: x49
There were issues with some computations A: x50## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.