Apply 2: Horror Movies
2024-09-19
Goal: to predict the average rating of horror movies. Click here for the data
Import Data
horror_movies <- 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.skim(horror_movies)| Name | horror_movies |
| 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_movies %>%
mutate(vote_average = log1p(vote_average)) %>%
filter(!is.na(overview), vote_count != 0) %>%
separate_rows(genre_names, sep = ", ") %>%
filter(status == "Released") %>%
select(id, vote_average, genre_names, overview, runtime, budget) %>%
na.omit()Explore Data
identify good predictors.
budget
data %>%
ggplot(aes(vote_average, budget)) +
geom_point()
runtime
data %>%
ggplot(aes(vote_average, runtime)) +
geom_point()
title
data %>%
#tokenize title
unnest_tokens(output = word, input = overview) %>%
# calculate avg rent per word
group_by(word) %>%
summarise(vote_average = mean(vote_average),
n = n()) %>%
ungroup() %>%
filter(n > 10, !str_detect(word, "\\d")) %>%
slice_max(order_by = vote_average, n = 20) %>%
# plot
ggplot(aes(vote_average, fct_reorder(word, vote_average))) +
geom_point() +
labs(y = "Words in Overview")
EDA shortcut
# Step 1: Prepare data
data_binarized_tbl <- data %>%
select(-overview, -id) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 43,444
## Columns: 23
## $ `vote_average__-Inf_1.66770682055808` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ vote_average__1.66770682055808_1.84054963339749 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ vote_average__1.84054963339749_1.97408102602201 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ vote_average__1.97408102602201_Inf <dbl> 1, 1, 1, 1, 1, 1, 1, 1…
## $ genre_names__Action <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Adventure <dbl> 0, 0, 1, 0, 0, 0, 0, 0…
## $ genre_names__Animation <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Comedy <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Crime <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Drama <dbl> 0, 0, 0, 1, 0, 0, 0, 0…
## $ genre_names__Fantasy <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Horror <dbl> 1, 0, 0, 0, 1, 1, 0, 0…
## $ genre_names__Mystery <dbl> 0, 0, 0, 0, 0, 0, 1, 0…
## $ genre_names__Science_Fiction <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names__Thriller <dbl> 0, 1, 0, 0, 0, 0, 0, 1…
## $ genre_names__TV_Movie <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `genre_names__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `runtime__-Inf_75` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ runtime__75_87 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ runtime__87_95 <dbl> 0, 0, 1, 1, 1, 0, 0, 0…
## $ runtime__95_Inf <dbl> 1, 1, 0, 0, 0, 1, 1, 1…
## $ budget__0 <dbl> 1, 1, 1, 1, 1, 0, 0, 0…
## $ `budget__-OTHER` <dbl> 0, 0, 0, 0, 0, 1, 1, 1…# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(vote_average__1.97408102602201_Inf)
data_corr_tbl## # A tibble: 23 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 vote_average 1.97408102602201_Inf 1
## 2 vote_average -Inf_1.66770682055808 -0.343
## 3 vote_average 1.84054963339749_1.97408102602201 -0.328
## 4 vote_average 1.66770682055808_1.84054963339749 -0.326
## 5 runtime -Inf_75 0.192
## 6 runtime 87_95 -0.128
## 7 runtime 75_87 -0.125
## 8 genre_names Animation 0.0769
## 9 runtime 95_Inf 0.0593
## 10 budget 0 -0.0497
## # ℹ 13 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 13 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 dataset for cross-validation
set.seed(4321)
data_cv <- rsample::vfold_cv(data_train)
data_cv## # 10-fold cross-validation
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [67/8]> Fold01
## 2 <split [67/8]> Fold02
## 3 <split [67/8]> Fold03
## 4 <split [67/8]> Fold04
## 5 <split [67/8]> Fold05
## 6 <split [68/7]> Fold06
## 7 <split [68/7]> Fold07
## 8 <split [68/7]> Fold08
## 9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10library(usemodels)## Warning: package 'usemodels' was built under R version 4.3.3usemodels::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(4796)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))xgboost_recipe <-
recipe(formula = vote_average ~ ., data = data_train) %>%
recipes::update_role(id, new_role = "id") %>%
step_tokenize(overview) %>%
step_tokenfilter(overview, max_tokens = 100) %>%
step_tfidf(overview) %>%
step_dummy(genre_names) %>%
step_YeoJohnson(runtime)
xgboost_recipe %>% prep() %>% bake(new_data = NULL) %>% glimpse()## Rows: 75
## Columns: 115
## $ id <dbl> 24499, 16456, 974191, 978651, 438309, 5428…
## $ runtime <dbl> 782.02147, 1015.49904, 811.81396, 1200.956…
## $ budget <dbl> 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+00, 0.0e+0…
## $ vote_average <dbl> 1.757858, 1.481605, 1.609438, 1.871802, 1.…
## $ tfidf_overview_a <dbl> 0.00000000, 0.11091181, 0.02184626, 0.1802…
## $ tfidf_overview_about <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_after <dbl> 0.09561937, 0.00000000, 0.00000000, 0.1593…
## $ tfidf_overview_all <dbl> 0.00000000, 0.00000000, 0.07456997, 0.0000…
## $ tfidf_overview_an <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_ancient <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_and <dbl> 0.08602013, 0.06616933, 0.05213341, 0.0716…
## $ tfidf_overview_are <dbl> 0.00000000, 0.11690967, 0.04605533, 0.0000…
## $ tfidf_overview_as <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_at <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_back <dbl> 0.0000000, 0.2002069, 0.0000000, 0.0000000…
## $ tfidf_overview_be <dbl> 0.10282260, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_become <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_been <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_begins <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_being <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_between <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_blood <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_but <dbl> 0.00000000, 0.00000000, 0.05429574, 0.0000…
## $ tfidf_overview_by <dbl> 0.06785120, 0.20877292, 0.00000000, 0.0000…
## $ tfidf_overview_cabin <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_child <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_creature <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_dead <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_down <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_end <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_evil <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_family <dbl> 0.00000000, 0.00000000, 0.07886938, 0.0000…
## $ tfidf_overview_find <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_for <dbl> 0.07790723, 0.00000000, 0.00000000, 0.1298…
## $ tfidf_overview_friend <dbl> 0.13862944, 0.00000000, 0.08401784, 0.0000…
## $ tfidf_overview_friends <dbl> 0.00000000, 0.00000000, 0.00000000, 0.1949…
## $ tfidf_overview_from <dbl> 0.00000000, 0.00000000, 0.04845018, 0.0000…
## $ tfidf_overview_ghost <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_group <dbl> 0.00000000, 0.00000000, 0.00000000, 0.2310…
## $ tfidf_overview_has <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_haunted <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_have <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_he <dbl> 0.08691354, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_help <dbl> 0.00000000, 0.00000000, 0.09039859, 0.0000…
## $ tfidf_overview_her <dbl> 0.29673371, 0.00000000, 0.04495965, 0.0000…
## $ tfidf_overview_him <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_his <dbl> 0.00000000, 0.10663803, 0.04200892, 0.0000…
## $ tfidf_overview_horror <dbl> 0.00000000, 0.00000000, 0.07886938, 0.0000…
## $ tfidf_overview_house <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_in <dbl> 0.00000000, 0.00000000, 0.06303140, 0.0000…
## $ tfidf_overview_into <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_is <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_it <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ `tfidf_overview_it's` <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_killer <dbl> 0.13013448, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_killing <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_life <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_man <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_must <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_mysterious <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_new <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_night <dbl> 0.00000000, 0.00000000, 0.00000000, 0.2050…
## $ tfidf_overview_not <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_now <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_of <dbl> 0.00000000, 0.06782603, 0.05343869, 0.1469…
## $ tfidf_overview_on <dbl> 0.00000000, 0.10900508, 0.00000000, 0.0000…
## $ tfidf_overview_one <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_only <dbl> 0.13862944, 0.00000000, 0.08401784, 0.0000…
## $ tfidf_overview_or <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_out <dbl> 0.00000000, 0.00000000, 0.07089088, 0.0000…
## $ tfidf_overview_party <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_people <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_police <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_professor <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_remote <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_samantha <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tfidf_overview_serial <dbl> 0.14915767, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_sex <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_she <dbl> 0.21400662, 0.00000000, 0.06485049, 0.0000…
## $ tfidf_overview_so <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_soon <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_take <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_that <dbl> 0.00000000, 0.00000000, 0.07894088, 0.0000…
## $ tfidf_overview_the <dbl> 0.04149531, 0.06383893, 0.12574335, 0.0000…
## $ tfidf_overview_their <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_them <dbl> 0.00000000, 0.00000000, 0.05267487, 0.0000…
## $ tfidf_overview_they <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_this <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_three <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_to <dbl> 0.08397507, 0.06459620, 0.05089398, 0.0000…
## $ tfidf_overview_town <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_two <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_up <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_what <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_when <dbl> 0.00000000, 0.11690967, 0.00000000, 0.0000…
## $ tfidf_overview_where <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ tfidf_overview_which <dbl> 0.00000000, 0.00000000, 0.00000000, 0.2168…
## $ tfidf_overview_while <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_who <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_with <dbl> 0.00000000, 0.00000000, 0.12336582, 0.0000…
## $ tfidf_overview_woman <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_women <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_world <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ tfidf_overview_young <dbl> 0.00000000, 0.00000000, 0.00000000, 0.0000…
## $ genre_names_Adventure <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Comedy <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Crime <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Drama <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Fantasy <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ genre_names_Horror <dbl> 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ genre_names_Mystery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Romance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Science.Fiction <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ genre_names_Thriller <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ genre_names_Western <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …xgboost_spec <-
boost_tree(trees = tune(), min_n = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
set.seed(1027)
xgboost_tune <-
tune_grid(xgboost_workflow, resamples = data_cv, grid = 5)## Warning: package 'xgboost' was built under R version 4.3.3## → A | warning: ! There are new levels in `genre_names`: "Western".
## ℹ Consider using step_novel() (`?recipes::step_novel()`) \ before
## `step_dummy()` to handle unseen values.##
There were issues with some computations A: x1
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→ B | warning: A correlation computation is required, but `estimate` is constant and has 0
## standard deviation, resulting in a divide by 0 error. `NA` will be returned.
## There were issues with some computations A: x5
→ C | warning: ! There are new levels in `genre_names`: "Romance".
## ℹ Consider using step_novel() (`?recipes::step_novel()`) \ before
## `step_dummy()` to handle unseen values.
## There were issues with some computations A: x5
There were issues with some computations A: x5 B: x1 C: x1
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