Apply 3
Johnny McKinnon
2024-09-26
Goal: Predict the average movie rating
Import data
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_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 %>%
# Treat missing values
select(-tagline, -backdrop_path, -collection_name, -collection, -release_date, -original_title, -overview, -poster_path) %>%
na.omit() %>%
# Log transform variables with pos-skewed distribution
mutate(popularity = log(popularity))Explore data
Identify good predictors
Popularity
data %>%
ggplot(aes(vote_average, popularity)) +
scale_x_log10() +
geom_point()## Warning in scale_x_log10(): log-10 transformation introduced infinite values.
Revenue
data %>%
ggplot(aes(vote_average, revenue)) +
geom_point()
Runtime
data %>%
ggplot(aes(vote_average, runtime)) +
geom_point()
Title
data %>%
# Tokenize title
unnest_tokens(output = word, input = title) %>%
# Calculate avg rating per word
group_by(word) %>%
summarise(vote_average = mean(vote_average),
n = n()) %>%
ungroup() %>%
filter(n > 5, !str_detect(word, "\\d")) %>%
slice_max(order_by = vote_average, n = 25) %>%
# Plot
ggplot(aes(vote_average, fct_reorder(word, vote_average))) +
geom_point() +
labs(y = "Words in Title")
EDA Shortcut
# Step 1: Prepare data
data_binarized_tbl <- data %>%
select(-id, -title) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 32,540
## Columns: 44
## $ original_language__cn <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__de <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__en <dbl> 1, 1, 1, 1, 0, 0, …
## $ original_language__es <dbl> 0, 0, 0, 0, 1, 1, …
## $ original_language__fr <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__id <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__it <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__ja <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__ko <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__pt <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__th <dbl> 0, 0, 0, 0, 0, 0, …
## $ original_language__zh <dbl> 0, 0, 0, 0, 0, 0, …
## $ `original_language__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `popularity__-Inf_-0.510825623765991` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `popularity__-0.510825623765991_-0.174353387144778` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `popularity__-0.174353387144778_0.807925688568666` <dbl> 0, 0, 0, 0, 0, 0, …
## $ popularity__0.807925688568666_Inf <dbl> 1, 1, 1, 1, 1, 1, …
## $ `vote_count__-Inf_2` <dbl> 0, 0, 0, 0, 0, 1, …
## $ vote_count__2_11 <dbl> 0, 0, 0, 0, 0, 0, …
## $ vote_count__11_Inf <dbl> 1, 1, 1, 1, 1, 0, …
## $ `vote_average__-Inf_4` <dbl> 0, 0, 0, 0, 0, 1, …
## $ vote_average__4_5.7 <dbl> 0, 0, 0, 0, 0, 0, …
## $ vote_average__5.7_Inf <dbl> 1, 1, 1, 1, 1, 0, …
## $ budget__0 <dbl> 1, 1, 0, 0, 1, 1, …
## $ `budget__-OTHER` <dbl> 0, 0, 1, 1, 0, 0, …
## $ revenue__0 <dbl> 0, 0, 0, 0, 1, 1, …
## $ `revenue__-OTHER` <dbl> 1, 1, 1, 1, 0, 0, …
## $ `runtime__-Inf_14` <dbl> 0, 0, 0, 0, 1, 1, …
## $ runtime__14_80 <dbl> 0, 0, 0, 0, 0, 0, …
## $ runtime__80_91 <dbl> 0, 0, 0, 0, 0, 0, …
## $ runtime__91_Inf <dbl> 1, 1, 1, 1, 0, 0, …
## $ status__Released <dbl> 1, 1, 1, 1, 1, 1, …
## $ `status__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Animation,_Horror` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Comedy,_Horror` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Drama,_Horror` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Drama,_Horror,_Thriller` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Fantasy,_Horror` <dbl> 0, 0, 0, 0, 0, 0, …
## $ genre_names__Horror <dbl> 0, 0, 0, 0, 1, 0, …
## $ `genre_names__Horror,_Mystery` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Horror,_Mystery,_Thriller` <dbl> 0, 0, 1, 0, 0, 0, …
## $ `genre_names__Horror,_Science_Fiction` <dbl> 0, 0, 0, 0, 0, 0, …
## $ `genre_names__Horror,_Thriller` <dbl> 1, 0, 0, 1, 0, 1, …
## $ `genre_names__-OTHER` <dbl> 0, 1, 0, 0, 0, 0, …# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(popularity__0.807925688568666_Inf)
data_corr_tbl## # A tibble: 44 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 popularity 0.807925688568666_Inf 1
## 2 vote_count 11_Inf 0.775
## 3 vote_count -Inf_2 -0.588
## 4 popularity -Inf_-0.510825623765991 -0.494
## 5 vote_average -Inf_4 -0.416
## 6 popularity -0.174353387144778_0.807925688568666 -0.328
## 7 vote_average 4_5.7 0.314
## 8 revenue 0 -0.303
## 9 revenue -OTHER 0.303
## 10 runtime -Inf_14 -0.297
## # ℹ 34 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build models
Split Data
data <- sample_n(data, 100)
# Split into train and test data-set
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(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)
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(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) %>%
step_zv(all_predictors()) %>%
update_role(id, new_role = "id variable") %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tfidf(title) %>%
step_other(original_language, genre_names) %>%
step_dummy(original_language, genre_names, one_hot = TRUE) %>%
step_YeoJohnson(popularity, vote_count, budget, revenue, runtime)
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 75
## Columns: 115
## $ id <dbl> 526052, 81384, 13094, 876269, 5328…
## $ popularity <dbl> 0.7677197, 0.3245809, 0.7198756, -…
## $ vote_count <dbl> 1.7202966, 1.1047995, 1.8427917, 0…
## $ budget <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ revenue <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ runtime <dbl> 23.134628, 22.988422, 21.490318, 2…
## $ vote_average <dbl> 5.5, 4.6, 5.9, 10.0, 0.0, 4.3, 1.0…
## $ tfidf_title_068 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_1984 <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_50 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_a <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_agency <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_alive <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_alone <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_american <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_amerikan <dbl> 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…
## $ tfidf_title_apartment <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_apparition <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_assassin <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_atta <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_attack <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_auopssessed <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_aylesbury <dbl> 0.000000, 0.000000, 0.000000, 2.16…
## $ tfidf_title_battle <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_becoming <dbl> 4.330733, 0.000000, 0.000000, 0.00…
## $ tfidf_title_bitch <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_black <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_blues <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_blurred <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_bone <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_boo <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_breathe <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_capps <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_cat <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_cobre <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_cookie <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_cried <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_crossing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_cut <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_dale <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_dance <dbl> 0.000000, 0.000000, 1.082683, 0.00…
## $ tfidf_title_dangerous <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_dans <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_darkness <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_day <dbl> 0.0000000, 0.0000000, 0.0000000, 0…
## $ tfidf_title_de <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_dead <dbl> 0.0000000, 0.0000000, 0.8145241, 1…
## $ tfidf_title_death <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_dementia <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_demons <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ `tfidf_title_devil's` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_doctor <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ `tfidf_title_don't` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_du <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_eizou <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_eta <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_evil <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_experimenting <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_expira <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_express <dbl> 0.000000, 2.165367, 0.000000, 0.00…
## $ tfidf_title_eyes <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_face <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_familiar <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_fantasies <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_first <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_foot <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_forbidden <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_game <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_ghost <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_ghosting <dbl> 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…
## $ tfidf_title_glass <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_gojusan <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_gotas <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_heart <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_holocaust <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_hontou <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_howling <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_in <dbl> 0.0000000, 0.0000000, 0.0000000, 0…
## $ tfidf_title_is <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_joaquim <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_kalong <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_kill <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_killer <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_kore <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_la <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_lost <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_love <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_lurking <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_mala <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_man <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_mari <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_market <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_mask <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_memo <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_monk <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_monster <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_moon <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_movie <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_new <dbl> 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_of <dbl> 0.0000000, 0.0000000, 0.5848498, 0…
## $ tfidf_title_sex <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_title_the <dbl> 0.0000000, 0.6785120, 0.3392560, 0…
## $ tfidf_title_vampire <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language_en <dbl> 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1…
## $ original_language_ja <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language_other <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0…
## $ genre_names_Comedy..Horror <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1…
## $ genre_names_Drama..Horror..Thriller <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ genre_names_Horror <dbl> 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0…
## $ genre_names_Horror..Thriller <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ genre_names_other <dbl> 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune(), learn_rate = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
set.seed(4796)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)## i Creating pre-processing data to finalize unknown parameter: mtry## Warning: package 'xgboost' was built under R version 4.3.3## → A | 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: x1
There were issues with some computations A: x2
There were issues with some computations A: x3
There were issues with some computations A: x4
There were issues with some computations A: x5
There were issues with some computations A: x6
There were issues with some computations A: x7
There were issues with some computations A: x8
There were issues with some computations A: x9
There were issues with some computations A: x10
There were issues with some computations A: x10Evaluate Models
tune::show_best(xgboost_tune, metric = "rsq")## # A tibble: 4 × 10
## mtry trees min_n learn_rate .metric .estimator mean n std_err .config
## <int> <int> <int> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 91 1985 22 0.0183 rsq standard 0.672 10 0.0722 Preproces…
## 2 50 527 2 0.00349 rsq standard 0.663 10 0.0736 Preproces…
## 3 32 1073 15 0.0434 rsq standard 0.611 10 0.0716 Preproces…
## 4 88 337 31 0.00111 rsq standard 0.152 10 0.0512 Preproces…# Update the model by selecting the best hyperparameters
xgboost_fw <- tune::finalize_workflow(xgboost_workflow,
tune::select_best(xgboost_tune, metric = "rmse"))
# Fit the model on the entire training data and test it on the test data
data_fit <- tune::last_fit(xgboost_fw, data_split)tune::collect_metrics(data_fit)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 1.42 Preprocessor1_Model1
## 2 rsq standard 0.728 Preprocessor1_Model1tune::collect_predictions(data_fit) %>%
ggplot(aes(vote_average, .pred)) +
geom_point(alpha = 0.3, fill = "midnightblue") +
geom_abline(lty = 2, color = "gray50") +
coord_fixed()