Apply 4
Johnny McKinnon
2024-10-03
Goal: Predict the success of movies
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(budget, popularity)) +
scale_x_log10() +
geom_point()## Warning in scale_x_log10(): log-10 transformation introduced infinite values.
Revenue
data %>%
ggplot(aes(budget, revenue)) +
geom_point()
Runtime
data %>%
ggplot(aes(budget, runtime)) +
geom_point()
Title
data %>%
# Tokenize title
unnest_tokens(output = word, input = title) %>%
# Calculate avg rating per word
group_by(word) %>%
summarise(budget = mean(budget),
n = n()) %>%
ungroup() %>%
filter(n > 5, !str_detect(word, "\\d")) %>%
slice_max(order_by = budget, n = 25) %>%
# Plot
ggplot(aes(budget, fct_reorder(word, budget))) +
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('budget__-OTHER')
data_corr_tbl## # A tibble: 44 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 budget 0 -1
## 2 budget -OTHER 1
## 3 revenue 0 -0.331
## 4 revenue -OTHER 0.331
## 5 original_language en 0.163
## 6 vote_count 11_Inf 0.161
## 7 popularity 0.807925688568666_Inf 0.160
## 8 vote_count -Inf_2 -0.0898
## 9 popularity -Inf_-0.510825623765991 -0.0852
## 10 original_language ja -0.0774
## # ℹ 34 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 23 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(budget ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = budget ~ ., 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 = budget ~ ., 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> 684433, 471920, 518695, 741187, 969412, 4…
## $ popularity <dbl> -0.7699431, -0.7699431, -0.7699431, -0.76…
## $ vote_count <dbl> 0.0000000, 0.0000000, 0.6111858, 0.902255…
## $ vote_average <dbl> 0.0, 0.0, 6.0, 6.5, 0.0, 3.9, 10.0, 5.0, …
## $ revenue <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ runtime <dbl> 36.637345, 47.797968, 3.520282, 47.797968…
## $ budget <dbl> 0.000000, 1.575996, 0.000000, 0.000000, 0…
## $ tfidf_title_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_a <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_aber <dbl> 2.165367, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_above <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_absent <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_addiction <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_aftershocks <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_alive <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_apocalypse <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_at <dbl> 0.0000000, 0.0000000, 0.7217889, 0.000000…
## $ tfidf_title_avacalho <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_bad <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_badd <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_ballad <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_ban <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_bath <dbl> 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,…
## $ tfidf_title_been <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_bender <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_bigfoot <dbl> 0.0000000, 0.0000000, 0.0000000, 0.721788…
## $ tfidf_title_billy <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_bitch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_bites <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_blackheath <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_blinder <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_blood <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_blutschrei <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2…
## $ tfidf_title_broadcast <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_by <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_call <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_camp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_carol <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_caronte <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_chapter <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_circus <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_cross <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_curtição <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_da <dbl> 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, 0…
## $ tfidf_title_death <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_der <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2…
## $ tfidf_title_devil <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_digging <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_dilim <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_discarnate <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_disputes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_do <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ `tfidf_title_don't` <dbl> 0.0000000, 0.0000000, 0.7217889, 0.000000…
## $ tfidf_title_dr <dbl> 0, 0, 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, 0, 0,…
## $ tfidf_title_earth <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_exorcists <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_experiment <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_faeries <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_fatal <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_fender <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_file <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_final <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_flavia <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_for <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_francisville <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_frank <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_frightmare <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_ghost <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_glücklich <dbl> 2.165367, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_go <dbl> 0.0000000, 0.0000000, 0.7217889, 0.000000…
## $ tfidf_title_grief <dbl> 0.000000, 2.165367, 0.000000, 0.000000, 0…
## $ tfidf_title_halloween <dbl> 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,…
## $ tfidf_title_heretic <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_high <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_himmel <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_horrible <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_house <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_i <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `tfidf_title_i've` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_iemon <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_into <dbl> 0.0000000, 0.0000000, 0.7217889, 0.000000…
## $ tfidf_title_irina <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_isle <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_it <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_jaakkirathai <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_kaiki <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_kills <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_kolobos <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_konak <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_kowasugi <dbl> 0.0000000, 0.0000000, 0.0000000, 0.000000…
## $ tfidf_title_la <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_larmes <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_last <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0…
## $ tfidf_title_legacy <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_legend <dbl> 0.000000, 0.000000, 0.000000, 0.608443, 0…
## $ tfidf_title_lilith <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_night <dbl> 0.000000, 0.000000, 0.608443, 0.000000, 0…
## $ tfidf_title_of <dbl> 0.0000000, 0.0000000, 0.0000000, 0.389899…
## $ tfidf_title_on <dbl> 0.000000, 0.000000, 0.000000, 0.608443, 0…
## $ tfidf_title_the <dbl> 0.0000000, 0.7790723, 0.2596908, 0.519381…
## $ original_language_en <dbl> 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,…
## $ original_language_es <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ original_language_ja <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ original_language_other <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,…
## $ genre_names_Comedy..Horror <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ genre_names_Horror <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,…
## $ genre_names_Horror..Thriller <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
## $ genre_names_other <dbl> 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,…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
→ B | warning: A correlation computation is required, but `truth` 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: x6
There were issues with some computations A: x6 B: x1
There were issues with some computations A: x7 B: x1
There were issues with some computations A: x8 B: x1
There were issues with some computations A: x9 B: x1
There were issues with some computations A: x9 B: x1Evaluate 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 50 527 2 0.00349 rsq standard 0.374 9 0.131 Preproces…
## 2 32 1073 15 0.0434 rsq standard 0.260 9 0.0929 Preproces…
## 3 91 1985 22 0.0183 rsq standard 0.162 9 0.0398 Preproces…
## 4 88 337 31 0.00111 rsq standard 0.122 9 0.0323 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 0.453 Preprocessor1_Model1
## 2 rsq standard 0.158 Preprocessor1_Model1tune::collect_predictions(data_fit) %>%
ggplot(aes(budget, .pred)) +
geom_point(alpha = 0.3, fill = "midnightblue") +
geom_abline(lty = 2, color = "gray50") +
coord_fixed()