catboost models

Function	Works
`tidypredict_fit()`, `tidypredict_sql()`, `parse_model()`
`tidypredict_to_column()`
`tidypredict_test()`
`tidypredict_interval()`, `tidypredict_sql_interval()`
`parsnip`

`tidypredict_` functions

library(catboost)
# Prepare data
X <- data.matrix(mtcars[, c("mpg", "cyl", "disp")])
y <- mtcars$hp

pool <- catboost.load_pool(
  X,
  label = y,
  feature_names = as.list(c("mpg", "cyl", "disp"))
)

model <- catboost.train(
  pool,
  params = list(
    iterations = 10L,
    depth = 3L,
    learning_rate = 0.5,
    loss_function = "RMSE",
    logging_level = "Silent",
    allow_writing_files = FALSE
  )
)

Create the R formula r tidypredict_fit(model)
Add the prediction to the original table ```r library(dplyr)

mtcars %>% tidypredict_to_column(model) %>% glimpse() ```

Confirm that tidypredict results match to the model’s predict() results. The xg_df argument expects the matrix data set. r tidypredict_test(model, xg_df = X)

Supported objectives

CatBoost supports many objective functions. The following objectives are supported by tidypredict:

Regression objectives (identity transform)

RMSE (default)
MAE
Quantile
MAPE
Poisson

Binary classification (sigmoid transform)

Logloss
CrossEntropy

Multiclass classification

MultiClass (softmax transform)
MultiClassOneVsAll (sigmoid per class)

Binary classification example

X_bin <- data.matrix(mtcars[, c("mpg", "cyl", "disp")])
y_bin <- mtcars$am

pool_bin <- catboost.load_pool(
  X_bin,
  label = y_bin,
  feature_names = as.list(c("mpg", "cyl", "disp"))
)

model_bin <- catboost.train(
  pool_bin,
  params = list(
    iterations = 10L,
    depth = 3L,
    learning_rate = 0.5,
    loss_function = "Logloss",
    logging_level = "Silent",
    allow_writing_files = FALSE
  )
)

tidypredict_test(model_bin, xg_df = X_bin)

Multiclass classification example

X_multi <- data.matrix(iris[, 1:4])
y_multi <- as.integer(iris$Species) - 1L

pool_multi <- catboost.load_pool(
  X_multi,
  label = y_multi,
  feature_names = as.list(colnames(iris)[1:4])
)

model_multi <- catboost.train(
  pool_multi,
  params = list(
    iterations = 10L,
    depth = 3L,
    learning_rate = 0.5,
    loss_function = "MultiClass",
    logging_level = "Silent",
    allow_writing_files = FALSE
  )
)

# Multiclass returns a list of formulas, one per class
formulas <- tidypredict_fit(model_multi)
names(formulas)

Test multiclass predictions:

tidypredict_test(model_multi, xg_df = X_multi)

Categorical features

CatBoost models can use categorical features with one-hot encoding.

With parsnip/bonsai (recommended)

When using parsnip/bonsai, categorical features are handled automatically:

library(parsnip)
library(bonsai)

df_cat <- data.frame(
  num_feat = mtcars$mpg,
  cat_feat = factor(ifelse(mtcars$am == 1, "manual", "auto")),
  target = mtcars$hp
)

model_spec <- boost_tree(trees = 10, tree_depth = 3) |>
  set_engine("catboost", logging_level = "Silent", one_hot_max_size = 10) |>
  set_mode("regression")

model_fit <- fit(model_spec, target ~ num_feat + cat_feat, data = df_cat)

# Categorical features are handled automatically
tidypredict_fit(model_fit)

With raw CatBoost

For raw CatBoost models, you need to manually establish the hash-to-category mapping:

pool_cat <- catboost.load_pool(
  df_cat[, c("num_feat", "cat_feat")],
  label = df_cat$target
)

model_cat <- catboost.train(
  pool_cat,
  params = list(
    iterations = 10L,
    depth = 3L,
    learning_rate = 0.5,
    loss_function = "RMSE",
    logging_level = "Silent",
    allow_writing_files = FALSE,
    one_hot_max_size = 10
  )
)

# Parse and set category mapping manually
pm_cat <- parse_model(model_cat)
pm_cat <- set_catboost_categories(pm_cat, model_cat, df_cat)

# Now use the parsed model
tidypredict_fit(pm_cat)

Parse model spec

Here is an example of the model spec:

pm <- parse_model(model)
str(pm, 2)

str(pm$trees[1])