CatBoost – ML

CatBoost is a machine learning library developed by Yandex that provides state-of-the-art performance in solving classification and regression problems in the field of machine learning.

Features

• High speed of training;
• Support for both CPU and GPU;
• Built-in class balancing mechanism;
• Effective handling of categorical features;
• Built-in cross-validation;
• Tolerant to missing values.

Installation

To install CatBoost, you can use pip:

pip install catboost

Usage

To use CatBoost, first import the library:

import catboost

To train a classification model, you can use the following code:

cat_features_idx = [0, 1, 2]  # The list of categorical features indexes
train_data = catboost.Pool(data=X_train, label=y_train, cat_features=cat_features_idx)
test_data = catboost.Pool(data=X_test, label=y_test, cat_features=cat_features_idx)

model = catboost.CatBoostClassifier(iterations=100, learning_rate=0.1, depth=6, loss_function='MultiClass')
model.fit(train_data)

predictions = model.predict(test_data)

To train a regression model:

train_data = catboost.Pool(data=X_train, label=y_train)
test_data = catboost.Pool(data=X_test, label=y_test)

model = catboost.CatBoostRegressor(iterations=100, learning_rate=0.1, depth=6)
model.fit(train_data)

predictions = model.predict(test_data)

Conclusion

CatBoost is a powerful tool to use in machine learning. Its speed and efficiency in handling categorical features make it an attractive solution for solving classification and regression problems.