CatBoost | DataScienceBase

CatBoost

Explore CatBoost, a powerful gradient boosting library that excels with categorical features. Developed by Yandex, it automatically handles categorical variables without the need for extensive preprocessing. Learn how CatBoost can improve model accuracy while being robust against overfitting, making it ideal for various applications, from recommendation systems to finance.

CatBoost Theory

Understand CatBoost's ordered categorical statistics, ordered boosting, symmetric trees, missing values, and objectives.

scikit-learn Example

Train CatBoost 1.2.10 on mixed numerical and categorical data with a separate stopping set and probability evaluation.

TensorFlow Guidance

Keep CatBoost and TensorFlow training boundaries explicit in mixed-model pipelines.

PyTorch Guidance

Combine PyTorch representations with CatBoost only through explicit, leakage-safe interfaces.

Common Mistakes & Best Practices

Avoid categorical leakage, validation misuse, schema errors, and misleading CatBoost evaluation.

Comparison with Other Algorithms

Compare CatBoost with other boosters and supervised baselines without categorical leakage or benchmark bias.