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

Explore the theoretical underpinnings of CatBoost, focusing on its handling of categorical features and boosting.

scikit-learn Example

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TensorFlow Example

Implement CatBoost using TensorFlow for effective handling of categorical data in machine learning.

PyTorch Example

Learn how to integrate CatBoost with PyTorch to enhance model training and accuracy.

Common Mistakes & Best Practices

Avoid common mistakes in CatBoost and learn best practices to maximize its performance and accuracy.

Comparison with Other Algorithms

Compare CatBoost with other machine learning algorithms to understand its unique advantages, especially with categorical data.