CatBoost is a depth-wise gradient boosting library developed by Yandex. It uses oblivious decision trees to grow a balanced tree. It also uses the same features to make left and right splits for each level of the tree.
- Allows for the training of data on several GPUs
- Provides great results with default parameters
- Offers improved accuracy due to reduced overfitting
- Fast prediction via CatBoost’s model applier
- Trained CatBoost models can be exported to Core ML for on-device inference
- Handle missing values internally
- Visualization of training statistics during training
- Used for regression and classification problems
I love that CatBoost provides visualizations that show training stats during the training process.
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