Contents
Overview of CatBoost
Installation
Python package
R package
Command-line version
Applying models
Objectives and metrics
Model analysis
Data format description
Parameter tuning
Speeding up the training
Data visualization
FAQ
Educational materials
Development and contributions
Algorithm details
Contacts
How training is performed
Quantization
Overfitting detector
Recovering training after an interruption
Missing values processing
Score functions
How training is performed
Preliminary calculation of splits
Transforming categorical features to numerical features
Transforming text features to numerical features
Choosing the tree structure
Bootstrap options
Unbiased boosting