Learn CATBOOST with Real Code Examples

Use GPU for large datasets with many features

Tune depth and learning_rate to balance speed and accuracy

Ordered boosting reduces overfitting on small datasets

Use early_stopping_rounds during cross-validation

Profile training time and memory usage

Validate and sanitize input data

Secure saved models with proper file permissions

Avoid exposing sensitive predictions without anonymization

Log anonymized features only

Ensure reproducible results via fixed seeds and proper dependencies

Track training and validation metrics

Monitor overfitting and early stopping

Log feature importance and predictions

Compare multiple models and parameters

Visualize metrics with plots or dashboards

Write modular training and evaluation scripts

Document hyperparameter choices

Version control models and code

Unit test feature preprocessing

Ensure reproducibility with fixed seeds