Learn CATBOOST with Real Code Examples

CatBoost handles categorical features natively without the need for extensive preprocessing.

It implements ordered boosting to reduce overfitting and improve generalization.

CatBoost integrates with Python, R, and other ML pipelines for seamless usage in real-world workflows.

Gradient boosting on decision trees

Ordered and symmetric tree boosting

Automatic handling of categorical features

Support for custom loss functions

Python, R, and CLI interfaces

Dataset: tabular data with categorical and numerical features

Pool: core data structure for CatBoost

Ordered boosting: reduces prediction shift

Objective function: learning goal (classification, regression, ranking)

Hyperparameters: control tree depth, learning rate, iterations, etc.

main.py / notebook.ipynb - training and evaluation scripts

data/ - raw and preprocessed datasets

models/ - saved CatBoost model files

utils/ - feature engineering and helper functions

notebooks/ - experiments and parameter tuning

Prepare data: train/test split, identify categorical features

Create Pool objects for CatBoost

Define parameters for training

Train using CatBoostClassifier/CatBoostRegressor

Evaluate performance and tune hyperparameters

Beginner: train simple classifier/regressor

Intermediate: handle categorical data and cross-validation

Advanced: ranking tasks and GPU training

Expert: custom loss functions and large-scale optimization

Enterprise: production deployment and monitoring

CatBoost vs LightGBM: better for categorical-heavy datasets

CatBoost vs XGBoost: less overfitting due to ordered boosting

CatBoost vs RandomForest: gradient boosting vs bagging

CatBoost vs scikit-learn GBM: more automated handling of categorical features

CatBoost vs TensorFlow/PyTorch: tabular ML vs deep learning

2017 – CatBoost released by Yandex

2018 – GPU training support added

2019 – Symmetric tree and model interpretation tools introduced

2021 – Enhanced performance for large-scale datasets

2025 – CatBoost 1.x with improved GPU optimization and ONNX export

Ordered boosting: sequential training to reduce overfitting

Symmetric tree: all leaves at a given depth are split simultaneously

Pool: core data structure for CatBoost

Categorical feature handling: automatic encoding internally

Objective function: learning target (regression/classification)