Learn CATBOOST with Real Code Examples

Train a classifier: clf = CatBoostClassifier(); clf.fit(X_train, y_train, cat_features=cat_features)

Predict: y_pred = clf.predict(X_test)

Evaluate: accuracy_score(y_test, y_pred)

Feature importance: clf.get_feature_importance()

Custom loss function: define function and pass to CatBoost model

Ensure categorical features are correctly marked

Check dataset format and Pool creation

Handle missing values appropriately

Tune learning_rate, depth, and iterations to prevent overfitting

Enable verbose to debug training issues

Check training/validation split

Monitor overfitting via early stopping

Validate predictions on test dataset

Profile training time and memory usage

Check feature importance and model stability

Local scripts and batch predictions

Model serving via Flask/FastAPI

Integration in cloud ML pipelines

Save/load models with CatBoost.save_model()

Export to ONNX/CoreML for platform-independent deployment

scikit-learn for pipelines

NumPy and Pandas for data handling

Matplotlib/Seaborn for visualization

Optuna or Hyperopt for hyperparameter optimization

Dask for distributed computation

CatBoostClassifier/Regressor with scikit-learn pipelines

Integration with pandas DataFrame

Hyperparameter tuning with Optuna or GridSearchCV

Distributed learning with Dask

Export models as .cbm, ONNX, or CoreML

Use CatBoostClassifier/CatBoostRegressor for fast prototyping

Enable early stopping to prevent overfitting

Batch large datasets efficiently

Use GPU for speed on big datasets

Tune depth, learning_rate, and iterations carefully

Handle large-scale datasets efficiently

Tune hyperparameters for optimal performance

Implement ranking objectives

Reduce overfitting on categorical-heavy datasets

Integrate with production ML pipelines