Learn LIGHTGBM with Real Code Examples

Train a classifier: clf = lgb.LGBMClassifier(); clf.fit(X_train, y_train)

Predict: y_pred = clf.predict(X_test)

Evaluate: accuracy_score(y_test, y_pred)

Feature importance: clf.feature_importances_

Custom objective function: define function and pass to lgb.train

Ensure categorical features are correctly marked

Check dataset format and shape

Handle missing values appropriately

Tune learning_rate, num_leaves, and max_depth 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 lgb.Booster or pickle

Export to ONNX or PMML for platform-independent deployment

scikit-learn for ML pipelines

NumPy and Pandas for data handling

Matplotlib/Seaborn for visualization

Optuna or Hyperopt for hyperparameter optimization

Dask/XGBoost for distributed computation

LGBMClassifier/LGBMRegressor with scikit-learn pipelines

Integration with pandas DataFrame

Use with Optuna for hyperparameter tuning

Distributed learning with Dask or MPI

Export models as .txt or .pkl for deployment

Use LGBMClassifier/LGBMRegressor for fast prototyping

Enable early stopping to prevent overfitting

Batch large datasets efficiently

Use GPU for speed on big datasets

Tune num_leaves, learning_rate, and max_depth carefully

Prevent overfitting on small datasets

Handle large-scale datasets efficiently

Tune hyperparameters for optimal performance

Implement ranking objectives

Integrate with production ML pipelines