LGBMClassifier - Training a Binary Classification Model With LightGBM
Python
This code snippet initializes and fits a binary classification model using the LGBMClassifier.
The code saves the trained model and plots the feature importances. Finally it makes predictions on the test set and evaluates performance using the classification report, log loss, and ROC AUC score.
1| import lightgbm as lgb 2| import matplotlib.pyplot as plt 3| from sklearn.metrics import classification_report, log_loss, roc_auc_score 4| 5| # Step 1: Initialise and fit LightGBM binary classification model 6| model = lgb.LGBMClassifier( 7| num_iterations=1000, 8| max_depth=4, 9| learning_rate=0.1, 10| reg_lambda=1, 11| random_state=101, 12| n_jobs=-1 13| ) 14| model.fit( 15| X_train, y_train, 16| eval_set=(X_test, y_test), 17| verbose=False 18| ) 19| 20| model.booster_.save_model('lightgbm_classification.model') 21| 22| # Step 2: Plot feature importances 23| features = X_train.columns 24| importance_values = model.feature_importances_ 25| 26| plt.barh(y=range(len(features)), 27| width=importance_values, 28| tick_label=features) 29| plt.show() 30| 31| # Step 3: Make predictions for test data & evaluate performance 32| y_pred = model.predict(X_test) 33| print('Classification Report:', classification_report(y_test, y_pred)) 34| print('Log Loss:', log_loss(y_test, y_pred)) 35| print('ROC AUC:', roc_auc_score(y_test, y_pred))
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