lightgbm.py

lgbm_tuned = LGBMClassifier(boosting_type = 'gbdt',
                            class_weight = None,
                            min_child_samples = 20,
                            num_leaves = 30,
                            subsample_for_bin = 20000,
                            learning_rate=0.01, 
                            max_depth=10, 
                            n_estimators=40, 
                            colsample_bytree=0.6) # LightGBM Classifier with optimum paramteres
lgbm_tuned.fit(X_train, y_train)

y_test_pred = lgbm_tuned.predict(X_test) #Predicting X_test to find the solutiın
score = round(accuracy_score(y_test, y_test_pred), 3) # Find the accuracy of y_test and predicitons, and round the result
print(score)