CatBoostRegressor - Training a Regression Model With CatBoost
Python
This code snippets demonstrates how to use CatBoost for regression, how to modify its hyperparameters, how to store the trained model, how to visualize feature importance, and how to evaluate the performance of the model using various metrics
1| from catboost import CatBoostRegressor 2| from sklearn.metrics import mean_squared_error, mean_absolute_error, max_error, explained_variance_score, mean_absolute_percentage_error 3| import matplotlib.pyplot as plt 4| 5| # Step 1: Initialise and fit CatBoost regression model 6| model = CatBoostRegressor(loss_function='RMSE', 7| n_estimators=1000, 8| max_depth=4, 9| learning_rate=0.1, 10| min_child_samples=1, 11| colsample_bylevel=0.9, 12| subsample=0.9, 13| random_seed=101) 14| model.fit(X_train, y_train) 15| 16| model.save_model('catboost_regressor.model') 17| 18| # Step 2: Plot feature importances 19| features = X_train.columns 20| importance_values = model.feature_importances_ 21| 22| plt.barh(y=range(len(features)), 23| width=importance_values, 24| tick_label=features) 25| plt.show() 26| 27| # Step 3: Make prediction for test data & evaluate performance 28| y_pred = model.predict(X_test) 29| print('RMSE:',mean_squared_error(y_test, y_pred, squared=False)) 30| print('MAE:',mean_absolute_error(y_test, y_pred)) 31| print('MAPE:',mean_absolute_percentage_error(y_test, y_pred)) 32| print('Max Error:',max_error(y_test, y_pred)) 33| print('Explained Variance Score:',explained_variance_score(y_test, y_pred))
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