Sklearn Decision Tree Regressor - Training a Decision Tree Regression Model
Python
In this code example a Decision Tree Regressor model is trained using Sklearn. A dictionary is also created containing a feature importance value for each feature and then the performance of the model is evaluated using test data.
1| from sklearn.tree import DecisionTreeRegressor 2| from sklearn.metrics import mean_squared_error, mean_absolute_error, max_error, explained_variance_score, mean_absolute_percentage_error 3| 4| # initialise & fit Decision Tree Regressor 5| model = DecisionTreeRegressor(criterion='squared_error', 6| max_depth=None, 7| min_samples_split=2, 8| min_samples_leaf=1, 9| random_state=101) 10| model.fit(X_train, y_train) 11| 12| # create dictionary that contains feature importance 13| feature_importance= dict(zip(X_train.columns, model.feature_importances_)) 14| print('Feature Importance',feature_importance) 15| 16| # make prediction for test data & evaluate performance 17| y_pred = model.predict(X_test) 18| print('RMSE:',mean_squared_error(y_test, y_pred, squared = False)) 19| print('MAE:',mean_absolute_error(y_test, y_pred)) 20| print('MAPE:',mean_absolute_percentage_error(y_test, y_pred)) 21| print('Max Error:',max_error(y_test, y_pred)) 22| print('Explained Variance Score:',explained_variance_score(y_test, y_pred))
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