Measure model fit
Now you will measure the regression performance on both training and testing data with two metrics - root mean squared error and mean absolute error. This is a critical step where you are measuring how "close" are the model predictions compared to actual values.
The numpy library has been loaded as np. The mean_absolute_error and mean_squared_error functions have been loaded. The training and testing target variables are loaded as train_Y and test_Y, and the predicted training and testing values are imported as train_pred_Y and test_pred_Y respectively.
This exercise is part of the course
Machine Learning for Marketing in Python
Exercise instructions
- Calculate the root mean squared error on the training data by using the
np.sqrt()function. - Calculate the mean absolute error on the training data.
- Calculate the root mean squared error on the testing data.
- Calculate the mean absolute error on the testing data.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate root mean squared error on training data
rmse_train = np.sqrt(___(train_Y, train_pred_Y))
# Calculate mean absolute error on training data
mae_train = ___(train_Y, train_pred_Y)
# Calculate root mean squared error on testing data
rmse_test = np.sqrt(___(test_Y, test_pred_Y))
# Calculate mean absolute error on testing data
mae_test = ___(test_Y, test_pred_Y)
# Print the performance metrics
print('RMSE train: {}; RMSE test: {}\nMAE train: {}, MAE test: {}'.format(rmse_train, rmse_test, mae_train, mae_test))