RMSE is an abbreviation of root mean squared error. It is explained as the square root of MSE:
Let's evaluate the model performance on a testing dataset. In the previous section, we predicted the values for the test set. Now, we will compare the predicted values with the actual values of the test set (y_test). scikit-learn offers the metrics class for evaluating the models. For regression model evaluation, we have methods for R-squared, MSE, MAE, and RMSE. Each of the methods takes two inputs: the actual values of the test set and the predicted values (y_test and y_pred). Let's assess the performance of the linear regression model:
# Import the required libraries import numpy as np from sklearn.metrics import mean_absolute_error from sklearn...