Evaluating the performance of linear regression models
In the previous section, you learned how to fit a regression model on training data. However, you discovered in previous chapters that it is crucial to test the model on data that it hasn’t seen during training to obtain a more unbiased estimate of its generalization performance.
As you may remember from Chapter 6, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, we want to split our dataset into separate training and test datasets, where we will use the former to fit the model and the latter to evaluate its performance on unseen data to estimate the generalization performance. Instead of proceeding with the simple regression model, we will now use all five features in the dataset and train a multiple regression model:
>>> from sklearn.model_selection import train_test_split
>>> target = 'SalePrice'
>>> features = df.columns[df.columns != target]
>>...
