In this chapter, we reviewed various techniques we can employ to improve the performance of our models. We learned how to use grid search to find the best hyperparameters in a search space, and how to tune our model using the scoring metric of our choosing with GridSearchCV. This means we don't have to accept the default in the score() method of our model and can customize it to our needs.
In our discussion of feature engineering, we learned how to reduce the dimensionality of our data using techniques such as PCA and feature selection. We saw how to use PolynomialFeatures to add interaction terms to models with categorical and numerical features. Next, we learned how to use a FeatureUnion to augment our training data with transformed features. In addition, we saw how decision trees can help us understand which features in the data contribute most to the classification...