Choosing the best model

In this exercise, you'll compare different classifiers and choose the one that performs the best.

The dataset here - already loaded and split into train and test sets - consists of Pokémon - their stats, types, and whether or not they're legendary. The objective of our classifiers is to predict this 'Legendary' variable.

Three individual classifiers have been fitted to the training set:

clf_lr is a logistic regression.
clf_dt is a decision tree.
clf_knn is a 5-nearest neighbors classifier.

As the classes here are imbalanced - only 65 of the 800 Pokémon in the dataset are legendary - we'll use F1-Score to evaluate the performance. Scikit-learn's f1_score() has been imported for you.

Predict the labels of X_test using each of the classifiers - clf_lr, clf_dt, and clf_knn.

Take Hint (-10 XP)

Exercise

Choosing the best model

Instructions 1/3