Overfitting and underfitting

Interpreting model complexity is a great way to evaluate performance when utilizing supervised learning. Your aim is to produce a model that can interpret the relationship between features and the target variable, as well as generalize well when exposed to new observations.

You will generate accuracy scores for the training and test sets using a KNN classifier with different n_neighbor values, which you will plot in the next exercise.

The training and test sets have been created from the churn_df dataset and preloaded as X_train, X_test, y_train, and y_test.

In addition, KNeighborsClassifier has been imported for you along with numpy as np.

Create neighbors as a numpy array of values from 1 up to and including 12.
Instantiate a KNN classifier, with the number of neighbors equal to the neighbor iterator.
Fit the model to the training data.
Calculate accuracy scores for the training set and test set separately using the .score() method, and assign the results to the index of the train_accuracies and test_accuracies dictionaries, respectively.

Exercise

Overfitting and underfitting

Instructions