Optimize n_neighbors

Now that we have scaled data, we can try using a KNN model. To maximize performance, we should tune our model's hyperparameters. For the k-nearest neighbors algorithm, we only have one hyperparameter: n, the number of neighbors. We set this hyperparameter when we create the model with KNeighborsRegressor. The argument for the number of neighbors is n_neighbors.

We want to try a range of values that passes through the setting with the best performance. Usually we will start with 2 neighbors, and increase until our scoring metric starts to decrease. We'll use the R\(^2\) value from the .score() method on the test set (scaled_test_features and test_targets) to optimize n here. We'll use the test set scores to determine the best n.

Loop through values of 2 to 12 for n and set this as n_neighbors in the knn model.
Fit the model to the training data (scaled_train_features and train_targets).
Print out the R\(^2\) values using the .score() method of the knn model for the train and test sets, and take note of the best score on the test set.

Exercise

Optimize n_neighbors

Instructions