As demonstrated in the previous chapters, the model makes different predictions based on the k (number of neighbors) values. When building a model, it's crucial to select the k value that yields the best performance.

You can use cross-validation to measure the model's performance. Running a loop and calculating cross-validation scores for a range of k values to select the highest is a straightforward approach. This is the most commonly used method. sklearn provides a convenient GridSearchCV class for this task:

The param_grid parameter takes a dictionary with parameter names as keys and a list of items to go through as a list. For example, to try values 1-99 for n_neighbors, you would use:

python
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The .fit(X, y) method leads the GridSearchCV object to find the best parameters from param_grid and re-train the model with the best parameters using the whole set.
You can then get the highest score using the .best_score_ attribute and predict new values using the .predict() method.