Earlier in the chapter, we explored choosing the best of a few nearest neighbors instances based on the number of neighbors, n_neighbors, parameter. This is the main parameter in nearest neighbors classification: classify a point based on the label of KNN. So, for 3-nearest neighbors, classify a point based on the label of the three nearest points. Take a majority vote of the three nearest points.
The classification metric in this case was the internal metric accuracy_score, which is defined as the number of classifications that were correct divided by the total number of classifications. There are alternate metrics, and we will explore them here.
