Evaluating the K-Means clustering model

In this section, we'll learn how to evaluate the performance of our K-Means clustering model.

The evaluation stage of a K-Means clustering model is different from the supervised machine learning models that we've performed in the previous chapters. Let's take a look at the steps we need to take to evaluate our machine learning model, as follows:

1. Let's extract the centroids from the first machine learning model that we trained in the previous section, by running the following code:

   SELECT *
   FROM ML.CENTROIDS
           (MODEL `07_chicago_taxi_drivers.clustering_by_speed`)
   ORDER BY centroid_id;

   The ML.CENTROIDS function returns information about the centroids of the K-Means model. It accepts the model name as input in the round brackets, preceded by the MODEL keyword.

   Important note

   A centroid represents the center of a cluster in a K-Means clustering model. During the training phase...