As discussed in previous chapters, evaluating a model is a critical part of the overall model-building process. A poorly trained model will only provide inaccurate predictions. Fortunately, ML.NET provides many popular attributes to calculate model accuracy based on a test set at the time of training to give you an idea of how well your model will perform in a production environment.
In ML.NET, as noted in the example application, there are three properties that comprise the ClusteringMetrics class object. Let's dive into the properties exposed in the ClusteringMetrics object:
- Average distance
- The Davies-Bouldin index
- Normalized mutual information
In the next sections, we will break down how these values are calculated and the ideal values to look for.
Average distance
Also referred to as the average score is the distance from the center of a cluster to the test data. The value, of type double, will decrease as the number of clusters increases, effectively...
