Exercise - Train and evaluate a clustering model

Clustering

In contrast to supervised machine learning, unsupervised learning is used when there is no "ground truth" from which to train and validate label predictions. The most common form of unsupervised learning is clustering, which is similar conceptually to classification, except that the training data does not include known values for the class label to be predicted.

Clustering works by separating the training cases based on similarities that can be determined from their feature values. In this exercise, you will train and evaluate a clustering model.

This exercise requires the Microsoft Learn sandbox to complete. You can access the exercise from the link below.

Exercise Link

Exercise - Train and evaluate a clustering model