Machine Learning Hero

5.4 Evaluation Techniques for Unsupervised Learning

Evaluating unsupervised learning models presents unique challenges due to the absence of predefined labels for comparison. Unlike supervised learning, where we can directly measure the model's performance against known outcomes, unsupervised learning requires more nuanced approaches to assess model quality. This section delves into a variety of evaluation techniques specifically designed for unsupervised learning scenarios.

We will explore methods to gauge the effectiveness of clustering algorithms, which aim to group similar data points together without prior knowledge of the correct groupings. Additionally, we'll examine strategies for evaluating dimensionality reduction techniques, which ...