Evaluation

Evaluation is crucial in machine learning to assess the reliability and effectiveness of prediction models. We prioritize diverse, robust, and transparent evaluation techniques. This encompasses a range of strategies addressing the unique challenges presented by different machine learning subfields, such as clustering and positive-unlabeled learning.

The following sections provide more details for each relevant machine learning subfield:

• Evaluating Clustering
• Evaluating PU Learning