Confusion matrix and data imbalances

AI Engineer
Data Scientist

How do we know if a model is good or bad at classifying our data? The way that computers assess model performance sometimes can be difficult for us to comprehend or can over-simplify how the model will behave in the real world. To build models that work in a satisfactory way, we need to find intuitive ways to assess them, and understand how these metrics can bias our view.

Learning objectives

In this module, you will:

  • Assess performance of classification models.
  • Review metrics to improve classification models.
  • Mitigate performance issues from data imbalances.


Basic familiarity with classification models