Confusion matrix and data imbalances

Module
9 Units

Beginner

AI Engineer

Data Scientist

Student

Azure

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 behaves in the real world. To build models that work satisfactorily, 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.

Prerequisites

Basic familiarity with classification models

Introduction min
Confusion matrices min
Exercise – Building a confusion matrix min
Data imbalances min
Exercise - Resolving biases in a classification model min
Cost functions versus evaluation metrics min
Exercise - Multiple metrics and ROC curves min
Module assessment min
Summary min