Performance Measures for Classification Models

And methods to evaluate the performance of a classifier

Photo by Franck V. on Unsplash

If you are building any Machine Learning model, be it on dummy dataset or real world problem, the most important part is to determine how well your models works. This is determined usually with a combination of two approaches.

→ Using a performance metric and usage of methods that take the performance metric and provide empirical performance data.

Basics of Supervised Learning (Classification)

This post will cover two broader aspects of classification models:

1. Multiple Performance Measures for a Classification Model
2. Different Methods to evaluate the performance based on the measures from point 1

The content covered will provide a conceptual grasp and they can be easily applied in real world practical implementations. Almost all of the measures I will discuss are already implemented in Machine Learning libraries for Python like scikit-learn, scipy, and numpy.

Performance Measures for a…