Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque.

This tutorial is authored by KVS Setty

You can find the complete source code at my git repository

A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a trained machine learning algorithm predicts a coarse grid across the input feature space.

A decision surface plot is a powerful tool for understanding how a given model “sees” the prediction task and how it has decided to divide the input feature space by class label.

In this tutorial, you will discover how to plot a decision surface for a classification machine learning algorithm.

After completing this tutorial, you will know:

• Decision surface is a diagnostic tool for understanding how a classification algorithm divides up the feature space.
• How to plot a decision surface for using crisp class labels for a machine learning algorithm.
• How to plot and interpret a decision surface using predicted probabilities.

Let’s get started.

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