Classification Accuracy is Not Enough: More Performance Measures You Can Use

Last Updated on June 20, 2019

When you build a model for a classification problem you almost always want to look at the accuracy of that model as the number of correct predictions from all predictions made.

This is the classification accuracy.

In a previous post, we have looked at evaluating the robustness of a model for making predictions on unseen data using cross-validation and multiple cross-validation where we used classification accuracy and average classification accuracy.

Once you have a model that you believe can make robust predictions you need to decide whether it is a good enough model to solve your problem. Classification accuracy alone is typically not enough information to make this decision.

In this post, we will look at Precision and Recall performance measures you can use to evaluate your model for a binary classification problem.

Recurrence of Breast Cancer

The breast cancer dataset is a standard machine
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