Evaluating Logistic Regression Models

Evaluating classification models

Just like there were many ways to evaluate linear regression models, there are many ways to evaluate the performance of classification models. Accuracy is one of the techniques. But it is not a sufficient metric alone. Why?

Think about a scenario where our model predicts a rare disease that is present only in 0.01% of the data. If our model always predicts that no disease is present, it will still be accurate 99% of the time but it would not diagnose correctly when it matters the most.

Classification report

A classification report is a table that calculates different metrics to evaluate our model. We can obtain the table using the function classification_report in sklearn.metrics

We will make the same model that we made in the last lesson.