is a type of regression we can use when the response variable is binary.

One common way to evaluate the quality of a logistic regression model is to create a confusion matrix, which is a 2×2 table that shows the predicted values from the model vs. the actual values from the test dataset.

The following step-by-step example shows how to create a confusion matrix in Excel.

Step 1: Enter the Data

First, let’s enter a column of actual values for a response variable along with the predicted values by a logistic regression model:

Step 2: Create the Confusion Matrix

Next, we’ll use the COUNTIFS() formula to count the number of values that are “0” in the Actual column and also “0” in the Predicted column:

We’ll use a similar formula to fill in every other cell in the confusion matrix:

Step 3: Calculate Accuracy, Precision and Recall

Once we’ve created the confusion matrix, we can calculate the following metrics:

• Accuracy: Percentage of correct predictions
• Precision: Correct positive predictions relative to total positive predictions
• Recall: Correct positive predictions relative to total actual positives

The following formulas show how to calculate each of these metrics in Excel:

In this example, our model has an accuracy of 0.7 which tells us that it correctly classified 70% of observations.

If we’d like, we can compare this accuracy to that of other logistic regression models to determine which model is best at classifying observations into categories of 0 or 1.