Logistic regression 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:
The higher the accuracy, the better a model is able to correctly classify observations.
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.