Examining logistic regression errors with a confusion matrix

Getting ready

Import and view the confusion matrix for the logistic regression we constructed:

from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_pred,labels = [1,0])

array([[27, 27],
       [12, 88]])

I passed three arguments to the confusion matrix:

• y_test: The test target set
• y_pred: Our logistic regression predictions
• labels: References to a positive class

The labels = [1,0] means that the positive class is 1 and the negative class is 0. In the medical context, we found while exploring the Pima Indians diabetes dataset that class 1 tested positive for diabetes.

Here is the confusion matrix, again in pandas dataframe form:

How to do it...

Reading the confusion matrix

The small array of numbers has the following meaning:

The confusion matrix tells us a bit more about what occurred during classification, not only the accuracy score. The diagonal elements from upper-left to lower-right are correct classifications. There...