Compute confusion matrix
As you learned in the video the logistic regression model generates two types of predictions, a continuous valued prediction, in the form of a probability, and a class prediction which in the example of the wells dataset is a discrete category with two classes.
In the previous exercise you computed the continuous values prediction in the form of a probability. In this exercise you will use those values to assign a class to each observation in your wells_test sample. Finally you will describe the model using the confusion matrix.
Computed predictions prediction and wells_test are loaded in your workspace.
This exercise is part of the course
Generalized Linear Models in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define the cutoff
cutoff = ____
# Compute class predictions: y_prediction
y_prediction = np.where(____ > ____, 1, 0)