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.
Deze oefening maakt deel uit van de cursus
Generalized Linear Models in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Define the cutoff
cutoff = ____
# Compute class predictions: y_prediction
y_prediction = np.where(____ > ____, 1, 0)