Machine Learning with Python Cookbook, 2nd Edition by Kyle Gallatin & Chris Albon

Author:Kyle Gallatin & Chris Albon [Kyle Gallatin]
Language: eng
Format: epub, pdf
ISBN: 9781098135720
Publisher: O'Reilly Media, Inc.
Published: 2023-08-25T00:00:00+00:00

# Get predicted probabilities logit.predict_proba(features_test)[0:1]

array([[0.86891533, 0.13108467]])

We can see the classes using classes_:

logit.classes_

array([0, 1])

In this example, the first observation has an ~87% chance of being in the negative class (0) and a 13% chance of being in the positive class (1). By default, scikit-learn predicts an observation is part of the positive class if the probability is greater than 0.5 (called the threshold). However, instead of a middle ground, we will often want to explicitly bias our model to use a different threshold for substantive reasons. For example, if a false positive is very costly to our company, we might prefer a model that has a high probability threshold. We fail to predict some positives, but when an observation is predicted to be positive, we can be very confident that the prediction is correct. This trade-off is represented in the true positive rate (TPR) and the false positive rate (FPR). The true positive rate is the number of observations correctly predicted true divided by all true positive observations:

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