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In the Logistic regression module,
you learned how to use the sigmoid function
to convert raw model output to a value between 0 and 1 to make probabilistic
predictions—for example, predicting that a given email has a 75% chance of
being spam. But what if your goal is not to output probability but a
category—for example, predicting whether a given email is "spam" or "not spam"?
Classification is
the task of predicting which of a set of classes
(categories) an example belongs to. In this module, you'll learn how to convert
a logistic regression model that predicts a probability into a
binary classification
model that predicts one of two classes. You'll also learn how to
choose and calculate appropriate metrics to evaluate the quality of a
classification model's predictions. Finally, you'll get a brief introduction to
multi-class classification
problems, which are discussed in more depth later in the course.
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