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.
[null,null,["Last updated 2024-10-16 UTC."],[[["This module focuses on converting logistic regression models into binary classification models for predicting categories instead of probabilities."],["You'll learn how to determine the optimal threshold for classification, calculate and select appropriate evaluation metrics, and interpret ROC and AUC."],["The module covers binary and provides an introduction to multi-class classification, building upon prior knowledge of machine learning, linear regression, and logistic regression."],["The content explores methods for evaluating the quality of classification model predictions and applying them to real-world scenarios."]]],[]]