Page Summary

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• 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.

• Introduction to Machine Learning
• Linear regression
• Logistic regression

In theLogistic regression module,you learned how to use thesigmoid functionto convert raw model output to a value between 0 and 1 to make probabilisticpredictions—for example, predicting that a given email has a 75% chance ofbeing spam. But what if your goal is not to output probability but acategory—for example, predicting whether a given email is "spam" or "not spam"?

Classification isthe task of predicting which of a set ofclasses(categories) an example belongs to. In this module, you'll learn how to converta logistic regression model that predicts a probability into abinary classificationmodel that predicts one of two classes. You'll also learn how tochoose and calculate appropriate metrics to evaluate the quality of aclassification model's predictions. Finally, you'll get a brief introduction tomulti-class classificationproblems, which are discussed in more depth later in the course.

• Binary classification
• Class
• Classification
• Multi-class classification
• Sigmoid function