scikit-learn : Machine Learning Simplified

Chapter 4. From Linear Regression to Logistic Regression

In Chapter 2, Linear Regression, we discussed simple linear regression, multiple linear regression, and polynomial regression. These models are special cases of the generalized linear model, a flexible framework that requires fewer assumptions than ordinary linear regression. In this chapter, we will discuss some of these assumptions as they relate to another special case of the generalized linear model called logistic regression.

Unlike the models we discussed previously, logistic regression is used for classification tasks. Recall that the goal in classification tasks is to find a function that maps an observation to its associated class or label. A learning algorithm must use pairs of ...