Linear Regression Limitation
Explore the limitations of linear regression in solving classification problems with categorical labels. Understand why linear regression fails with non-aligned data points and outliers, and learn the shift toward binary classification to handle these challenges. This lesson prepares you to develop classifiers that predict categorical outcomes rather than numeric values.
We'll cover the following...
Towards the goal
We started our journey in machine learning by way of linear regression. Now we’ll use that knowledge (and that code) to branch off toward our goal, which is a program that recognizes images.
This chapter covers the first step toward image recognition. We’ll build a classifier, which isa program that assigns data to one of a limited number of classes. Instead of numerical labels, classifiers work with categorical labels. For example, consider the difference between our pizza predictor and a system that recognizes plants. The pizza predictor outputs a number. By contrast, the plant classifier would output the name of a plant taken from a predefined list of possible species.
This lesson will start off small: our classifier will be a binary classifier that only recognizes two classes. Many useful real-life systems are based on binary classification. For example, the pneumonia detector that we described in the first chapter assigns X-ray scans to either the class “pneumonia” or the class “no pneumonia.”
We’ll replace the ...