In the previous chapter, we added several useful supervised learning algorithms for regression tasks to our toolbox. Continuing with building on top of linear regression, in this chapter, we are going to build two classification algorithms: linear classifier and logistic regression. Both of them take familiar feature vectors as input, similar to multiple linear regression. The difference is in their output. The linear classifier will output true or false (binary classification) and logistic regression will provide the probability of some event happening.
The topics to discuss in this chapter are:
- Bias and variance
- Linear classifier
- Logistic regression
