3. Classification algorithms

Let’s enter the vast world of classification algorithms in machine learning.

In classification, the outcomes - the targets - are no more real-valued but class labels that can be associated with discrete numericals. While it could appear as a sub-case of the linear regression, we will see that we need to redefine our mathematical tools.

In this lecture we will cover a method for classifying data called logistic regression. You will discover key machine learning concepts:

• The sigmoid function

• A revised cost function for logistic regression

• Regularization

• Performance metrics to diagnose how well the model fits the data and what to do to get a better fit

Particle physics florishes of classification implementations. We will put the logistic regression in practice during the assignment to classify collision data.