Classification Models

The goal of classification models is to classify data into distinct classes. For example, a spam filter is a classification model that aims to classify emails into "spam" (referring to unsolicited and unwanted email) or "ham" (a legitimate email). Spam filters are an example of a binary classifier since there are two classes. The input to the filter may include the content of the email, the email address of the sender, and the subject line, among other features, and the output will be the predicted class, spam or ham. Classification models can classify data into more than two distinct classes (known as multi-class classification) or classify data with multiple positive labels (known as multi-label classification).

There are several different algorithms that can be used for classification tasks. Some popular ones include logistic regression, decision trees, and ANNs. ANNs are a great choice for classification models since they can learn complex...