Prerequisites

• ML Introduction with scikit-learn
• Linear Regression with Python

Classification is a supervised learning task where the goal is to predict the class of an instance using its features. The model learns from labeled examples in a training set and then assigns a class to new, unseen data.

Regression predicts a continuous numeric value (e.g., price), which can take many possible values. Classification predicts a categorical value (e.g., type of sweet), choosing one option from a limited set of classes.

There are several types of classification:

• Binary classification: the target has two possible outcomes (spam/not spam, cookie/not cookie);
• Multi-class classification: three or more possible categories (spam/important/ad/other; cookie/marshmallow/candy);
• Multi-label classification: an instance can belong to multiple classes simultaneously (a movie can be action and comedy; an email can be important and work-related).

For most ML models, you need to encode the target to a number. For binary classification, outcomes are usually encoded as 0/1 (e.g., 1 - cookie, 0 - not a cookie). For a multi-class classification, outcomes are usually encoded as 0, 1, 2, ... (e.g., 0 - candy, 1 - cookie, 2 - marshmallow).

Many different models can perform classification. Some examples include:

• k-Nearest Neighbors;
• Logistic Regression;
• Decision Tree;
• Random Forest.

Luckily, they are all implemented in the scikit-learn library and are easy to use.