Classification

Classification is a supervised learning task where the variable we are trying to predict is discrete, whether that is binary or categorical.

Some examples of discrete data include:

• Whether an email is spam or not (binary)
• Whether an outcome is successful or not (binary)
• Which of nine numeric digits is represented by some handwriting (categorical)
• Which of three families a given penguin is likely to be a member of (categorical)

In this chapter, we will explore different classification models, and introduce key performance metrics used to evaluate the effectiveness of classification models.

Classification Objectives

Classification Models

Classification Models:

• Logistic Regression (yes, this is a classification, not a regression model)
• Decision Tree
• Random Forest
• etc.

Classification Metrics

Classification Metrics:

• Accuracy
• Precision
• Recall
• F-1 Score
• ROC AUC
• etc.