Chapter 9: Supervised Learning

Learning Objectives

•Distinguish the different types of models used in supervised learning.

•Relate correlation to a linear regression model in a supervised learning problem.

•Evaluate and compare types of supervised learning algorithms like logistic regression, decision trees, random forests, and k-nearest neighbors for both regression and classification problems.

Google Drive For Chapter 9 Review Questions

Google Drive For Chapter 9 "Try It Yourself" Python Notebooks

Google Drive For Chapter 9 Data Sets

Section 9.1: Linear Regression with No Explanatory Variables

Section 9.2: Linear Regression with a Categorical Explanatory Variable

Try It Yourself: Perform a Two-Sample t-Test

Try It Yourself: Connecting Linear Regression and t-Test

Section 9.3: Linear Regression with a Quantitative Explanatory Variable

Try It Yourself: Compute a Correlation and Fit a Regression Line

Try It Yourself: Extrapolation

Section 9.4: Multiple Linear Regression

Try It Yourself: Exploring R-Squared in Multiple Linear Regression

Section 9.5: Nonparametric Regression Models

Try It Yourself: Fit, Evaluate, and Interpret Decision Trees

Try It Yourself: Fit, Evaluate, and Interpret Random Forests

Try It Yourself: Fit and Evaluate k-Nearest Neighbors

Try It Yourself: Compare Regression Models

Section 9.6: Classification Models

Try It Yourself: Fit, Evaluate, and Interpret Logistic Regression

Try It Yourself: Compare Classification Models