Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
rasbt
GitHub Repository: rasbt/machine-learning-book
 Path: blob/main/ch09/README.md
1245 views

Chapter 9: Predicting Continuous Target Variables with Regression Analysis

Chapter Outline

  • Introducing regression

    • Simple linear regression

  • Exploring the Ames Housing Dataset

    • Loading the Ames Housing dataset into a data frame

    • Visualizing the important characteristics of a dataset

  • Implementing an ordinary least squares linear regression model

    • Solving regression for regression parameters with gradient descent

    • Estimating the coefficient of a regression model via scikit-learn

  • Fitting a robust regression model using RANSAC

  • Evaluating the performance of linear regression models

  • Using regularized methods for regression

  • Turning a linear regression model into a curve - polynomial regression

    • Modeling nonlinear relationships in the Ames Housing dataset

    • Dealing with nonlinear relationships using random forests

      • Decision tree regression

      • Random forest regression

  • Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.