xuman-Amy / Regression-Analysis

回归分析、《python machine learning 》chapter10

Python Machine Learning - Code Examples

Chapter 10: Predicting Continuous Target Variables with Regression Analysis

Chapter Outline

• Introducing linear regression
  • Simple linear regression
  • Multiple linear regression
• Exploring the Housing dataset
  • Loading the Housing dataset into a data frame
  • Visualizing the important characteristics of a dataset
  • Looking at relationships using a correlation matrix
• Implementing an ordinary least squares linear regression model
  • Solving regression for regression parameters with gradient descent
  • Estimating coef cient 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
  • Adding polynomial terms using scikit-learn
  • Modeling nonlinear relationships in the Housing dataset
• Dealing with nonlinear relationships using random forests
  • Decision tree regression
  • Random forest regression
• Summary