Learn
Multiple Linear Regression
Review

Great work! Let’s review the concepts before you move on:

  • Multiple Linear Regression uses two or more variables to make predictions about another variable:
y=b+m1x1+m2x2+...+mnxny = b + m_{1}x_{1} + m_{2}x_{2} + ... + m_{n}x_{n}
  • Multiple linear regression uses a set of independent variables and a dependent variable. It uses these variables to learn how to find optimal parameters. It takes a labeled dataset and learns from it. Once we confirm that it’s learned correctly, we can then use it to make predictions by plugging in new x values.
  • We can use scikit-learn’s LinearRegression() to perform multiple linear regression.
  • Residual Analysis is used to evaluate the regression model’s accuracy. In other words, it’s used to see if the model has learned the coefficients correctly.
  • Scikit-learn’s linear_model.LinearRegression comes with a .score() method that returns the coefficient of determination R² of the prediction. The best score is 1.0.

Instructions

We have made an applet using the multiple linear regression model that you built! Have fun!

Folder Icon

Sign up to start coding

Already have an account?