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Training Sets, Test Sets, R, and ggplot

via Udemy

Overview

How to evaluate regression model performance in R

What you'll learn:
  • randomly divide a data set into a training set and a test set
  • calculate the test MSE (mean squared error)
  • calculate quickly the MSE for a number of models
  • visualize the variability of the MSE with ggplot
  • row-slice data frames
  • use R's predict function
  • write for loops in R
  • write functions of two variables in R
  • combine functions and for loops
  • add titles and labels to plots in ggplot

In this course, I show you how to evaluate the performance of a regression model using training sets and test sets. We will use R and ggplot as our tools. Along the way, we will learn how to row-slice data frames, use the predict function in R, and add titles and labels to our plots. We will also work on our programming skills by learning how to write for loops and functions of two variables.

Students should have the background in R, ggplot, and regression equivalent to what one would have after viewing my two Udemy courses on linear and polynomial regression. At a relaxed pace, it should take about two weeks to complete the course.

Taught by

Charles Redmond

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