Training Sets, Test Sets, R, and ggplot
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# Training Sets, Test Sets, R, and ggplot

How to evaluate regression model performance in R
4.6 (329 ratings)
8,183 students enrolled
Created by Charles Redmond
Last updated 6/2015
English
English [Auto-generated]
Price: Free
Includes:
• 1.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
What Will I 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
View Curriculum
Requirements
• It is necessary that the students have the background one would get by viewing my two Udemy courses on linear and polynomial regression.
• Students will need to have R and RStudio installed on their own computers.
Description

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.

Who is the target audience?
• This course is for those looking to improve their R programming skills.
• This course is for those with the background equivalent to what one would have after viewing my first two Udemy courses in linear and polynomial regression.
Compare to Other Test Taking Skills Courses
Curriculum For This Course
15 Lectures
01:30:25
+
Training and Test Sets
7 Lectures 42:28

In this video, I introduce the course.

Introduction
01:28

After this lecture, you will be able to extract specific rows from a data frame. You will also be able to sample from a given set of integers. Putting all of this together, you will be able to randomly divide a data frame into two distinct data frames, each of the same size.

Row-slicing Data Frames
08:01

In this lecture, I generate plots of both the training set and the test set. I also show you how to add a title to your plots in ggplot.

Plotting the Training and Test Sets
07:37

After viewing this lecture, you will be able to use R's predict function and ggplot to obtain a plot of the least-squares line. You will also begin thinking about applying the least-squares line, generated from the training data, to the test data.

Plotting the Least-Squares Line
09:43

After viewing this video, you will be able to use R's predict function to calculate the test mean squared error.

Calculating the Test MSE
05:58

After viewing this video, you will be able to use R's predict function to plot the quadratic polynomial that fits the training data set the best. We will have to do this by writing our own function and using the stat_function in ggplot.

06:59

After viewing this video, you will be able to use R's predict function to calculate the test MSE

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Calculating the Test MSE for the Quadratic Model
02:42
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More with the MSE
8 Lectures 47:57

After viewing this lecture, you will be able to write for loops in R.

For Loops
03:30

After viewing this lecture, it will be easier for you to generate higher-degree polynomial models.

lm Revisited
03:31

After viewing this video, you will be able to quickly generate test MSE's for higher degree polynomials, using a bit of programming.

MSE via a For Loop
07:39

After viewing this video, you will be able to generate a plot, via ggplot, of polynomial degree vs. MSE.

Visualizing the MSE's
05:41

After viewing this lecture, you will be able to write functions of two variables in R.

Functions of Two Variables
01:59

In this video, we will take our for loop and set it inside a function of two variables.

For Loop inside a Function
07:44

After viewing this lecture, you will be able to repeatedly divide the original data set into training and test sets, calculate the test MSE's for a range of polynomial models, and plot the results. You will do this with the help of a bit of programming.

Variability of the Test MSE
15:29

In this lecture, I mention some problems associated with the method we have been discussing throughout the course. I also give information about an excellent resource on machine learning.

Course Wrap-up
02:24