Training Sets, Test Sets, R, and ggplot

Training Sets, Test Sets, R, and ggplot, available at Free, has an average rating of 4.35, with 15 lectures, based on 734 reviews, and has 19954 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is for those looking to improve their R programming skills. or 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. It is particularly useful for This course is for those looking to improve their R programming skills. or 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.

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Summary

Title: Training Sets, Test Sets, R, and ggplot

Price: Free

Average Rating: 4.35

Number of Lectures: 15

Number of Published Lectures: 15

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 15

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Training and Test Sets

Lecture 1: Introduction

Lecture 2: Row-slicing Data Frames

Lecture 3: Plotting the Training and Test Sets

Lecture 4: Plotting the Least-Squares Line

Lecture 5: Calculating the Test MSE

Lecture 6: Generating a Quadratic Model

Lecture 7: Calculating the Test MSE for the Quadratic Model

Chapter 2: More with the MSE

Lecture 1: For Loops

Lecture 2: lm Revisited

Lecture 3: MSE via a For Loop

Lecture 4: Visualizing the MSE's

Lecture 5: Functions of Two Variables

Lecture 6: For Loop inside a Function

Lecture 7: Variability of the Test MSE

Lecture 8: Course Wrap-up

Instructors

Charles Redmond
Professor at Mercyhurst University

Rating Distribution

1 stars: 4 votes
2 stars: 7 votes
3 stars: 60 votes
4 stars: 247 votes
5 stars: 416 votes

Frequently Asked Questions

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