������������������������
������
����������������������������
������������������������
���������������������
��������������
Jason Brownlee
Machine Learning Mastery with R
Get Started, Build Accurate Models and Work Through Projects
Step-by-Step
i
Machine Learning Mastery with R
©Copyright 2016 Jason Brownlee. All Rights Reserved.
First Edition, v1.1
Contents
Preface iii
I Introduction
1 Welcome 2
1.1 Learn R The Wrong Way
1.2 Machine Learning in R
1.3 What This Book is Not
1.4 Summary
2 The R Platform
2.1 Why Use R
2.2 What Is R
2.3 Summary
II Lessons
3 Installing and Starting R
3.1 Download and Install R
3.2 R Interactive Environment
3.3 R Scripts
3.4 Summary
4 Crash Course in R For Developers
4.1 R Syntax is Di erent, But The Same
4.2 Assignment
4.3 Data Structures
4.4 Flow Control
4.5 Functions
4.6 Packages
4.7 5 Things To Remember
4.8 Summary
ii
iii
5 Standard Machine Learning Datasets
5.1 Practice On Small Well-Understood Datasets
5.2 Package: datasets
5.3 Package: mlbench
5.4 Package: AppliedPredictiveModeling
5.5 Summary
6 Load Your Machine Learning Datasets
6.1 Access To Your Data
6.2 Load Data From CSV File
6.3 Load Data From CSV URL
6.4 Summary
7 Understand Your Data Using Descriptive Statistics
7.1 You Must Understand Your Data
7.2 Peek At Your Data
7.3 Dimensions of Your Data
7.4 Data Types
7.5 Class Distribution
7.6 Data Summary
7.7 Standard Deviations
7.8 Skewness
7.9 Correlations
7.10 Tips To Remember
7.11 Summary
8 Understand Your Data Using Data Visualization
8.1 Understand Your Data To Get The Best Results
8.2 Visualization Packages
8.3 Univariate Visualization
8.4 Multivariate Visualization
8.5 Tips For Data Visualization
8.6 Summary
9 Prepare Your Data For Machine Learning With Pre-Processing
9.1 Need For Data Pre-Processing
9.2 Data Pre-Processing in R
9.3 Scale Data
9.4 Center Data
9.5 Standardize Data
9.6 Normalize Data
9.7 Box-Cox Transform
9.8 Yeo-Johnson Transform
9.9 Principal Component Analysis Transform
9.10 Independent Component Anal
Machine Learning Mastery with R_Jason Brownlee.pdf