Machine Learning Mastery With R: How to Prepare Data and Develop Models to Predict the Future

✍ Scribed by Jason Brownlee

• Publisher: Independently Published
• Year: 2016
• Tongue: English
• Leaves: 224
• Series: Machine Learning Mastery
• Edition: 1.1
• Category: Library

✦ Synopsis

R has been the gold standard in applied machine learning for a long time. Surveys show that it is the most popular platform used by professional data scientists. It is also preferred by the best data scientists in the world.

In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn how to get started, practice and apply machine learning using the R platform.

✦ Table of Contents

Preface
I Introduction
Welcome
Learn R The Wrong Way
Machine Learning in R
What This Book is Not
Summary
The R Platform
Why Use R
What Is R
Summary
II Lessons
Installing and Starting R
Download and Install R
R Interactive Environment
R Scripts
Summary
Crash Course in R For Developers
R Syntax is Different, But The Same
Assignment
Data Structures
Flow Control
Functions
Packages
5 Things To Remember
Summary
Standard Machine Learning Datasets
Practice On Small Well-Understood Datasets
Package: datasets
Package: mlbench
Package: AppliedPredictiveModeling
Summary
Load Your Machine Learning Datasets
Access To Your Data
Load Data From CSV File
Load Data From CSV URL
Summary
Understand Your Data Using Descriptive Statistics
You Must Understand Your Data
Peek At Your Data
Dimensions of Your Data
Data Types
Class Distribution
Data Summary
Standard Deviations
Skewness
Correlations
Tips To Remember
Summary
Understand Your Data Using Data Visualization
Understand Your Data To Get The Best Results
Visualization Packages
Univariate Visualization
Multivariate Visualization
Tips For Data Visualization
Summary
Prepare Your Data For Machine Learning With Pre-Processing
Need For Data Pre-Processing
Data Pre-Processing in R
Scale Data
Center Data
Standardize Data
Normalize Data
Box-Cox Transform
Yeo-Johnson Transform
Principal Component Analysis Transform
Independent Component Analysis Transform
Tips For Data Transforms
Summary
Resampling Methods To Estimate Model Accuracy
Estimating Model Accuracy
Data Split
Bootstrap
k-fold Cross Validation
Repeated k-fold Cross Validation
Leave One Out Cross Validation
Tips For Evaluating Algorithms
Summary
Machine Learning Model Evaluation Metrics
Model Evaluation Metrics in R
Accuracy and Kappa
RMSE and R2
Area Under ROC Curve
Logarithmic Loss
Summary
Spot-Check Machine Learning Algorithms
Best Algorithm For a Problem
Algorithms To Spot-Check in R
Linear Algorithms
Non-linear Algorithms
Other Algorithms
Summary
Compare The Performance of Machine Learning Algorithms
Choose The Best Machine Learning Model
Prepare Dataset
Train Models
Compare Models
Summary
Tune Machine Learning Algorithms
Get Better Accuracy From Top Algorithms
Tune Machine Learning Algorithms
Test Setup
Tune Using Caret
Tune Using Algorithm Tools
Craft Your Own Parameter Search
Summary
Combine Predictions From Multiple Machine Learning Models
Increase The Accuracy Of Your Models
Test Dataset
Boosting Algorithms
Bagging Algorithms
Stacking Algorithms
Summary
Save And Finalize Your Machine Learning Model
Finalize Your Machine Learning Model
Make Predictions On New Data
Create A Standalone Model
Save and Load Your Model
Summary
III Projects
Predictive Modeling Project Template
Practice Machine Learning With Projects
Machine Learning Project Template in R
Machine Learning Project Template Steps
Tips For Using The Template Well
Summary
Your First Machine Learning Project in R Step-By-Step
Hello World of Machine Learning
Load The Data
Summarize Dataset
Visualize Dataset
Evaluate Some Algorithms
Make Predictions
Summary
Regression Machine Learning Case Study Project
Problem Definition
Analyze Data
Evaluate Algorithms: Baseline
Evaluate Algorithms: Feature Selection
Evaluate Algorithms: Box-Cox Transform
Improve Results With Tuning
Ensemble Methods
Finalize Model
Summary
Binary Classification Machine Learning Case Study Project
Problem Definition
Analyze Data
Evaluate Algorithms: Baseline
Evaluate Algorithms: Transform
Algorithm Tuning
Ensemble Methods
Finalize Model
Summary
More Predictive Modeling Projects
Build And Maintain Recipes
Small Projects on Small Datasets
Competitive Machine Learning
Summary
IV Conclusions
How Far You Have Come
Getting More Help
CRAN
Q&A Websites
Mailing Lists
Package Websites
Books
Acknowledgements