Mastering machine learning with R: advanced prediction, algorithms, and learning methods with R 3.x

✍ Scribed by Lesmeister, Cory

• Publisher: Packt Publishing Limited
• Year: 2017
• Tongue: English
• Leaves: 410
• Category: Library

✦ Synopsis

Master machine learning techniques with R to deliver insights in complex projects About This Book - Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST - Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning - Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn - Gain deep insights into the application of machine learning tools in the industry - Manipulate data in R efficiently to prepare it for analysis - Master the skill of recognizing techniques for effective visualization of data - Understand why and how to create test and training data sets for analysis - Master fundamental learning methods such as linear and logistic regression - Comprehend advanced learning methods such as support vector machines - Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.

✦ Table of Contents

Cover......Page 1
Credits......Page 4
About the Author......Page 5
About the Reviewers......Page 6
Packt Upsell......Page 7
Customer Feedback......Page 8
Table of Contents......Page 9
Preface......Page 15
Chapter 1: A Process for Success......Page 22
The process......Page 23
Business understanding......Page 24
Identifying the business objective......Page 25
Producing a project plan......Page 26
Data preparation......Page 27
Modeling......Page 28
Deployment......Page 29
Algorithm flowchart......Page 30
Summary......Page 35
Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning......Page 36
Univariate linear regression......Page 37
Business understanding......Page 40
Business understanding......Page 46
Data understanding and preparation......Page 47
Modeling and evaluation......Page 50
Qualitative features......Page 64
Interaction terms......Page 66
Summary......Page 68
Chapter 3: Logistic Regression and Discriminant Analysis......Page 69
Logistic regression......Page 70
Business understanding......Page 71
Data understanding and preparation......Page 72
Modeling and evaluation......Page 77
The logistic regression model......Page 78
Logistic regression with cross-validation......Page 81
Discriminant analysis overview......Page 84
Discriminant analysis application......Page 87
Multivariate Adaptive Regression Splines (MARS)......Page 90
Model selection......Page 96
Summary......Page 99
Chapter 4: Advanced Feature Selection in Linear Models......Page 100
Regularization in a nutshell......Page 101
LASSO......Page 102
Business understanding......Page 103
Data understanding and preparation......Page 104
Best subsets......Page 110
Ridge regression......Page 114
LASSO......Page 119
Elastic net......Page 122
Cross-validation with glmnet......Page 125
Model selection......Page 127
Logistic regression example ......Page 128
Summary......Page 131
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines......Page 132
K-nearest neighbors......Page 133
Support vector machines......Page 134
Business understanding......Page 138
Data understanding and preparation......Page 139
KNN modeling......Page 145
SVM modeling......Page 150
Model selection......Page 153
Feature selection for SVMs......Page 156
Summary......Page 158
Chapter 6: Classification and Regression Trees......Page 159
Understanding the regression trees......Page 160
Classification trees......Page 161
Random forest......Page 162
Gradient boosting......Page 163
Regression tree......Page 164
Classification tree......Page 168
Random forest regression......Page 170
Random forest classification......Page 173
Extreme gradient boosting - classification......Page 177
Feature Selection with random forests......Page 182
Summary......Page 185
Introduction to neural networks......Page 186
Deep learning, a not-so-deep overview......Page 191
Deep learning resources and advanced methods......Page 193
Business understanding......Page 195
Data understanding and preparation......Page 196
Modeling and evaluation......Page 201
An example of deep learning......Page 206
Data upload to H2O......Page 207
Create train and test datasets......Page 209
Modeling......Page 210
Summary......Page 214
Chapter 8: Cluster Analysis......Page 215
Hierarchical clustering......Page 216
Distance calculations......Page 217
K-means clustering......Page 218
Gower and partitioning around medoids......Page 219
Gower......Page 220
Random forest......Page 221
Business understanding......Page 222
Data understanding and preparation......Page 223
Hierarchical clustering......Page 225
K-means clustering......Page 235
Gower and PAM......Page 239
Random Forest and PAM......Page 241
Summary......Page 243
Chapter 9: Principal Components Analysis......Page 244
An overview of the principal components......Page 245
Rotation......Page 248
Business understanding......Page 250
Data understanding and preparation......Page 251
Component extraction......Page 253
Orthogonal rotation and interpretation......Page 254
Creating factor scores from the components......Page 256
Regression analysis......Page 257
Summary......Page 263
Chapter 10: Market Basket Analysis, Recommendation Engines, and Sequential Analysis......Page 264
An overview of a market basket analysis......Page 265
Business understanding......Page 266
Data understanding and preparation......Page 267
Modeling and evaluation......Page 269
An overview of a recommendation engine......Page 273
Item-based collaborative filtering......Page 275
Singular value decomposition and principal components analysis......Page 276
Data understanding, preparation, and recommendations......Page 280
Modeling, evaluation, and recommendations......Page 283
Sequential data analysis......Page 293
Sequential analysis applied......Page 294
Summary......Page 301
Chapter 11: Creating Ensembles and Multiclass Classification......Page 302
Ensembles......Page 303
Business and data understanding......Page 304
Modeling evaluation and selection......Page 305
Multiclass classification......Page 308
Business and data understanding......Page 309
Model evaluation and selection......Page 313
Random forest......Page 314
Ridge regression......Page 316
MLR's ensemble......Page 317
Summary......Page 319
Chapter 12: Time Series and Causality......Page 320
Univariate time series analysis......Page 321
Understanding Granger causality......Page 327
Business understanding......Page 328
Data understanding and preparation......Page 330
Univariate time series forecasting......Page 334
Linear regression......Page 338
Vector autoregression......Page 341
Summary......Page 346
Chapter 13: Text Mining......Page 348
Text mining framework and methods......Page 349
Topic models......Page 351
Other quantitative analyses......Page 352
Data understanding and preparation......Page 354
Word frequency and topic models......Page 357
Additional quantitative analysis......Page 362
Summary......Page 371
Chapter 14: R on the Cloud......Page 372
Creating an Amazon Web Services account......Page 373
Launch a virtual machine......Page 375
Start RStudio......Page 379
Summary......Page 381
Getting R up-and-running......Page 382
Using R......Page 388
Data frames and matrices......Page 393
Creating summary statistics......Page 395
Installing and loading R packages......Page 399
Data manipulation with dplyr......Page 400
Summary......Page 403
Appendix B: Sources......Page 404
Index......Page 405