Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory.

Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Features:

· Offers a practical and applied introduction to the most popular machine learning methods.

· Topics covered include feature engineering, resampling, deep learning and more.

· Uses a hands-on approach and real world data.

Brad Boehmke is a data scientist at 84.51° where he wears both software developer and machine learning engineer hats. He is an Adjunct Professor at the University of Cincinnati, author of Data Wrangling with R, and creator of multiple public and private enterprise R packages.

Brandon Greenwell is a data scientist at 84.51° where he works on a diverse team to enable, empower, and encourage others to successfully apply machine learning to solve real business problems. He's part of the Adjunct Graduate Faculty at Wright State University, an Adjunct Instructor at the University of Cincinnati, and the author of several R packages available on CRAN.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory.

Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Features:

• Offers a practical and applied introduction to the most popular machine learning methods.
• Takes readers through the entire modeling process; from data prep to hyperparameter tuning, model evaluation, and interpretation.
• Introduces readers to a wide variety of packages that make up R's machine learning stack.
• Uses a hands-on approach and real world data.

Brad Boehmke is a data scientist at 84.51° where he wears both software developer and machine learning engineer hats. He is an Adjunct Professor at the University of Cincinnati, author of Data Wrangling with R, and creator of multiple public and private enterprise R packages.

Brandon Greenwell is a data scientist at 84.51° where he works on a diverse team to enable, empower, and encourage others to successfully apply machine learning to solve real business problems. He's part of the Adjunct Graduate Faculty at Wright State University, an Adjunct Instructor at the University of Cincinnati, and the author of several R packages available on CRAN.

FUNDAMENTALS

Introduction to Machine Learning

Supervised learning

Regression problems

Classification problems

Unsupervised learning

Roadmap

The data sets

Prerequisites

Data splitting

Simple random sampling

Stratified sampling

Class imbalances

Creating models in R

Many formula interfaces

Many engines

Resampling methods

k-fold cross validation

Bootstrapping

Alternatives

Bias variance trade-off

Bias

Variance

Hyperparameter tuning

Model evaluation

Regression models

Classification models

Putting the processes together

Prerequisites

Target engineering

Dealing with missingness

Visualizing missing values

Imputation

Feature filtering

Numeric feature engineering

Skewness

Standardization

Categorical feature engineering

Lumping

One-hot & dummy encoding

Label encoding

Alternatives

Dimension reduction

Proper implementation

Sequential steps

Data leakage

Putting the process together

Contents v

Prerequisites

Simple linear regression

Estimation

Inference

Multiple linear regression

Assessing model accuracy

Model concerns

Principal component regression

Partial least squares

Feature interpretation

Final thoughts

Prerequisites

Why logistic regression

Simple logistic regression

Multiple logistic regression

Assessing model accuracy

Model concerns

Feature interpretation

Final thoughts

Prerequisites

Why regularize?

Ridge penalty

Lasso penalty

Elastic nets

Implementation

vi Contents

Tuning

Feature interpretation

Attrition data

Final thoughts

Prerequisites

The basic idea

Multivariate regression splines

Fitting a basic MARS model

Tuning

Feature interpretation

Attrition data

Final thoughts