Applied Supervised Learning with R

Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction.


Key Features
Study supervised learning algorithms by using real-world datasets

Fine tune optimal parameters with hyperparameter optimization

Select the best algorithm using the model evaluation framework


Book Description
R provides excellent visualization features that are essential for exploring data before using it in automated learning.


Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms.


By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.


What you will learn
Develop analytical thinking to precisely identify a business problem

Wrangle data with dplyr, tidyr, and reshape2

Visualize data with ggplot2

Validate your supervised machine learning model using k-fold

Optimize hyperparameters with grid and random search, and Bayesian optimization

Deploy your model on Amazon Web Services (AWS) Lambda with plumber

Improve your model's performance with feature selection and dimensionality reduction


Who this book is for
This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book.