Applied Supervised Learning with R

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. The book demonstrates how you can add different regularization terms to avoid overfitting your model.

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

Type
ebook
Category
Data
publication date
2019-05-31
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

no of pages
502
duration
1004
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
approach
Applied Supervised Learning with R perfectly balances theory and exercises. Each module is designed to build on the learnings of the previous module. The book contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
audience
This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.
meta description
Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.
short description
Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.
subtitle
Use machine learning libraries of R to build models that solve business problems and predict future trends
keywords
R, Machine Learning, Supervised Learning, data analysis, regression
Product ISBN
9781838556334