R Machine Learning Essentials电子书

R Machine Learning Essentials

Table of Contents

R Machine Learning Essentials

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Citations and references

Piracy

Questions

1. Transforming Data into Actions

A data-driven approach in business decisions

Business decisions come from knowledge and expertise

The digital era provides more data and expertise

Technology connects data and businesses

Identifying hidden patterns

Data contains hidden information

Business problems require hidden information

Reshaping the data

Identifying patterns with unsupervised learning

Making business decisions with unsupervised learning

Estimating the impact of an action

Business problems require estimating future events

Gathering the data to learn from

Predicting future outcomes using supervised learning

Summary

2. R – A Powerful Tool for Developing Machine Learning Algorithms

Why R

An interactive approach to machine learning

Expectations of machine learning software

R and RStudio

The R tutorial

The basic tools of R

Understanding the basic R objects

What are the R standards?

Some useful R packages

Summary

3. A Simple Machine Learning Analysis

Exploring data interactively

Defining a table with the data

Visualizing the data through a histogram

Visualizing the impact of a feature

Visualizing the impact of two features combined

Exploring the data using machine learning models

Exploring the data using a decision tree

Predicting newer outcomes

Building a machine learning model

Using the model to predict new outcomes

Validating a model

Summary

4. Step 1 – Data Exploration and Feature Engineering

Building a machine learning solution

Building the feature data

Exploring and visualizing the features

Modifying the features

Ranking the features using a filter or a dimensionality reduction

Summary

5. Step 2 – Applying Machine Learning Techniques

Identifying a homogeneous group of items

Identifying the groups using k-means

Exploring the clusters

Identifying a cluster's hierarchy

Applying the k-nearest neighbor algorithm

Optimizing the k-nearest neighbor algorithm

Summary

6. Step 3 – Validating the Results

Validating a machine learning model

Measuring the accuracy of an algorithm

Defining the average accuracy

Visualizing the average accuracy computation

Tuning the parameters

Selecting the data features to include in the model

Tuning features and parameters together

Summary

7. Overview of Machine Learning Techniques

Overview

Supervised learning

The k-nearest neighbors algorithm

Decision tree learning

Linear regression

Perceptron

Ensembles

Unsupervised learning

k-means

Hierarchical clustering

PCA

Summary

8. Machine Learning Examples Applicable to Businesses

Overview of the problem

Data overview

Exploring the output

Exploring and transforming features

Clustering the clients

Predicting the output

Summary

Index