Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Machine Learning with R
  • Toc
  • feedback
Machine Learning with R

Machine Learning with R

By : Brett Lantz
4.2 (46)
close
Machine Learning with R

Machine Learning with R

4.2 (46)
By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Table of Contents (16 chapters)
close
close
Preface
Preface
Who this book is for
What this book covers
What you need for this book
Get in touch
Free Chapter
1
1. Introducing Machine Learning
1. Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
2
2. Managing and Understanding Data
2. Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
3
3. Lazy Learning – Classification Using Nearest Neighbors
3. Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
4
4. Probabilistic Learning – Classification Using Naive Bayes
chevron up
4. Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
5
5. Divide and Conquer – Classification Using Decision Trees and Rules
5. Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
6
6. Forecasting Numeric Data – Regression Methods
6. Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
7
7. Black Box Methods – Neural Networks and Support Vector Machines
7. Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – modeling the strength of concrete with ANNs
Understanding support vector machines
Example – performing OCR with SVMs
Summary
8
8. Finding Patterns – Market Basket Analysis Using Association Rules
8. Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
9
9. Finding Groups of Data – Clustering with k-means
9. Finding Groups of Data – Clustering with k-means
Understanding clustering
Finding teen market segments using k-means clustering
Summary
10
10. Evaluating Model Performance
10. Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
11
11. Improving Model Performance
11. Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
12
12. Specialized Machine Learning Topics
12. Specialized Machine Learning Topics
Managing and preparing real-world data
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
13
Other Books You May Enjoy
Other Books You May Enjoy
14
Leave a review - let other readers know what you think
Leave a review - let other readers know what you think
15
Index
Index

Chapter 4. Probabilistic Learning – Classification Using Naive Bayes

When a meteorologist provides a weather forecast, precipitation is typically described with phrases like "70 percent chance of rain." Such forecasts are known as probability of precipitation reports. Have you ever considered how they are calculated? It is a puzzling question because in reality, either it will rain or not.

Weather estimates are based on probabilistic methods, which are those concerned with describing uncertainty. They use data on past events to extrapolate future events. In the case of the weather, the chance of rain describes the proportion of prior days with similar atmospheric conditions in which precipitation occurred. A 70 percent chance of rain implies that in seven out of 10 past cases with similar conditions, precipitation occurred somewhere in the area.

This chapter covers the Naive Bayes algorithm, which uses probabilities in much the same way as a weather...

End of Section 1
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete