Machine learning with R / Brett Lantz.

Format:

Book

Author/Creator:

Lantz, Brett.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

Machine learning--Statistical methods--Handbooks, manuals, etc.

Machine learning.

R (Computer program language)--Handbooks, manuals, etc.

R (Computer program language).

Programming languages (Electronic computers).

Physical Description:

1 online resource (396 p.)

Edition:

1st edition

Place of Publication:

Birmingham : Packt Publishing, 2013.

Language Note:

English

System Details:

text file

Summary:

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.

Contents:

Intro

Machine Learning with R

Table of Contents

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers and more

Why Subscribe?

Free Access for Packt account holders

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

Errata

Piracy

Questions

1. Introducing Machine Learning

The origins of machine learning

Uses and abuses of machine learning

Ethical considerations

How do machines learn?

Abstraction and knowledge representation

Generalization

Assessing the success of learning

Steps to apply machine learning to your data

Choosing a machine learning algorithm

Thinking about the input data

Thinking about types of machine learning algorithms

Matching your data to an appropriate algorithm

Using R for machine learning

Installing and loading R packages

Installing an R package

Installing a package using the point-and-click interface

Loading an R package

Summary

2. Managing and Understanding Data

R data structures

Vectors

Factors

Lists

Data frames

Matrixes and arrays

Managing data with R

Saving and loading R data structures

Importing and saving data from CSV files

Importing data from SQL databases

Exploring and understanding data

Exploring the structure of data

Exploring numeric variables

Measuring the central tendency - mean and median

Measuring spread - quartiles and the five-number summary

Visualizing numeric variables - boxplots

Visualizing numeric variables - histograms

Understanding numeric data - uniform and normal distributions

Measuring spread - variance and standard deviation.

Exploring categorical variables

Measuring the central tendency - the mode

Exploring relationships between variables

Visualizing relationships - scatterplots

Examining relationships - two-way cross-tabulations

3. Lazy Learning - Classification Using Nearest Neighbors

Understanding classification using nearest neighbors

The kNN algorithm

Calculating distance

Choosing an appropriate k

Preparing data for use with kNN

Why is the kNN algorithm lazy?

Diagnosing breast cancer with the kNN algorithm

Step 1 - collecting data

Step 2 - exploring and preparing the data

Transformation - normalizing numeric data

Data preparation - creating training and test datasets

Step 3 - training a model on the data

Step 4 - evaluating model performance

Step 5 - improving model performance

Transformation - z-score standardization

Testing alternative values of k

4. Probabilistic Learning - Classification Using Naive Bayes

Understanding naive Bayes

Basic concepts of Bayesian methods

Probability

Joint probability

Conditional probability with Bayes' theorem

The naive Bayes algorithm

The naive Bayes classification

The Laplace estimator

Using numeric features with naive Bayes

Example - filtering mobile phone spam with the naive Bayes algorithm

Data preparation - processing text data for analysis

Visualizing text data - word clouds

Data preparation - creating indicator features for frequent words

5. Divide and Conquer - Classification Using Decision Trees and Rules

Understanding decision trees.

Divide and conquer

The C5.0 decision tree algorithm

Choosing the best split

Pruning the decision tree

Example - identifying risky bank loans using C5.0 decision trees

Data preparation - creating random training and test datasets

Boosting the accuracy of decision trees

Making some mistakes more costly than others

Understanding classification rules

Separate and conquer

The One Rule algorithm

The RIPPER algorithm

Rules from decision trees

Example - identifying poisonous mushrooms with rule learners

6. Forecasting Numeric Data - Regression Methods

Understanding regression

Simple linear regression

Ordinary least squares estimation

Correlations

Multiple linear regression

Example - predicting medical expenses using linear regression

Exploring relationships among features - the correlation matrix

Visualizing relationships among features - the scatterplot matrix

Model specification - adding non-linear relationships

Transformation - converting a numeric variable to a binary indicator

Model specification - adding interaction effects

Putting it all together - an improved regression model

Understanding regression trees and model trees

Adding regression to trees.

Example - estimating the quality of wines with regression trees and model trees

Visualizing decision trees

Measuring performance with mean absolute error

7. Black Box Methods - Neural Networks and Support Vector Machines

Understanding neural networks

From biological to artificial neurons

Activation functions

Network topology

The number of layers

The direction of information travel

The number of nodes in each layer

Training neural networks with backpropagation

Modeling the strength of concrete with ANNs

Understanding Support Vector Machines

Classification with hyperplanes

Finding the maximum margin

The case of linearly separable data

The case of non-linearly separable data

Using kernels for non-linear spaces

Performing OCR with SVMs

8. Finding Patterns - Market Basket Analysis Using Association Rules

Understanding association rules

The Apriori algorithm for association rule learning

Measuring rule interest - support and confidence

Building a set of rules with the Apriori principle

Example - identifying frequently purchased groceries with association rules

Step 2 - exploring and preparing the data.

Data preparation - creating a sparse matrix for transaction data

Visualizing item support - item frequency plots

Visualizing transaction data - plotting the sparse matrix

Sorting the set of association rules

Taking subsets of association rules

Saving association rules to a file or data frame

9. Finding Groups of Data - Clustering with k-means

Understanding clustering

Clustering as a machine learning task

The k-means algorithm for clustering

Using distance to assign and update clusters

Choosing the appropriate number of clusters

Finding teen market segments using k-means clustering

Data preparation - dummy coding missing values

Data preparation - imputing missing values

10. Evaluating Model Performance

Measuring performance for classification

Working with classification prediction data in R

A closer look at confusion matrices

Using confusion matrices to measure performance

Beyond accuracy - other measures of performance

The kappa statistic

Sensitivity and specificity

Precision and recall

The F-measure

Visualizing performance tradeoffs

ROC curves

Estimating future performance

The holdout method

Cross-validation

Bootstrap sampling

11. Improving Model Performance

Tuning stock models for better performance

Using caret for automated parameter tuning

Creating a simple tuned model

Customizing the tuning process

Improving model performance with meta-learning

Understanding ensembles

Bagging

Boosting.

Random forests.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed December 2, 2013).

ISBN:

9781680153583

1680153587

9781782162155

1782162151

OCLC:

862380117