Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications

• Main Author: Lantz, Brett (Author)
• Format: Electronic eBook
• Language: English
• Published: Birmingham, UK : Packt Publishing, 2013.
• Series: Community experience distilled.
• Subjects: R (Computer program language) > Handbooks, manuals, etc.; Machine learning > Statistical methods > Handbooks, manuals, etc.; elektronické knihy; electronic books
• ISBN: 9781782162155; 1782162151; 9781461949657; 1461949653; 1306070333; 9781306070331; 9781680153583; 1680153587; 1782162143; 9781782162148
• Physical Description: 1 online resource (vii, 375 pages) : illustrations

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating...

Table of Contents:

• Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 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 learningInstalling and loading R packages; Installing an R package; Installing a package using the point-and-click interface; Loading an R package; Summary; Chapter 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 summaryVisualizing 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; Summary; Chapter 3: Lazy Learning
• Classification using Nearest Neighbors; Understanding classification using nearest neighbors.
• The kNN algorithmCalculating 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; Summary.
• Chapter 4: Probabilistic Learning
• Classification using Naive BayesUnderstanding 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; Step 1
• collecting data; Step 2
• exploring and preparing the data; Data preparation
• processing text data for analysis; Data preparation
• creating training and test datasets.
• Visualizing text data
• word clouds.