Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications /: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. (2013)

• Record Type: Book
• Further Information: Note: Brett Lantz.
• Authors: Lantz, Brett
• 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.
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2013
• Copyright Date: 2013
• Extent: 1 online resource (vii, 375 pages), illustrations
• Subjects: 005.8; COMPUTERS -- Programming -- Algorithms; R (Computer program language) -- Handbooks, manuals, etc; Machine learning -- Statistical methods -- Handbooks, manuals, etc; R (Computer program language) -- Handbooks, manuals, etc; Machine learning -- Statistical methods -- Handbooks, manuals, etc; R (Computer program language) -- Handbooks, manuals, etc; Machine learning -- Statistical methods -- Handbooks, manuals, etc; COMPUTERS -- Machine Theory; COMPUTERS -- Programming Languages; Machine learning -- Statistical methods; R (Computer program language); COMPUTERS -- Mathematical & Statistical Software; COMPUTERS -- Data Processing; Electronic books; Handbooks and manuals
• Languages: English
• ISBNs: 9781782162155; 1782162151; 9781461949657; 1461949653; 1306070333; 9781306070331; 9781680153583; 1680153587
• Related ISBNs: 1782162143; 9781782162148
• Notes: Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.