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Machine learning with R : expert techniques for predictive modeling to solve all your data analysis problems /
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior e...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
[2015]
|
| Edición: | Second edition. |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Lantz, Brett, |e author. | |
| 245 | 1 | 0 | |a Machine learning with R : |b expert techniques for predictive modeling to solve all your data analysis problems / |c Brett Lantz. |
| 250 | |a Second edition. | ||
| 260 | |a Birmingham : |b Packt Publishing, |c [2015] | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a 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; Machine learning successes; The limits of machine learning; Machine learning ethics; How machines learn; Data storage; Abstraction; Generalization; Evaluation; Machine learning in practice; Types of input data; Types of machine learning algorithms; Matching input data to algorithms; Machine learning with R; Installing R packages; Loading and unloading R packages; Summary. | |
| 505 | 8 | |a Chapter 2: Managing and Understanding DataR data structures; Vectors; Factors; Lists; Data frames; Matrixes and arrays; Managing data with R; Saving, loading, and removing R data structures; Importing and saving data from CSV files; 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. | |
| 505 | 8 | |a Measuring spread -- variance and standard deviationExploring 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 nearest neighbor classification; The k-NN algorithm; Measuring similarity with distance; Choosing an appropriate k; Preparing data for use with k-NN; Why is the k-NN algorithm lazy?; Example -- Diagnosing breast cancer with the k-NN algorithm. | |
| 505 | 8 | |a Step 1 -- collecting dataStep 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 Bayes; Understanding Naive Bayes; Basic concepts of Bayesian methods; Understanding probability; Understanding joint probability. | |
| 505 | 8 | |a Computing conditional probability with Bayes' theoremThe Naive Bayes algorithm; Classification with Naive Bayes; 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 -- cleaning and standardizing text data; Data preparation -- splitting text documents into words; Data preparation -- creating training and test datasets; Visualizing text data -- word clouds; Data preparation -- creating indicator features for frequent words. | |
| 505 | 8 | |a Step 3 -- training a model on the data. | |
| 520 | |a Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required. | ||
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning |x Statistical methods. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique |x Méthodes statistiques. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |x Statistical methods |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Erscheint auch als: |n Druck-Ausgabe |t Lantz, Brett. Machine Learning with R |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpMLREDHB3/toc |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL2122139 | ||
| 938 | |a EBSCOhost |b EBSC |n 1048220 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis32308277 | ||
| 938 | |a YBP Library Services |b YANK |n 12556475 | ||
| 994 | |a 92 |b IZTAP | ||