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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2013.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 245 | 1 | 0 | |a Machine learning with R : |b learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications / |c Brett Lantz. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2013. | |
| 264 | 4 | |c ©2013 | |
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| 520 | |a 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 the results. These steps will build the knowledge you need to apply them to your own data science tasks. Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or. | ||
| 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; 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a Visualizing text data -- word clouds. | |
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