Machine Learning with R discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R.
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...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, [England] ; Mumbai, [India] :
Packt Publishing
2015.
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| Edición: | 2nd ed |
| Colección: | Community experience distilled.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629740806719 |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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
- Step 3 - training a model on the data
