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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]
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| Edición: | Second edition. |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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.