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Machine Learning with R Cookbook - Second Edition电子书

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9.8

作者：AshishSingh Bhatia,Yu-Wei, Chiu (David Chiu)

出版社：Packt Publishing

出版时间：2017-10-23

字数：52.7万

所属分类：进口书 > 外文原版书 > 电脑/网络

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Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book ? Apply R to simplify predictive modeling with short and simple code ? Use machine learning to solve problems ranging from small to big data ? Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn ? Create and inspect transaction datasets and perform association analysis with the Apriori algorithm ? Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm ? Compare differences between each regression method to discover how they solve problems ? Detect and impute missing values in air quality data ? Predict possible churn users with the classification approach ? Plot the autocorrelation function with time series analysis ? Use the Cox proportional hazards model for survival analysis ? Implement the clustering method to segment customer data ? Compress images with the dimension reduction method ? Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

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How it works...

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Measuring performance differences between models with the caret package

Getting ready

How to do it...

How it works...

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Ensemble Learning

Introduction

Using the Super Learner algorithm

Getting ready

How to do it...

How it works...

Using ensemble to train and test

Getting ready

How to do it...

How it works...

Classifying data with the bagging method

Getting ready

How to do it...

How it works...

There's more...

Performing cross-validation with the bagging method

Getting ready

How to do it...

How it works...

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Classifying data with the boosting method

Getting ready

How to do it...

How it works...

There's more...

Performing cross-validation with the boosting method

Getting ready

How to do it...

How it works...

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Classifying data with gradient boosting

Getting ready

How to do it...

How it works...

There's more...

Calculating the margins of a classifier

Getting ready

How to do it...

How it works...

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Calculating the error evolution of the ensemble method

Getting ready

How to do it...

How it works...

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Classifying data with random forest

Getting ready

How to do it...

How it works...

There's more...

Estimating the prediction errors of different classifiers

Getting ready

How to do it...

How it works...

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Clustering

Introduction

Clustering data with hierarchical clustering

Getting ready

How to do it...

How it works...

There's more...

Cutting trees into clusters

Getting ready

How to do it...

How it works...

There's more...

Clustering data with the k-means method

Getting ready

How to do it...

How it works...

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Drawing a bivariate cluster plot

Getting ready

How to do it...

How it works...

There's more...

Comparing clustering methods

Getting ready

How to do it...

How it works...

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Extracting silhouette information from clustering

Getting ready

How to do it...

How it works...

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Obtaining the optimum number of clusters for k-means

Getting ready

How to do it...

How it works...

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Clustering data with the density-based method

Getting ready

How to do it...

How it works...

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Clustering data with the model-based method

Getting ready

How to do it...

How it works...

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Visualizing a dissimilarity matrix

Getting ready

How to do it...

How it works...

There's more...

Validating clusters externally

Getting ready

How to do it...

How it works...

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Association Analysis and Sequence Mining

Introduction

Transforming data into transactions

Getting ready

How to do it...

How it works...

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Displaying transactions and associations

Getting ready

How to do it...

How it works...

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Mining associations with the Apriori rule

Getting ready

How to do it...

How it works...

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Pruning redundant rules

Getting ready

How to do it...

How it works...

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Visualizing association rules

Getting ready

How to do it...

How it works...

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Mining frequent itemsets with Eclat

Getting ready

How to do it...

How it works...

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Creating transactions with temporal information

Getting ready

How to do it...

How it works...

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Mining frequent sequential patterns with cSPADE

Getting ready

How to do it...

How it works...

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Using the TraMineR package for sequence analysis

Getting ready

How to do it...

How it works...

Visualizing sequence, Chronogram, and Traversal Statistics

Getting ready

How to do it...

How it works...

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Dimension Reduction

Introduction

Why to reduce the dimension?

Performing feature selection with FSelector

Getting ready

How to do it...

How it works...

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Performing dimension reduction with PCA

Getting ready

How to do it...

How it works...

There's more...

Determining the number of principal components using the scree test

Getting ready

How to do it...

How it works...

There's more...

Determining the number of principal components using the Kaiser method

Getting ready

How to do it...

How it works...

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Visualizing multivariate data using biplot

Getting ready

How to do it...

How it works...

There's more...

Performing dimension reduction with MDS

Getting ready

How to do it...

How it works...

There's more...

Reducing dimensions with SVD

Getting ready

How to do it...

How it works...

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Compressing images with SVD

Getting ready

How to do it...

How it works...

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Performing nonlinear dimension reduction with ISOMAP

Getting ready

How to do it...

How it works...

There's more...

Performing nonlinear dimension reduction with Local Linear Embedding

Getting ready

How to do it...

How it works...

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Big Data Analysis (R and Hadoop)

Introduction

Preparing the RHadoop environment

Getting ready

How to do it...

How it works...

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Installing rmr2

Getting ready

How to do it...

How it works...

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Installing rhdfs

Getting ready

How to do it...

How it works...

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Operating HDFS with rhdfs

Getting ready

How to do it...

How it works...

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Implementing a word count problem with RHadoop

Getting ready

How to do it...

How it works...

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Comparing the performance between an R MapReduce program and a standard R program

Getting ready

How to do it...

How it works...

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Testing and debugging the rmr2 program

Getting ready

How to do it...

How it works...

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Installing plyrmr

Getting ready

How to do it...

How it works...

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Manipulating data with plyrmr

Getting ready

How to do it...

How it works...

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Conducting machine learning with RHadoop

Getting ready

How to do it...

How it works...

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Configuring RHadoop clusters on Amazon EMR

Getting ready

How to do it...

How it works...

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