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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.

目录展开

Title Page

Second Edition

Machine Learning with R Cookbook

Second Edition

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

Practical Machine Learning with R

Introduction

Downloading and installing R

Getting ready

How to do it...

How it works...

See also

Downloading and installing RStudio

Getting ready

How to do it...

How it works...

See also

Installing and loading packages

Getting ready

How to do it...

How it works...

See also

Understanding of basic data structures

Data types

Data structures

Vectors

How to do it...

How it works...

Lists

How to do it...

How it works...

Array

How to do it...

How it works...

Matrix

How to do it...

DataFrame

How to do it...

Basic commands for subsetting

How to do it...

Data input

Reading and writing data

Getting ready

How to do it...

How it works...

There's more...

Manipulating data

Getting ready

How to do it...

How it works...

There's more...

Applying basic statistics

Getting ready

How to do it...

How it works...

There's more...

Visualizing data

Getting ready

How to do it...

How it works...

See also

Getting a dataset for machine learning

Getting ready

How to do it...

How it works...

See also

Data Exploration with Air Quality Datasets

Introduction

Using air quality dataset

Getting ready

How to do it...

How it works...

There's more...

Converting attributes to factor

Getting ready

How to do it...

How it works...

There's more...

Detecting missing values

Getting ready

How to do it...

How it works...

There's more...

Imputing missing values

Getting ready

How to do it...

How it works...

Exploring and visualizing data

Getting ready

How to do it...

Predicting values from datasets

Getting ready

How to do it...

How it works...

Analyzing Time Series Data

Introduction

Looking at time series data

Getting ready

How to do it...

How it works...

See also

Plotting and forecasting time series data

Getting ready

How to do it...

How it works...

See also

Extracting, subsetting, merging, filling, and padding

Getting ready

How to do it...

How it works...

See also

Successive differences and moving averages

Getting ready

How to do it...

How it works...

See also

Exponential smoothing

Getting ready

How to do it...

How it works...

See also

Plotting the autocorrelation function

Getting ready

How to do it...

How it works...

See also

R and Statistics

Introduction

Understanding data sampling in R

Getting ready

How to do it...

How it works...

See also

Operating a probability distribution in R

Getting ready

How to do it...

How it works...

There's more...

Working with univariate descriptive statistics in R

Getting ready

How to do it...

How it works...

There's more...

Performing correlations and multivariate analysis

Getting ready

How to do it...

How it works...

See also

Conducting an exact binomial test

Getting ready

How to do it...

How it works...

See also

Performing a student's t-test

Getting ready

How to do it...

How it works...

See also

Performing the Kolmogorov-Smirnov test

Getting ready

How to do it...

How it works...

See also

Understanding the Wilcoxon Rank Sum and Signed Rank test

Getting ready

How to do it...

How it works...

See also

Working with Pearson's Chi-squared test

Getting ready

How to do it...

How it works...

There's more...

Conducting a one-way ANOVA

Getting ready

How to do it...

How it works...

There's more...

Performing a two-way ANOVA

Getting ready

How to do it...

How it works...

See also

Understanding Regression Analysis

Introduction

Different types of regression

Fitting a linear regression model with lm

Getting ready

How to do it...

How it works...

There's more...

Summarizing linear model fits

Getting ready

How to do it...

How it works...

See also

Using linear regression to predict unknown values

Getting ready

How to do it...

How it works...

See also

Generating a diagnostic plot of a fitted model

Getting ready

How to do it...

How it works...

There's more...

Fitting multiple regression

Getting ready

How to do it...

How it works...

Summarizing multiple regression

Getting ready

How to do it...

How it works...

See also

Using multiple regression to predict unknown values

Getting ready

How to do it...

How it works...

See also

Fitting a polynomial regression model with lm

Getting ready

How to do it...

How it works...

There's more...

Fitting a robust linear regression model with rlm

Getting ready

How to do it...

How it works...

There's more...

Studying a case of linear regression on SLID data

Getting ready

How to do it...

How it works...

See also

Applying the Gaussian model for generalized linear regression

Getting ready

How to do it...

How it works...

See also

Applying the Poisson model for generalized linear regression

Getting ready

How to do it...

How it works...

See also

Applying the Binomial model for generalized linear regression

Getting ready

How to do it...

How it works...

See also

Fitting a generalized additive model to data

Getting ready

How to do it...

How it works...

See also

Visualizing a generalized additive model

Getting ready

How to do it...

How it works...

There's more...

Diagnosing a generalized additive model

Getting ready

How to do it...

How it works...

There's more...

Survival Analysis

Introduction

Loading and observing data

Getting ready

How to do it...

How it works...

There's more...

Viewing the summary of survival analysis

Getting ready

How to do it...

How it works...

Visualizing the Survival Curve

Getting ready

How to do it...

How it works...

Using the log-rank test

Getting ready

How to do it...

How it works...

Using the COX proportional hazard model

Getting ready

How to do it...

How it works...

Nelson-Aalen Estimator of cumulative hazard

Getting ready

How to do it...

How it works...

See also

Classification 1 - Tree, Lazy, and Probabilistic

Introduction

Preparing the training and testing datasets

Getting ready

How to do it...

How it works...

There's more...

Building a classification model with recursive partitioning trees

Getting ready

How to do it...

How it works...

See also

Visualizing a recursive partitioning tree

Getting ready

How to do it...

How it works...

See also

Measuring the prediction performance of a recursive partitioning tree

Getting ready

How to do it...

How it works...

See also

Pruning a recursive partitioning tree

Getting ready

How to do it...

How it works...

See also

Handling missing data and split and surrogate variables

Getting ready

How to do it...

How it works...

See also

Building a classification model with a conditional inference tree

Getting ready

How to do it...

How it works...

See also

Control parameters in conditional inference trees

Getting ready

How to do it...

How it works...

See also

Visualizing a conditional inference tree

Getting ready

How to do it...

How it works...

See also

Measuring the prediction performance of a conditional inference tree

Getting ready

How to do it...

How it works...

See also

Classifying data with the k-nearest neighbor classifier

Getting ready

How to do it...

How it works...

See also

Classifying data with logistic regression

Getting ready

How to do it...

How it works...

See also

Classifying data with the Naïve Bayes classifier

Getting ready

How to do it...

How it works...

See also

Classification 2 - Neural Network and SVM

Introduction

Classifying data with a support vector machine

Getting ready

How to do it...

How it works...

See also

Choosing the cost of a support vector machine

Getting ready

How to do it...

How it works...

See also

Visualizing an SVM fit

Getting ready

How to do it...

How it works...

See also

Predicting labels based on a model trained by a support vector machine

Getting ready

How to do it...

How it works...

There's more...

Tuning a support vector machine

Getting ready

How to do it...

How it works...

See also

The basics of neural network

Getting ready

How to do it...

Training a neural network with neuralnet

Getting ready

How to do it...

How it works...

See also

Visualizing a neural network trained by neuralnet

Getting ready

How to do it...

How it works...

See also

Predicting labels based on a model trained by neuralnet

Getting ready

How to do it...

How it works...

See also

Training a neural network with nnet

Getting ready

How to do it...

How it works...

See also

Predicting labels based on a model trained by nnet

Getting ready

How to do it...

How it works...

See also

Model Evaluation

Introduction

Why do models need to be evaluated?

Different methods of model evaluation

Estimating model performance with k-fold cross-validation

Getting ready

How to do it...

How it works...

There's more...

Estimating model performance with Leave One Out Cross Validation

Getting ready

How to do it...

How it works...

See also

Performing cross-validation with the e1071 package

Getting ready

How to do it...

How it works...

See also

Performing cross-validation with the caret package

Getting ready

How to do it...

How it works...

See also

Ranking the variable importance with the caret package

Getting ready

How to do it...

How it works...

There's more...

Ranking the variable importance with the rminer package

Getting ready

How to do it...

How it works...

See also

Finding highly correlated features with the caret package

Getting ready

How to do it...

How it works...

See also

Selecting features using the caret package

Getting ready

How to do it...

How it works...

See also

Measuring the performance of the regression model

Getting ready

How to do it...

How it works...

There's more...

Measuring prediction performance with a confusion matrix

Getting ready

How to do it...

How it works...

See also

Measuring prediction performance using ROCR

Getting ready

How to do it...

How it works...

See also

Comparing an ROC curve using the caret package

Getting ready

How to do it...

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