当当云阅读 > 进口书 > 外文原版书 > 电脑/网络 > Machine Learning with R Cookbook - Second Edition

| | 手机阅读

扫描下载当当云阅读App

Machine Learning with R Cookbook - Second Edition电子书

售价：¥

3人正在读 | 0人评论

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

温馨提示：数字商品不支持退换货，不提供源文件，不支持导出打印

为你推荐

读书简介
目录
累计评论(0条)

读书简介
目录
累计评论(0条)

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

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

See also

Displaying transactions and associations

Getting ready

How to do it...

How it works...

See also

Mining associations with the Apriori rule

Getting ready

How to do it...

How it works...

See also

Pruning redundant rules

Getting ready

How to do it...

How it works...

See also

Visualizing association rules

Getting ready

How to do it...

How it works...

See also

Mining frequent itemsets with Eclat

Getting ready

How to do it...

How it works...

See also

Creating transactions with temporal information

Getting ready

How to do it...

How it works...

See also

Mining frequent sequential patterns with cSPADE

Getting ready

How to do it...

How it works...

See also

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...

See also

Dimension Reduction

Introduction

Why to reduce the dimension?

Performing feature selection with FSelector

Getting ready

How to do it...

How it works...

See also

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...

See also

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...

See also

Compressing images with SVD

Getting ready

How to do it...

How it works...

See also

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...

See also

Big Data Analysis (R and Hadoop)

Introduction

Preparing the RHadoop environment

Getting ready

How to do it...

How it works...

See also

Installing rmr2

Getting ready

How to do it...

How it works...

See also

Installing rhdfs

Getting ready

How to do it...

How it works...

See also

Operating HDFS with rhdfs

Getting ready

How to do it...

How it works...

See also

Implementing a word count problem with RHadoop

Getting ready

How to do it...

How it works...

See also

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

Getting ready

How to do it...

How it works...

See also

Testing and debugging the rmr2 program

Getting ready

How to do it...

How it works...

See also

Installing plyrmr

Getting ready

How to do it...

How it works...

See also

Manipulating data with plyrmr

Getting ready

How to do it...

How it works...

See also

Conducting machine learning with RHadoop

Getting ready

How to do it...

How it works...

See also

Configuring RHadoop clusters on Amazon EMR

Getting ready

How to do it...

How it works...

See also

累计评论(0条) 0个书友正在讨论这本书 发表评论

发表评论

发表评论，分享你的想法吧！

发表评论

买过这本书的人还买过

读了这本书的人还在读

支持设备

BeagleBone By Example ￥71.93

Jayakarthigeyan Prabakar

￥71.93

Python Data Analysis Cookbook ￥90.46

Ivan Idris

￥90.46

Solidity Programming Essentials ￥63.21

Ritesh Modi

￥63.21

Hands-On MQTT Programming with Python ￥63.21

Gaston C. Hillar

￥63.21

Mastering pandas for Finance ￥80.65

Michael Heydt

￥80.65

Ceph Cookbook ￥80.65

Karan Singh

￥80.65

Matplotlib for Python Developers ￥80.65

Sandro Tosi

￥80.65

Python for Secret Agents ￥50.13

Steven F. Lott

￥50.13

Python Parallel Programming Cookbook ￥90.46

Giancarlo Zaccone

￥90.46

Instant Debian: Build a Web Server ￥45.77

Jose Miguel Parrella

￥45.77

更多同类图书 >