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Machine learning with R cookbook analyze data and build predictive models

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...

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Bibliographic Details
Main Authors: Bhatia, AshishSingh, Chiu, Yu-Wei (Author)
Format: eBook
Language:English
Published: Birmingham, UK Packt Publishing 2017
Edition:Second edition
Subjects:
R (langage De Programmation)
Mathematical Statistics / Data Processing / Fast
Computers. / Neural Networks / Bisacsh
R (computer Program Language) / Fast
Computers. / Data Processing / Bisacsh
Computers. / Intelligence (ai) & Semantics / Bisacsh
R (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh2002004407
Mathematical Statistics / Data Processing / Http://id.loc.gov/authorities/subjects/sh85082137
Statistique Mathématique / Informatique
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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100 1 |a Bhatia, AshishSingh 
245 0 0 |a Machine learning with R cookbook  |b analyze data and build predictive models  |c AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu) 
246 3 1 |a Analyze data and build predictive models 
250 |a Second edition 
260 |a Birmingham, UK  |b Packt Publishing  |c 2017 
300 |a 1 volume  |b illustrations 
505 0 |a How to do it... -- Predicting values from datasets -- Getting ready -- How to do it... -- How it works... -- Chapter 3: 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 -- Chapter 4: 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 
505 0 |a 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 -- Chapter 8: 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 
505 0 |a 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... -- Chapter 6: 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 -- Chapter 7: 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 
505 0 |a Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 
505 0 |a 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 -- Chapter 5: 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 
653 |a R (Langage de programmation) 
653 |a Mathematical statistics / Data processing / fast 
653 |a COMPUTERS. / Neural Networks / bisacsh 
653 |a R (Computer program language) / fast 
653 |a COMPUTERS. / Data Processing / bisacsh 
653 |a COMPUTERS. / Intelligence (AI) & Semantics / bisacsh 
653 |a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407 
653 |a Mathematical statistics / Data processing / http://id.loc.gov/authorities/subjects/sh85082137 
653 |a Statistique mathématique / Informatique 
700 1 |a Chiu, Yu-Wei  |e author 
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989 |b OREILLY  |a O'Reilly 
500 |a Includes index 
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856 4 0 |u https://learning.oreilly.com/library/view/~/9781787284395/?ar  |x Verlag  |3 Volltext 
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082 0 |a 519.502855133 
082 0 |a 519.5 
520 |a 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. Yo.. 
520 |a 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.  
520 |a 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.  

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