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Machine Learning Essentials

Sat, 10 Mar 2018 15:37:00 +0100

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.

This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models.

The main parts of the book include:

Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods.
Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies.
Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines.
Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting).
Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables.
Model validation and evaluation techniques for measuring the performance of a predictive model.
Model diagnostics for detecting and fixing a potential problems in a predictive model.

The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

Covers machine learning algorithm and implementation
Key mathematical concepts are presented
Short, self-contained chapters with practical examples.

At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter.

Where to find the book?:

Download the PDF through payhip
Read the ebook on google play
Order a physical copy from amazon
(Download the book preview)

Network Analysis and Visualization in R

Mon, 27 Nov 2017 08:17:00 +0100

Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks.

This book provides a quick start guide to network analysis and visualization in R.

You'll learn, how to:

- Create static and interactive network graphs using modern R packages.
- Change the layout of network graphs.
- Detect important or central entities in a network graph.
- Detect community (or cluster) in a network.

R Graphics Essentials for Great Data Visualization: 200 Practical Examples You Want to Know for Data Science

Thu, 16 Nov 2017 07:43:00 +0100

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data.

In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems.

Next, we provide practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics.

With this book, you 'll learn:

- How to quickly create beautiful graphics using ggplot2 packages

- How to properly customize and annotate the plots

- Type of graphics for visualizing categorical and continuous variables

- How to add automatically p-values to box plots, bar plots and alternatives

- How to add marginal density plots and correlation coefficients to scatter plots

- Key methods for analyzing and visualizing multivariate data

- R functions and packages for plotting time series data

- How to combine multiple plots on one page to create production-quality figures.

Exploratory Multivariate Analysis by Example Using R

Thu, 24 Aug 2017 18:53:00 +0200

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications.

It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods.

The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical.

They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.

The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

Practical Guide to Principal Component Methods in R

Wed, 23 Aug 2017 11:12:00 +0200

Introduction

Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced.

This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.

Where to find the book:

Download the PDF through payhip
Read the ebook on google play
Order a physical copy from amazon
(Download the book preview)

The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.

There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition.

However, the result is presented differently depending on the used package.

To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and principal component methods - we developed an easy-to-use R package named factoextra (official online documentation: https://www.sthda.com/english/rpkgs/factoextra).

No matter which package you decide to use for computing principal component methods, the factoextra R package can help to extract easily, in a human readable data format, the analysis results from the different packages mentioned above. factoextra provides also convenient solutions to create ggplot2-based beautiful graphs.

Methods, which outputs can be visualized using the factoextra package are shown in the figure below:

In this book, we’ll use mainly:

the FactoMineR package to compute principal component methods;
and the factoextra package for extracting, visualizing and interpreting the results.

The other packages - ade4, ExPosition, etc - will be also presented briefly.

How this book is organized

This book contains 4 parts.

Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra.

Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include:

Principal Component Analysis (PCA, for continuous variables),
Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)
Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).

In Part III, you’ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:

Factor Analysis of Mixed Data (FAMD) and,
Multiple Factor Analysis (MFA).

Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables

Key features of this book

This book presents the basic principles of the different methods and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

Covers principal component methods and implementation in R
Highlights the most important information in your data set using ggplot2-based elegant visualization
Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy reference

At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter. Additionally, we provide links to other resources and to our hand-curated list of videos on principal component methods for further learning.

Examples of plots

Some examples of plots generated in this book are shown hereafter. You’ll learn how to create, customize and interpret these plots.

Eigenvalues/variances of principal components. Proportion of information retained by each principal component.

PCA - Graph of variables:

Control variable colors using their contributions to the principal components.

Highlight the most contributing variables to each principal dimension:

PCA - Graph of individuals:

Control automatically the color of individuals using the cos2 (the quality of the individuals on the factor map)

Change the point size according to the cos2 of the corresponding individuals:

PCA - Biplot of individuals and variables

Correspondence analysis. Association between categorical variables.

FAMD/MFA - Analyzing mixed and structured data

Clustering on principal components

Book preview

Download the preview of the book at: Principal Component Methods in R (Book preview)

Order now

About the author

Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).

He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.

He created a bioinformatics web-tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization.

He developed also a training website on data science, named STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com/english), which contains many tutorials on data analysis and visualization using R software and packages.

He is the author of many popular R packages for:

multivariate data analysis (factoextra, https://www.sthda.com/english/rpkgs/factoextra),
survival analysis (survminer, https://www.sthda.com/english/rpkgs/survminer/),
correlation analysis (ggcorrplot, https://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),
creating publication ready plots in R (ggpubr, https://www.sthda.com/english/rpkgs/ggpubr).

Recently, he published three books on data analysis and visualization:

Practical Guide to Cluster Analysis in R (https://goo.gl/DmJ5y5)
Guide to Create Beautiful Graphics in R (https://goo.gl/vJ0OYb).
Complete Guide to 3D Plots in R (https://goo.gl/v5gwl0).

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

Wed, 02 Aug 2017 22:12:00 +0200

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master.

This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started.

If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.

- Create vectors, handle variables, and perform other basic functions

- Input and output data

- Tackle data structures such as matrices, lists, factors, and data frames

- Work with probability, probability distributions, and random variables

- Calculate statistics and confidence intervals, and perform statistical tests

- Create a variety of graphic displays

- Build statistical models with linear regressions and analysis of variance (ANOVA)

- Explore advanced statistical techniques, such as finding clusters in your data

"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

Author: Paul Teetor

R Graphics Cookbook: Practical Recipes for Visualizing Data

Wed, 02 Aug 2017 22:00:00 +0200

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.

- Use R’s default graphics for quick exploration of data

- Create a variety of bar graphs, line graphs, and scatter plots

- Summarize data distributions with histograms, density curves, box plots, and other examples

- Provide annotations to help viewers interpret data

- Control the overall appearance of graphics

- Render data groups alongside each other for easy comparison

- Use colors in plots

- Create network graphs, heat maps, and 3D scatter plots

- Structure data for graphing

Author: Winston Chang

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Tue, 01 Aug 2017 07:58:00 +0200

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning.

Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks — scikit-learn and TensorFlow — author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

- Explore the machine learning landscape, particularly neural nets

- Use scikit-learn to track an example machine-learning project end-to-end

- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods

- Use the TensorFlow library to build and train neural nets

- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning

- Learn techniques for training and scaling deep neural nets

- Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Author: Aurélien Géron

Practical Guide to Cluster Analysis in R

Tue, 01 Aug 2017 00:14:00 +0200

Introduction

Large amounts of data are collected every day from satellite images, bio-medical, security, marketing, web search, geo-spatial or other automatic equipment. Mining knowledge from these big data far exceeds human’s abilities.

Clustering is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest.

In the litterature, it is referred as “pattern recognition” or “unsupervised machine learning” - “unsupervised” because we are not guided by a priori ideas of which variables or samples belong in which clusters. “Learning” because the machine algorithm “learns” how to cluster.

Cluster analysis is popular in many fields, including:

In cancer research for classifying patients into subgroups according their gene expression profile. This can be useful for identifying the molecular profile of patients with good or bad prognostic, as well as for understanding the disease.
In marketing for market segmentation by identifying subgroups of customers with similar profiles and who might be receptive to a particular form of advertising.
In City-planning for identifying groups of houses according to their type, value and location.

This book provides a practical guide to unsupervised machine learning or cluster analysis using R software. Additionally, we developped an R package named factoextra to create, easily, a ggplot2-based elegant plots of cluster analysis results. Factoextra official online documentation: https://www.sthda.com/english/rpkgs/factoextra

Where to find the book:

Download the PDF through payhip
Read the ebook on google play
Order a physical copy from amazon
(Download the book preview)

Key features of this book

Although there are several good books on unsupervised machine learning/clustering and related topics, we felt that many of them are either too high-level, theoretical or too advanced. Our goal was to write a practical guide to cluster analysis, elegant visualization and interpretation.

The main parts of the book include:

distance measures,
partitioning clustering,
hierarchical clustering,
cluster validation methods, as well as,
advanced clustering methods such as fuzzy clustering, density-based clustering and model-based clustering.

The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

Covers clustering algorithm and implementation
Key mathematical concepts are presented
Short, self-contained chapters with practical examples. This means that, you don’t need to read the different chapters in sequence.

At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter.

How this book is organized?

This book contains 5 parts. Part I (Chapter 1 - 3) provides a quick introduction to R (chapter 1) and presents required R packages and data format (Chapter 2) for clustering analysis and visualization.

The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects. Chapter 3 covers the common distance measures used for assessing similarity between observations.

Part II starts with partitioning clustering methods, which include:

K-means clustering (Chapter 4),
K-Medoids or PAM (partitioning around medoids) algorithm (Chapter 5) and
CLARA algorithms (Chapter 6).

Partitioning clustering approaches subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst.

In Part III, we consider agglomerative hierarchical clustering method, which is an alternative approach to partitionning clustering for identifying groups in a data set. It does not require to pre-specify the number of clusters to be generated. The result of hierarchical clustering is a tree-based representation of the objects, which is also known as dendrogram (see the figure below).

In this part, we describe how to compute, visualize, interpret and compare dendrograms:

Agglomerative clustering (Chapter 7)
- Algorithm and steps
- Verify the cluster tree
- Cut the dendrogram into different groups
Compare dendrograms (Chapter 8)
- Visual comparison of two dendrograms
- Correlation matrix between a list of dendrograms
Visualize dendrograms (Chapter 9)
- Case of small data sets
- Case of dendrogram with large data sets: zoom, sub-tree, PDF
- Customize dendrograms using dendextend
Heatmap: static and interactive (Chapter 10)
- R base heat maps
- Pretty heat maps
- Interactive heat maps
- Complex heatmap
- Real application: gene expression data

In this section, you will learn how to generate and interpret the following plots.

Standard dendrogram with filled rectangle around clusters:

Compare two dendrograms:

Heatmap:

Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Before applying any clustering algorithm to a data set, the first thing to do is to assess the clustering tendency. That is, whether applying clustering is suitable for the data. If yes, then how many clusters are there. Next, you can perform hierarchical clustering or partitioning clustering (with a pre-specified number of clusters). Finally, you can use a number of measures, described in this chapter, to evaluate the goodness of the clustering results.

The different chapters included in part IV are organized as follow:

Assessing clustering tendency (Chapter 11)
Determining the optimal number of clusters (Chapter 12)
Cluster validation statistics (Chapter 13)
Choosing the best clustering algorithms (Chapter 14)
Computing p-value for hierarchical clustering (Chapter 15)

In this section, you’ll learn how to create and interpret the plots hereafter.

Visual assessment of clustering tendency (left panel): Clustering tendency is detected in a visual form by counting the number of square shaped dark blocks along the diagonal in the image.
Determine the optimal number of clusters (right panel) in a data set using the gap statistics.

## Clustering k = 1,2,..., K.max (= 10): .. done
## Bootstrapping, b = 1,2,..., B (= 100)  [one "." per sample]:
## .................................................. 50 
## .................................................. 100

Cluster validation using the silhouette coefficient (Si): A value of Si close to 1 indicates that the object is well clustered. A value of Si close to -1 indicates that the object is poorly clustered. The figure below shows the silhouette plot of a k-means clustering.

Part V presents advanced clustering methods, including:

Hierarchical k-means clustering (Chapter 16)
Fuzzy clustering (Chapter 17)
Model-based clustering (Chapter 18)
DBSCAN: Density-Based Clustering (Chapter 19)

The hierarchical k-means clustering is an hybrid approach for improving k-means results.

In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster.

In model-based clustering, the data are viewed as coming from a distribution that is mixture of two ore more clusters. It finds best fit of models to data and estimates the number of clusters.

The density-based clustering (DBSCAN is a partitioning method that has been introduced in Ester et al. (1996). It can find out clusters of different shapes and sizes from data containing noise and outliers.

Book preview

Download the preview of the book at: Practical Guide to Cluster Analysis in R (Book preview)

Order now

About the author

Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).

He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.

He created a bioinformatics web-tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization.

He is the author of many popular R packages for:

multivariate data analysis (factoextra, https://www.sthda.com/english/rpkgs/factoextra),
survival analysis (survminer, https://www.sthda.com/english/rpkgs/survminer/),
correlation analysis (ggcorrplot, https://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),
creating publication ready plots in R (ggpubr, https://www.sthda.com/english/rpkgs/ggpubr).

He is the author three four on data analysis and visualization:

Practical Guide to Principal Component Methods in R (https://goo.gl/JHvjuK)
Practical Guide to Cluster Analysis in R (https://goo.gl/ZRUY48)
Guide to Create Beautiful Graphics in R (https://goo.gl/vJ0OYb).
Complete Guide to 3D Plots in R (https://goo.gl/v5gwl0).

R in Action: Data Analysis and Graphics with R

Mon, 31 Jul 2017 16:49:00 +0200

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods.

You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Author: Robert Kabacoff