Machine Learning Essentials: Practical Guide in R

Alboukadel Kassambara

maalis 2018 · STHDA

Tietoa tästä e-kirjasta

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 real word data sets, as well as, for building predictive models.  

The main parts of the book include: A) 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. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) 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). E) 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. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) 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. 

Tietoja kirjoittajasta

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, http://www.sthda.com/english/rpkgs/factoextra), 

- survival analysis (survminer, http://www.sthda.com/english/rpkgs/survminer/),

- correlation analysis (ggcorrplot, http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2), 

- creating publication ready plots in R (ggpubr, http://www.sthda.com/english/rpkgs/ggpubr).

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

1. Practical Guide to Cluster Analysis in R (https://goo.gl/yhhpXh)

2. Practical Guide To Principal Component Methods in R (https://goo.gl/d4Doz9)

3. R Graphics Essentials for Great Data Visualization (https://goo.gl/oT8Ra6)

4. Network Analysis and Visulization in R (https://goo.gl/WBdn4n)