Books

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. With examples in R and Python.

Website of the english version

A step-by-step introduction to the most important methods of explainable machine learning (XAI). Learn the intuitions, mathematical foundations and application examples of LIME, SHAP, Break Down, Partial Dependece, Permutational Variable Importance, Accumulated Local Effects and other popular techniques. Methods are enhanced with examples in R and Python using the DALEX library to explore any predictive model.

The Hitchhiker’s Guide to Responsible Machine Learning

Website of the english version

Strona www wersji polskiej

A one-of-a-kind 52-page story about responsible machine learning. Beta and Bit use decision trees, random forests, and AutoML tools to build a risk model after a covid infection, and then use explainable artificial intelligence tools to analyze the behavior of that model. The description of the data analysis process is intertwined with descriptions of ML tools and code snippets. All examples are fully reproducible!

Chaos Game with examples in R, Python and Julia.

Website of the english version

Strona www wersji polskiej

Are you curious about fractals? The Chaos Game is the book for you. You will learn the mathematical basis behind these figures, find out what algorithm can be used to code them, write code in your favourite programming language (Python, R, Julia?) and also explore the bibliographies of three mathematicians associated with the development of mathematics around these shapes. This is the next book in the Beta Bit series for anyone interested in computational mathematics and data analysis.

Przewodnik po pakiecie R

Strona www wersji polskiej

The Guide to the R package was the first published Polish book focused on the R language. The current fourth edition consists of four parts: Basics of using R (+tidyverse, shiny, knitr and other goodies), Programming in R (object-oriented, package development, class system), Statistics with R (statistical tests, models, exploration techniques) and Visualization with R (graphics, lattice and ggplot2 packages).

Eseje o sztuce wizualizacji danych

Strona www wersji polskiej

Discover! Reveal! Explain! These three roles can be fulfilled by good statistical graphics. Good means understandable, faithful to the data, aesthetic. How to create such graphics? A collection of essays on the art of displaying data systematises knowledge useful in designing and producing good data visualisations. It is not easy. On the one hand, we can fall into the trap of a colourful mush full of numbers, which is sometimes proudly called infographics. On the other hand, we can fall into the trap of graphics that perfectly reproduce the complexity of numbers, and thus completely incomprehensible. Somewhere in the middle is a graphic that explains, that informs, that is aesthetically pleasing and informative.

Analiza danych z programem R

Strona www wersji polskiej

An academic textbook describing estimation and testing topics for linear models with fixed effects, random effects and mixed effects. The theoretical introduction is complemented by numerous examples for one-way and multivariate ANOVA, one and multiple random components. The examples focus on biological and medical applications and are based on real analyses of real data.