**Preface to "Entropy in Real-World Datasets and Its Impact on Machine Learning"**

Nowadays, machine learning is considered as a group of various methods used to solve the most complex real-world problems. Its usability is crucial in fields such as medicine, finance, text mining, image analysis, and more. Among the most prominent examples of machine-learning-related methods, we can find ensemble methods, multicriteria evolutionary algorithms, deep learning in neural networks, etc. Here, we are particularly interested in subjects connecting the entropy of datasets and the effectiveness of machine learning algorithms.

The main aspect of this book is devoted to entropy in the ever-growing amount of data available for users. Concepts such as big data and data streams are still increasingly gaining attention. The efficiency of classical methods seems to create debate amongst these types of data; thus, we believe that there is a necessity for continuous improvements in what is widely understood as machine learning. This book is dedicated to the analysis of real-world datasets, in particular, in terms of the entropy present in them and the impact on machine learning.

The topic of the book is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this book contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the book also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker.

> **Jan Kozak and Przemysław Juszczuk** *Editors*
