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3. Nov. 2023 · Learn how entropy measures the level of disorder or uncertainty in a dataset and how it is used to optimize decision trees. Explore the concept of entropy in information theory, its formula, and its applications in machine learning.
2. Nov. 2022 · Learn how to use entropy, information gain, Gini index and CCP pruning to build decision trees for classification problems. This tutorial covers the basics of decision trees, splitting criteria, tree pruning and prediction.
This issue explores the relationship between entropy and data mining, with a focus on improving data mining algorithms and applications. It includes papers on entropy metrics, feature selection, time series analysis, network entropy, and more.
A chapter from a book series on interactive knowledge discovery and data mining in biomedical informatics. It reviews four entropy methods for data analysis and discusses their applicability and challenges.
- Andreas Holzinger, Matthias Hörtenhuber, Christopher C. Mayer, Martin Bachler, Siegfried Wassertheur...
- 2014
11. Jan. 2019 · Learn how entropy measures disorder or uncertainty in a target variable and how information gain measures the reduction of uncertainty given a feature. See how decision trees use entropy and information gain to choose the best feature to split on.
A special issue of Entropy journal that covers various aspects of entropy measures for data analysis, such as parameter choice, feature extraction, learning entropy, coupling and similarity analysis. The issue includes papers on applications of entropy measures in physiology, econophysics, engineering, machine learning and more.
1. Okt. 2022 · Data mining is a complex process that allows for the performance of analyses and the acquisition of knowledge from data by using different methods, depending on the aim and kind of data. Among data mining tools, decision rules, decision trees, reducts, and association rules can be used.
