Cause Effect Pairs in Machine Learning is popular PDF and ePub book, written by Isabelle Guyon in 2019-10-22, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Cause Effect Pairs in Machine Learning can be Read Online from any device for your convenience.
Cause Effect Pairs in Machine Learning Book PDF Summary
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
Detail Book of Cause Effect Pairs in Machine Learning PDF
- Author : Isabelle Guyon
- Release : 22 October 2019
- Publisher : Springer Nature
- ISBN : 9783030218102
- Genre : Computers
- Total Page : 372 pages
- Language : English
- PDF File Size : 21,6 Mb
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