Editing Machine learning (section)

=== Statistics ===
Machine learning and [[statistics]] are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population [[Statistical inference|inferences]] from a [[Sample (statistics)|sample]], while machine learning finds generalisable predictive patterns.<ref>{{cite journal |first1=Danilo |last1=Bzdok |first2=Naomi |last2=Altman |author-link2=Naomi Altman |first3=Martin |last3=Krzywinski |title=Statistics versus Machine Learning |journal=[[Nature Methods]] |volume=15 |issue=4 |pages=233–234 |year=2018 |doi=10.1038/nmeth.4642 |pmid=30100822 |pmc=6082636 }}</ref> According to [[Michael I. Jordan]], the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.<ref name="mi jordan ama">{{cite web|url=https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt?context=3|title=statistics and machine learning|publisher=reddit|date=10 September 2014|access-date=1 October 2014|author=Michael I. Jordan|author-link=Michael I. Jordan|archive-date=18 October 2017|archive-url=https://web.archive.org/web/20171018192328/https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt/?context=3|url-status=live}}</ref> He also suggested the term [[data science]] as a placeholder to call the overall field.<ref name="mi jordan ama" />

Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.<ref>Hung et al. Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. JAMA Surg. 2018</ref>

[[Leo Breiman]] distinguished two statistical modelling paradigms: data model and algorithmic model,<ref name="Cornell-University-Library-2001">{{cite journal|url=http://projecteuclid.org/download/pdf_1/euclid.ss/1009213726|title=Breiman: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)|author=Cornell University Library|journal=Statistical Science|date=August 2001|volume=16|issue=3|doi=10.1214/ss/1009213726|s2cid=62729017|access-date=8 August 2015|archive-date=26 June 2017|archive-url=https://web.archive.org/web/20170626042637/http://projecteuclid.org/download/pdf_1/euclid.ss/1009213726|url-status=live|doi-access=free}}</ref> wherein "algorithmic model" means more or less the machine learning algorithms like [[Random forest|Random Forest]].

Some statisticians have adopted methods from machine learning, leading to a combined field that they call ''statistical learning''.<ref name="islr">{{cite book |author1=Gareth James |author2=Daniela Witten |author3=Trevor Hastie |author4=Robert Tibshirani |title=An Introduction to Statistical Learning |publisher=Springer |year=2013 |url=http://www-bcf.usc.edu/~gareth/ISL/ |page=vii |access-date=25 October 2014 |archive-date=23 June 2019 |archive-url=https://web.archive.org/web/20190623150237/http://www-bcf.usc.edu/~gareth/ISL/ |url-status=live }}</ref>