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Histories of Data and the DatabaseEpisode from the podcastHistories of Data and the Database
Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Released Saturday, 19th November 2016
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Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability

Saturday, 19th November 2016
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Matthew Jones from Columbia University delivers a talk titled “Random Forests and Decision Trees: Machine Learning, Empirical Statistics, and the Challenge of Interpretability.” This talk was included in the session titled “Methods and Ambiguities in the Contemporary Age.”

Part of “Histories of Data and the Database,” a conference held at The Huntington Nov. 18–19, 2016.

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From The Podcast

In the age of internet searches and social media, data has become hot—and not for the first time. An international group of historians will consider the promises, fears, practices, and technologies for recording and transmitting data in the 18th century to the present, including the implications for the lives of citizens and subjects. The conference was held at The Huntington on Nov. 18–19, 2016.

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