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商品編號： 9-622-101 出版日期： 2022/03/23 作者姓名： Bojinov, Iavor;Parzen, Michael;Hamilton, Paul J. 商品類別： Other 商品規格： 42p 再版日期： 2025/01/06 地域： 產業： 個案年度： -
商品敘述：
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional statistical modeling and machine learning. Next, the note covers two models used for classification, logistic regression and decision trees. After introducing these two models, the note explains how train, validation, and holdout sets (and k-fold cross validation) are used to tune and evaluate different models. Finally, the note concludes with a discussion of different performance metrics (ROC cruves, confusion matrices, log loss) that are used to evaluate classification models.
涵蓋領域：
Data science
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