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Titlebook: Applied Statistical Learning; With Case Studies in Matthias Schonlau Textbook 2023 The Editor(s) (if applicable) and The Author(s), under e

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21#
發(fā)表于 2025-3-25 05:41:01 | 只看該作者
https://doi.org/10.1007/978-3-031-33390-3Statistical Learning; Machine Learning; Stata; Applications in the Social Sciences; Case Studies; Text Da
22#
發(fā)表于 2025-3-25 10:09:18 | 只看該作者
23#
發(fā)表于 2025-3-25 15:08:17 | 只看該作者
Textbook 2023s of data science in the field. Although mainly intended for upper undergraduate and graduatestudents in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science..
24#
發(fā)表于 2025-3-25 18:34:58 | 只看該作者
25#
發(fā)表于 2025-3-25 22:39:49 | 只看該作者
Statistical Learning: Concepts,f interpretation and prediction. We introduce the bias-variance tradeoff as a central theme in statistical learning. Next, we introduce the Bayes error as the lowest possible error. The Bayes error is of limited use in practice because it requires knowledge of the true functional relationship betwee
26#
發(fā)表于 2025-3-26 03:34:05 | 只看該作者
Statistical Learning: Practical Aspects,cause the true functional relationship is unknown in practice, the Bayes error cannot be computed. Instead, we use different subsets of the data for training and evaluation. There are several techniques for splitting the data into subsets for this purpose. One such technique, cross-validation, uses
27#
發(fā)表于 2025-3-26 08:12:11 | 只看該作者
28#
發(fā)表于 2025-3-26 09:14:05 | 只看該作者
Lasso and Friends,aussian linear regression, choosing an L2 penalty leads to ridge regression and choosing an L1 penalty leads to the Lasso. The same penalties can be applied to logistic regression. Both penalties tend to reduce the magnitude of coefficients. Because the L1 penalty can reduce coefficients to zero, th
29#
發(fā)表于 2025-3-26 14:15:31 | 只看該作者
30#
發(fā)表于 2025-3-26 19:57:46 | 只看該作者
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