图 书 馆
可解释机器学习算法Algorithms for Interpretable Machine Learning |
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| 课程网址: | http://videolectures.net/kdd2014_rudin_machine_learning/ |
| 主讲教师: | Cynthia Rudin |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  在许多应用程序领域中，使预测建模具有透明度非常重要。领域专家并不倾向于使用“黑匣子”预测模型。他们想了解如何进行预测，并且可能更喜欢模拟人类专家可能做出决策的方式的模型，该模型具有一些重要的变量以及做出特定预测的明确的令人信服的理由。我将讨论有关决策表和稀疏整数线性模型的可解释预测模型的最新工作。我将描述几种方法，包括基于离散优化的算法和基于贝叶斯分析的算法。我将展示一些可解释的模型，这些模型可用于医疗患者的中风预测和非家庭护理中引发的年轻人暴力犯罪的预测。 |
| 课程简介: | It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction. I will discuss recent work on interpretable predictive modeling with decision lists and sparse integer linear models. I will describe several approaches, including an algorithm based on discrete optimization, and an algorithm based on Bayesian analysis. I will show examples of interpretable models for stroke prediction in medical patients and prediction of violent crime in young people raised in out-of-home care. |
| 关 键 词: | 学习算法; 预测建模; 离散优化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-28：zyk |
| 最后编审: | 2021-09-15：zyk |
| 阅读次数: | 57 |