图 书 馆
特征重要性排名测量The Feature Importance Ranking Measure |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_kramer_firm/ |
| 主讲教师: | Nicole Krämer |
| 开课单位: | 柏林工业大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importanc Ranking Measure(FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations. |
| 关 键 词: | 特征选择; 机器学习; 线性模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-31：nkq |
| 阅读次数: | 104 |