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Feature selected
特徵選擇 (Feature selection)


在機器學習和統計學中,特徵選擇也被稱為變量選擇、屬性選擇 或變量子集選擇 。它是指:為了構建模型而選擇相關特徵子集的過程。使用特徵選擇技術有三個原因: 簡化模型,使之更易於被研究人員或用戶理解, 縮短訓練時間, 改善通用性、降低過擬合 维基百科


2019年11月27日 — Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the ...
Univariate feature selection works by selecting the best features based on univariate statistical tests. It can be seen as a preprocessing step to an estimator.
2018年10月28日 — Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or ...
2019年7月27日 — Filter based: We specify some metric and based on that filter features. · Wrapper-based: Wrapper methods consider the selection of a set of ...
Feature selection is the process of isolating the most consistent, non-redundant, and relevant features to use in model construction.
2020年10月10日 — Feature selection is used to find the best set of features that allows one to build useful models. Learn feature selection techniques in ML.
2016年12月1日 — Filter methods are generally used as a preprocessing step. The selection of features is independent of any machine learning algorithms. Instead, ...
作者:Y Masoudi-Sobhanzadeh2019被引用次数:50 — FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular ...

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