# F

**Feature (Feature Selection, Feature Learning)（特征-特征选择、特征学习）**

用作模型输入的变量。

**Feature Learning（特征学习）**

旨在自动从原始数据中发现特征检测或分类所需的表示的一组技术。

**False Positive（误报）**

由于结果在虚无假设原本不应该存在的情况下拒绝虚无假设而导致的误差。

**False Negative（漏报）**

由于结果在虚无假设应该存在的情况下未拒绝虚无假设而导致的误差。

**Feed-Forward (Neural) Networks（前馈神经网络）**

一种人工神经网络，其中神经元之间的连接不会向后移动或形成循环。

**F-Score（F 得分）**

衡量模型准确性的一个指标，它会考量准确率和召回率来计算得分。更具体地说，F 得分是准确率和召回率的调和平均值，该平均值的最大值为 1（完美的准确率和召回率），最小值为 0。


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