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(一) 定义
TF-IDF(Term Frequency-Inverse DocumentFrequency, 词频-逆文件频率)，一种用于资讯检索和资讯探勘的常用加权技术。
TF-IDF是一种统计方法，用以评估一字词对于一个文件集或一个语料库中的其中一份文件的重要程度。字词的重要性随着它在文件中
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<time title="修改时间：2019-03-31 02:31:15" itemprop="dateModified" datetime="2019-03-31T02:31:15+08:00">2019-03-31</time>
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<span itemprop="about" itemscope="" itemtype="http://schema.org/Thing"><a href="/categories/数据挖掘/" itemprop="url" rel="index"><span itemprop="name">数据挖掘</span></a></span>
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(一) 特征缩放定义
特征缩放是用来统一资料中的自变项或特征范围的方法，在资料处理中，通常会被使用在资料前处理这个步骤。因为在原始的资料中，各变数的范围大不相同。
(二) 特征缩放的目的
对于大多数的机器学习算法和优化算法来说，将特征值缩放到相同区间可以使得获取性能更好的模型。
例如：
（a）有
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