世界第一大栗旬 发表于 2022-1-16 11:07:17

帮忙解释一下这里fit方法的作用

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

请尽可能通俗易懂地解释一下这里的fit方法是干嘛的

18929659582 发表于 2022-1-17 13:07:54

fit(): Method calculates the parameters μ and σ and saves them as internal objects.
解释:简单来说,就是求得训练集X的均值,方差,最大值,最小值,这些训练集X固有的属性。

transform(): Method using these calculated parameters apply the transformation to a particular dataset.
解释:在fit的基础上,进行标准化,降维,归一化等操作(看具体用的是哪个工具,如PCA,StandardScaler等)。

fit_transform(): joins the fit() and transform() method for transformation of dataset.
解释:fit_transform是fit和transform的组合,既包括了训练又包含了转换。
transform()和fit_transform()二者的功能都是对数据进行某种统一处理(比如标准化~N(0,1),将数据缩放(映射)到某个固定区间,归一化,正则化等)

fit_transform(trainData)对部分数据先拟合fit,找到该part的整体指标,如均值、方差、最大值最小值等等(根据具体转换的目的),然后对该trainData进行转换transform,从而实现数据的标准化、归一化等等。
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查看完整版本: 帮忙解释一下这里fit方法的作用