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feature scaling:缩小或扩大feature的值,使所有的feature处于类似的范围,这样进行gradient descnet时更快趋向最小值.因为不同的feature的范围相差很大时,它的cost function的等值线是椭圆的,但当它们相近时,等值线接近于圆,这样趋向最小值是,圆更快,椭圆更曲折,趋向更慢.(一般在+3到-3的范围内即可) mean normalization:…
假设有一段文本:"I have a cat, his name is Huzihu. Huzihu is really cute and friendly. We are good friends." 那么怎么提取这段文本的特征呢? 一个简单的方法就是使用词袋模型(bag of words model).选定文本内一定的词放入词袋,统计词袋内所有词在文本中出现的次数(忽略语法和单词出现的顺序),将其用向量的形式表示出来. 词频统计可以用scikit-learn的CountVectori…
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@(131 - Machine Learning | 机器学习) 1 Feature Scaling transforms features to have range [0,1] according to the formula $x' = \frac{x-x_{min}}{x_{max}-x_{min}} $ 1.1 Sklearn - MinMaxScaler from sklearn.preprocessing import MinMaxScaler import numpy weigh…
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