使用fit_transform（）和transform（）
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
我所知道的是fit（）方法计算功能的均值和标准偏差，然后transform（）方法使用它们将功能转换为新的缩放功能。 fit_transform（）无非是调用fit（）＆amp; transform（）单行中的方法。
但是这里为什么我们只调用fit（）用于培训数据而不是用于测试数据？
这是否意味着我们正在使用Mean＆amp;培训数据的标准偏差以转换我们的测试数据？
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
What I know is fit() method calculates mean and standard deviation of the feature and then transform() method uses them to transform the feature into a new scaled feature. fit_transform() is nothing but calling fit() & transform() method in a single line.
But here why are we only calling fit() for training data and not for testing data??
Does that means we are using mean & standard deviation of training data to transform our testing data ??
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fit计算用于以后缩放的平均值和STDEV，请注意，这只是一个未完成缩放的计算。转换使用先前计算的平均值和STDEV来扩展数据（从所有值中减去平均值，然后将其除以STDEV）。fit_transform同时进行。因此，您只需1行代码即可完成。对于
x_train数据集，我们执行fit_transform，因为我们需要计算均值和stdev，然后使用它来扩展x_train数据集。对于x_test数据集，由于我们已经具有均值和stdev，因此我们只执行转换部分。edit ：
x_test数据应完全看不见和未知（即，从它们中提取任何信息），所以我们只能从x_train中得出信息。我们之所> y_test 和y_pred。顺便说一句，如果火车/测试数据在没有偏差的情况下正确拆分，并且数据足够大，则两个数据集将与总体平均值和STDEV具有相同的近似值。
fitcomputes the mean and stdev to be used for later scaling, note it's just a computation with no scaling done.transformuses the previously computed mean and stdev to scale the data (subtract mean from all values and then divide it by stdev).fit_transformdoes both at the same time. So you can do it with just 1 line of code.For
X_traindataset, we dofit_transformbecause we need to compute mean and stdev, and then use it to scale theX_traindataset. ForX_testdataset, since we already have the mean and stdev, we only do the transformation part.Edit:
X_testdata should be totally unseen and unknown (ie, no info is extracted from them), so we can only derive info fromX_train. The reason why we apply the derived mean and stdev (fromX_train) to transformX_testas well, is to have the same "apple-to-apple" comparison fory_testandy_pred.By the way, if the train/test data is split properly without bias, and that the data is sufficiently large, both datasets would have the same approximation to the population mean and stdev.