Scaling Data In Scikit-learn SVM

While libsvm provides tools for scaling data, with Scikit-Learn (which should be based upon libSVM for the SVC classifier) I find no way to scale my data. Basically I want to use

Solution 1:

You have that functionality in sklearn.preprocessing:

>>> from sklearn import preprocessing
>>> X = [[ 1., -1.,  2.],
...      [ 2.,  0.,  0.],
...      [ 0.,  1., -1.]]
>>> X_scaled = preprocessing.scale(X)

>>> X_scaled                                          
array([[ 0.  ..., -1.22...,  1.33...],
       [ 1.22...,  0.  ..., -0.26...],
       [-1.22...,  1.22..., -1.06...]])

The data will then have zero mean and unit variance.

Solution 2:

You can also try StandardScalerfor datascaling :

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain) # where X is your data to be scaled
Xtrain = scaler.transform(Xtrain)