class sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)

参数：

degree

interaction_only 默认为False

include_bias 　　表示生成0指数项

>>> import numpy as np

>>> from sklearn.preprocessing import PolynomialFeatures

>>> X = np.arange(6).reshape(3, 2)

>>> X

array([[0, 1],

       [2, 3],

       [4, 5]])

>>> poly = PolynomialFeatures(2)

>>> poly.fit_transform(X)

array([[ 1.,  0.,  1.,  0.,  0.,  1.],

       [ 1.,  2.,  3.,  4.,  6.,  9.],

       [ 1.,  4.,  5., 16., 20., 25.]])

interaction_only=True

>>> X = np.arange(9).reshape(3, 3)

>>> X

array([[0, 1, 2],

       [3, 4, 5],

       [6, 7, 8]])

>>> poly = PolynomialFeatures(degree=3, interaction_only=True)

>>> poly.fit_transform(X)

array([[  1.,   0.,   1.,   2.,   0.,   0.,   2.,   0.],

       [  1.,   3.,   4.,   5.,  12.,  15.,  20.,  60.],

       [  1.,   6.,   7.,   8.,  42.,  48.,  56., 336.]])

fit(X[, y])    Compute number of output features.

fit_transform(X[, y])    Fit to data, then transform it.

get_feature_names([input_features])    Return feature names for output features

get_params([deep])    Get parameters for this estimator.

set_params(**params)    Set the parameters of this estimator.

transform(X)    Transform data to polynomial features