多项式特征处理
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
| Parameters: |
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案例1：
>>> 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.]])
方法：
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