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