klearn.preprocessing.PolynomialFeatures学习

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:

degree : integer The degree of the polynomial features. Default = 2. interaction_only : boolean, default = False If true, only interaction features are produced: features that are products of at most degreedistinct input features (so not x[1] ** 2, x[0] * x[2] ** 3, etc.). include_bias : boolean If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model).

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