2.2sklearn.preprocessing.polynomialfeatures Generating Crossover features

Sklearn.preprocessing.PolynomialFeatures Original

The polynomial generation function:sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)
Parameter description:

• degree: Default is 2, polynomial count (same number of times in several equations)
• interaction_only: Whether to include a single argument **n (n>1) feature data ID, default to False, true to remove the case of multiplying itself
• include_bias: whether to include the deviation identifier, which is true by default, or false to indicate that no deviation is included

import numpy as npfrom sklearn.preprocessing import PolynomialFeatures

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

X

array([[0, 1],       [2, 3],       [4, 5]])

poly = PolynomialFeatures(degree = 2)

poly.fit_transform(X)

array([[ 1.,  0.,  1.,  0.,  0.,  1.],       [ 1.,  2.,  3.,  4.,  6.,  9.],       [ 1.,  4.,  5., 16., 20., 25.]])

# 设置参数interaction_only = True，不包含单个自变量****n(n>1)特征数据poly = PolynomialFeatures(degree = 2, interaction_only = True)

poly.fit_transform(X)

array([[ 1.,  0.,  1.,  0.],       [ 1.,  2.,  3.,  6.],       [ 1.,  4.,  5., 20.]])

# 再添加 设置参数include_bias= False，不包含偏差项数据poly = PolynomialFeatures(degree = 2, interaction_only = True, include_bias=False)

poly.fit_transform(X)

array([[ 0.,  1.,  0.],       [ 2.,  3.,  6.],       [ 4.,  5., 20.]])

2.2sklearn.preprocessing.polynomialfeatures Generating Crossover features