importnumpyasnp X=np.array([[1,2], [3,4]]) poly=PolynomialFeatures(degree=2,include_bias=False) X_poly=poly.fit_transform(X) print(X_poly) # 输出: [[ 1. 2. 1. 2. 4.] # [3. 4. 9. 12. 16.]] pr...
的df数据框: pf = PolynomialFeatures(degree=2).fit(X) X_new = pf.transform(X) clumns_list = pf.get_feature_names(X.columns) features = pd.DataFrame(X_new, columns=clumns_list) 1. 2. 3. 4. X_new是这样的: 获取X的列名: 通过PolynomialFeatures的get_feature_names得到遍历的符合PolynomialF...
from sklearn.preprocessing import PolynomialFeatures #包含直到x**10的多项式,默认的“include_bias=True”添加恒等于1的常数特征 poly = PolynomialFeatures(degree=10, include_bias=False) poly.fit(x) x_poly = poly.transform(x) #多项式次数为10,所以生成了10个特征: print("x_poly.shape:{}".format(x...
X=np.array([[1,2],[3,4]])poly=PolynomialFeatures(degree=2,include_bias=False)X_poly=poly.fit_transform(X)print(X_poly)# 输出: [[1. 2. 1. 2. 4.]# [3. 4. 9. 12. 16.]]print(poly.get_feature_names(['x1','x2']))# 输出: ['x1', 'x2', 'x1^2', 'x1 x2', 'x...
多项式特征是一种在线性模型中引入非线性的有效方法。Scikit-Learn的PolynomialFeatures类能够生成多项式特征和变量间的交互项。 常见的多项式特征包括: x² (平方项) x³ (立方项) x⁴ (四次方项) 更高次项 对于具有多个特征的模型(x_1, x_2, …, x_n),还可以创建交互项: ...
poly=PolynomialFeatures(degree=2, include_bias=False) X_poly=poly.fit_transform(X) print(X_poly) # 输出: [[ 1. 2. 1. 2. 4.] # [3. 4. 9. 12. 16.]] print(poly.get_feature_names(['x1', 'x2'])) # 输出: ['x1', 'x2', 'x1^2', 'x1 x2', 'x2^2'] ...
y = df['activity']fromsklearn.preprocessingimportPolynomialFeatures poly = PolynomialFeatures(degree=2, include_bias=False, interaction_only=False) X_ploly = poly.fit_transform(X) X_ploly_df = pd.DataFrame(X_ploly, columns=poly.get_feature_names())print(X_ploly_df.head()) ...
format(X_poly[0])) print("PolynomialFeatures对原始数据的处理:\n{}".format(poly.get_feature_names())) 输出 代码语言:javascript 复制 原始数据第一个样本: [4.84191851] 多项式处理后第一个样本: [4.84191851e+00 2.34441748e+01 1.13514784e+02 5.49629333e+02 2.66126044e+03 1.28856062e+04 6.23910549e...
preprocessing import PolynomialFeatures import pandas as pd import numpy as np df = pd.DataFrame.from_dict({ 'x': [2], 'y': [5], 'z': [6]}) p = PolynomialFeatures(degree=2).fit(df) f = pd.DataFrame(p.transform(df), columns=p.get_feature_names(df.columns)) print('deg 2\n'...
#扩展数值特征from sklearn.preprocessing import PolynomialFeaturesx =df[['x','y','z']]y =df['activity']poly = PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)x_poly = poly.fit_transform(x)pd.DataFrame(x_poly, columns=poly.get_feature_names()).head() ...