[英]Polynomial Regression using sklearn
这是我的代码:
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
X=np.array([[1, 2, 4]]).T
print(X)
y=np.array([1, 4, 16])
print(y)
model = PolynomialFeatures(degree=2)
model.fit(X,y)
print('Coefficients: \n', model.coef_)
print('Others: \n', model.intercept_)
#X_predict=np.array([[3]])
#model.predict(X_predict)
我有这些错误:
PolynomialFeatures
没有名为coef_
的变量。 PolynomialFeatures 不进行多项式拟合,它只是将您的初始变量转换为更高阶。 实际进行回归的完整代码是:
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
X=np.array([[1, 2, 4]]).T
print(X)
y=np.array([1, 4, 16])
print(y)
model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression(fit_intercept = False))
model.fit(X,y)
X_predict = np.array([[3]])
print(model.named_steps.linearregression.coef_)
print(model.predict(X_predict))
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