使用 sklearn 進行多項式回歸

Question

這是我的代碼：

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)

我有這些錯誤：

https://imgur.com/a/GkYFQlc

Answer 1

PolynomialFeatures沒有名為coef_的變量。 PolynomialFeatures 不進行多項式擬合，它只是將您的初始變量轉換為更高階。 實際進行回歸的完整代碼是：

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))