Scikit-learn LinearRegression vs Numpy Polyfit
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from sklearn.preprocessing import PolynomialFeatures | |
from sklearn.linear_model import LinearRegression | |
from sklearn.pipeline import Pipeline | |
poly1d = PolynomialFeatures(degree=1) | |
X1 = poly1d.fit_transform(np.array(x).reshape(-1, 1)) | |
linear_reg_1d = LinearRegression() | |
linear_reg_1d.fit(X1, y) | |
print(linear_reg_1d.coef_[1:]) | |
print(linear_reg_1d.intercept_) | |
print(np.linalg.norm(y - linear_reg_1d.predict(X1)) ** 2) | |
[-0.1605313] | |
125.77023172322043 | |
92743.77449407903 | |
poly2d = PolynomialFeatures(degree=2) | |
X2 = poly2d.fit_transform(np.array(x).reshape(-1, 1)) | |
linear_reg_2d = LinearRegression() | |
linear_reg_2d.fit(X2, y) | |
print(linear_reg_2d.coef_[1:]) | |
print(linear_reg_2d.intercept_) | |
print(np.linalg.norm(y - linear_reg_2d.predict(X2)) ** 2) | |
[-0.40780952 0.00079718] | |
139.78768437342626 | |
92025.81182290474 | |
... |
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