In [1]:

    

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline

X = np.sort(np.random.rand(100))
y = np.cos(1.5 * np.pi * X) + np.random.randn(100) * 0.1
X = X[:, np.newaxis]

polynomial_features = PolynomialFeatures(degree=4);
linear_regression = LinearRegression();

#빠진 부분
model = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)])

model.fit(X, y)


    

    
Out[1]:





Pipeline(steps=[('polynomial_features', PolynomialFeatures(degree=4, include_bias=True, interaction_only=False)), ('linear_regression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])

In [5]:

    

Pipeline([("polynomial_feafures", polynomial_features)])


    

    
Out[5]:





Pipeline(steps=[('polynomial_feafures', PolynomialFeatures(degree=4, include_bias=True, interaction_only=False))])