多项式回归

def solve():
    import numpy as np
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.linear_model import LinearRegression
    from sklearn.pipeline import Pipeline
    import matplotlib.pyplot as plt
    np.random.seed(____)
    X = np.linspace(____).reshape(____)
    y = ____ * X.ravel() ** ____ - X.ravel() + ____ + np.random.normal(____, ____, ____)
    degrees = [____, ____, ____]
    plt.figure(figsize=(____, ____))
    plt.scatter(____)
    X_test = np.linspace(____).reshape(____)
    for degree in degrees:
        model = Pipeline([('____', PolynomialFeatures(____)), ('____', LinearRegression())])
        model.fit(____)
        y_pred = model.predict(____)
        plt.plot(____)
    plt.xlabel(____)
    plt.ylabel(____)
    plt.title(____)
    plt.legend()
    plt.grid(____)
    plt.show()