KNN回归可视化

def solve():
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.neighbors import KNeighborsRegressor
    from sklearn.model_selection import train_test_split
    df = pd.read_csv("https://data.zuihe.com/regression.csv")
    X = df[["x1"]]; y = df["y"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    knn = KNeighborsRegressor(n_neighbors=5).fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    plt.scatter(X_train, y_train, c="blue", alpha=0.5, label="Train")
    plt.scatter(X_test, y_pred, c="green", marker="^", label="Predicted")
    plt.legend(); plt.xlabel("x1"); plt.ylabel("y"); plt.title("KNN Regression")
    plt.show()
    print(f"{knn.score(X_test, y_test):.4f}")