线性回归可视化

def solve():
    import pandas as pd
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.linear_model import LinearRegression
    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)
    lr = LinearRegression().fit(X_train, y_train)
    xline = np.linspace(X["x1"].min(), X["x1"].max(), 100).reshape(-1, 1)
    plt.scatter(X["x1"], y, alpha=0.5)
    plt.plot(xline, lr.predict(xline), "r-", linewidth=2, label="Regression Line")
    plt.xlabel("x1"); plt.ylabel("y"); plt.legend(); plt.title("Linear Regression")
    plt.show()
    print(f"{lr.score(X_test, y_test):.4f}:{lr.intercept_:.4f}")