混淆矩阵详解可视化

def solve():
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler
    from sklearn.metrics import confusion_matrix
    from sklearn.model_selection import train_test_split
    df = pd.read_csv("https://data.zuihe.com/breast_cancer.csv")
    X = df.drop("target", axis=1); y = df["target"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    scaler = StandardScaler()
    X_train_s = scaler.fit_transform(X_train); X_test_s = scaler.transform(X_test)
    lr = LogisticRegression(max_iter=5000, random_state=42).fit(X_train_s, y_train)
    y_pred = lr.predict(X_test_s)
    cm = confusion_matrix(y_test, y_pred)
    plt.imshow(cm, cmap="Blues"); plt.colorbar()
    for i in range(2):
        for j in range(2):
            plt.text(j, i, str(cm[i,j]), ha="center", va="center", fontsize=16)
    plt.xlabel("Predicted"); plt.ylabel("True"); plt.title("Confusion Matrix")
    plt.show()
    print(f"{cm[0,0]},{cm[0,1]},{cm[1,0]},{cm[1,1]}")