模型评估：混淆矩阵

def solve():
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import confusion_matrix
    import seaborn as sns
    import matplotlib.pyplot as plt
    iris = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
    model = RandomForestClassifier(random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    cm = confusion_matrix(y_test, y_pred)
    print("混淆矩阵:")
    print(cm)
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)
    plt.xlabel('预测类别')
    plt.ylabel('真实类别')
    plt.title('混淆矩阵')
    plt.show()