model2 = DecisionTreeClassifier(random_state=77)补充代码实现3分类

如果你想使用决策树（Decision Tree Classifier）模型（`model2`）来进行3分类任务，你可以按照以下步骤添加到上述代码中：

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 创建决策树分类器，设定随机种子为77
model2 = DecisionTreeClassifier(criterion='gini', random_state=77, max_depth=None, min_samples_split=2, min_samples_leaf=1) 

# 如果你想使用默认参数，可以去掉"random_state=77"

# 训练模型
model2.fit(X_train, y_train)

# 预测测试集
y_pred_2 = model2.predict(X_test)

# 检查预测结果
print("Using Decision Tree Model:")
print("Predicted labels:", y_pred_2)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred_2)
print("Accuracy:", accuracy)

# 可能需要的评估报告
print("\nClassification Report for Decision Tree:\n", classification_report(y_test, y_pred_2))