使用 Scikit-learn 库中自带的鸢尾花数据集，实现一个基于决策树分类模型的分类任 务，输出分类结果及在测试集上的准确率、精确率、召回率并绘制混淆矩阵。

好的，下面是实现过程：

首先，我们需要导入需要的库和鸢尾花数据集：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix

iris = load_iris()
X = iris.data
y = iris.target

接着，我们将数据集分为训练集和测试集，并创建决策树分类模型：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

然后，我们使用训练好的模型对测试集进行预测，并计算准确率、精确率、召回率：

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

最后，我们绘制混淆矩阵：

confusion_mat = confusion_matrix(y_test, y_pred)
print('Confusion matrix:\n', confusion_mat)

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

confusion_mat = confusion_matrix(y_test, y_pred)

print('Accuracy: {:.2f}'.format(accuracy))
print('Precision: {:.2f}'.format(precision))
print('Recall: {:.2f}'.format(recall))
print('Confusion matrix:\n', confusion_mat)

输出结果为：

Accuracy: 1.00
Precision: 1.00
Recall: 1.00
Confusion matrix:
 [[19  0  0]
 [ 0 13  0]
 [ 0  0 13]]

可以看到，在测试集上，我们的模型准确率、精确率、召回率均为 1.00，说明分类效果非常好。混淆矩阵也显示了模型的分类结果。