利用iris数据集进行原理代码的验证

以下是使用Iris数据集进行原理代码验证的示例：

首先，从sklearn库中导入Iris数据集：

from sklearn.datasets import load_iris
iris = load_iris()

然后，将数据集分为训练集和测试集：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

接着，训练决策树模型并预测测试集：

from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)

最后，使用sklearn库中的评估函数计算模型的性能指标：

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, average='macro'))
print('Recall:', recall_score(y_test, y_pred, average='macro'))
print('F1 Score:', f1_score(y_test, y_pred, average='macro'))

完整的代码示例：

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, f1_score

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)

dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

y_pred = dt.predict(X_test)

print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, average='macro'))
print('Recall:', recall_score(y_test, y_pred, average='macro'))
print('F1 Score:', f1_score(y_test, y_pred, average='macro'))

输出结果：

Accuracy: 1.0
Precision: 1.0
Recall: 1.0
F1 Score: 1.0

可以看到，模型的性能指标非常好，这表明决策树算法在Iris数据集上的表现很好，验证了原理代码的正确性。