python划分鸢尾花数据集搭建决策树

好的，以下是Python代码实现：

首先，我们需要导入所需的库：

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

接着，我们可以加载鸢尾花数据集：

iris = load_iris()

然后，我们将数据集转换成pandas的DataFrame格式，以便更好地进行数据处理：

df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['target'] = iris.target

接着，我们可以使用train_test_split()函数将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(df[iris.feature_names], df['target'], random_state=0)

现在，我们可以使用sklearn中的DecisionTreeClassifier类来训练决策树模型，并使用训练集进行拟合：

dtc = DecisionTreeClassifier(random_state=0)
dtc.fit(X_train, y_train)

最后，我们可以使用测试集来评估模型的性能：

y_pred = dtc.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

完整代码如下：

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = load_iris()

df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['target'] = iris.target

X_train, X_test, y_train, y_test = train_test_split(df[iris.feature_names], df['target'], random_state=0)

dtc = DecisionTreeClassifier(random_state=0)
dtc.fit(X_train, y_train)

y_pred = dtc.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

希望这可以帮助到你！