K-means聚类算法实现，采用文件“C:\Users\Administrator\Desktop”里面的莺尾花数据，学习决策树，写出代码画出决策树

Handler());
params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
params.setImportFieldHandler(new ExcelImportCellRangeData好的，感谢您提供了更加具体和明确的问题。下面是关于K-means聚类Handler());
params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
params.setImportFieldHandler(new ExcelImportCellRangeData算法和决策树的实现代码：

K-means聚类算法实现代码：

import numpy as npHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris

# 加载莺尾花数据集
iris = load_Handler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDatairis()
X = iris.data

# 构建K-means模型，聚类数量为3
kmeans = KMeans(n_clustersHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData=3, random_state=0)

# 进行聚类
kmeans.fit(X)

# 输出聚类结果
print(kmeans.labelsHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData_)

决策树实现代码：

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier,Handler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData plot_tree
import matplotlib.pyplot as plt

# 加载莺尾花数据集
iris = load_iris()
X = iris.data
Handler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDatay = iris.target

# 构建决策树模型
clf = DecisionTreeClassifier(random_state=0)

# 进行拟合Handler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData
clf.fit(X, y)

# 绘制决策树
plt.figure(figsize=(20, 10))
plot_tree(clf, filledHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
    params.setImportFieldHandler(new ExcelImportCellRangeData=True)
plt.show()

需要注意的是，上述代码中需要安装scikit-learn和matplotlib库。另外Handler());
params.setImportFieldHandler(new ExcelImportCellRangeDataHandler());
params.setImportFieldHandler(new ExcelImportCellRangeData，决策树的绘制结果可能比较复杂，需要进行适当缩放和调整。