采用KNN，SVM，决策树、集成学习方法实现中鸢尾花数据集分类，挑选准确率最高的模型。写出代码

好的，以下是Python实现的代码示例：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import accuracy_score

# 加载中鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# KNN算法
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
knn_acc = accuracy_score(y_test, knn_pred)

# SVM算法
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
svm_acc = accuracy_score(y_test, svm_pred)

# 决策树算法
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)
dt_acc = accuracy_score(y_test, dt_pred)

# 随机森林算法
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
rf_acc = accuracy_score(y_test, rf_pred)

# AdaBoost算法
ada = AdaBoostClassifier()
ada.fit(X_train, y_train)
ada_pred = ada.predict(X_test)
ada_acc = accuracy_score(y_test, ada_pred)

# 输出各个算法的准确率
print("KNN准确率：", knn_acc)
print("SVM准确率：", svm_acc)
print("决策树准确率：", dt_acc)
print("随机森林准确率：", rf_acc)
print("AdaBoost准确率：", ada_acc)

# 选择准确率最高的算法作为最终的分类器
best_acc = max(knn_acc, svm_acc, dt_acc, rf_acc, ada_acc)
if best_acc == knn_acc:
    clf = knn
    print("最佳分类器是KNN")
elif best_acc == svm_acc:
    clf = svm
    print("最佳分类器是SVM")
elif best_acc == dt_acc:
    clf = dt
    print("最佳分类器是决策树")
elif best_acc == rf_acc:
    clf = rf
    print("最佳分类器是随机森林")
else:
    clf = ada
    print("最佳分类器是AdaBoost")

注意，由于中鸢尾花数据集的特征较少，我们省略了特征选择和特征缩放等步骤。在实际应用中，这些步骤可能对模型性能产生重要影响。