鸢尾花分类：KNN和SVM算法比较

该程序主要实现了使用KNN算法和SVM算法对鸢尾花数据集进行分类，并计算分类的准确率。其中，通过加载数据集、划分训练集和测试集，得到用于训练和测试的数据和标签；然后使用KNN算法和SVM算法对数据进行分类，并计算分类的准确率。最后输出KNN算法和SVM算法的分类准确率。

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.metrics import accuracy_score

# 加载数据集
iris = load_iris()

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# KNN算法
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
knn_acc = accuracy_score(y_test, knn_pred)
print('KNN accuracy:', knn_acc)

# SVM（支持向量机）算法
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
svm_acc = accuracy_score(y_test, svm_pred)
print('SVM accuracy:', svm_acc)