knn鸢尾花分类python

KNN（K-最近邻）是一种简单而有效的分类算法，它可以用于鸢尾花数据集的分类。下面是一个使用Python实现的KNN分类器：

首先，导入所需的库和数据集：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target

接下来，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=0.2)

然后，创建一个KNN分类器对象，并使用训练数据进行拟合：

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

最后，使用测试数据进行预测，并计算分类器的准确性：

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

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

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