鸢尾花分类python实现

鸢尾花分类是一个经典的机器学习问题，可以使用Python进行实现。以下是一种简单的实现方法：

首先，我们需要导入必要的库和数据集：

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, y = iris.data, iris.target

然后，我们将数据集分成训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

接着，我们使用K近邻算法进行分类：

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

最后，我们计算模型的准确率：

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, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

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)

运行以上代码，即可得到鸢尾花分类的准确率。