from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score iris = datasets.load_iris() iris_X = iris.data iris_y = iris.target X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3) knn = KNeighborsClassifier () knn.fit(X_train,y_train) y_knn = knn.predict(X_test) print('分类器得到的分类：\n ',y_knn) print('真实分类：\n ',y_test) print('准确率为：',accuracy_score(y_test,y_knn))采用随机森林模型来改写，用python

好的，以下是使用随机森林模型对鸢尾花数据集进行分类的代码：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载鸢尾花数据集
iris = datasets.load_iris()
iris_X = iris.data
iris_y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3)

# 创建随机森林分类器
rfc = RandomForestClassifier(n_estimators=100)

# 训练模型
rfc.fit(X_train, y_train)

# 预测测试集
y_rfc = rfc.predict(X_test)

# 输出分类结果和准确率
print('分类器得到的分类：\n ', y_rfc)
print('真实分类：\n ', y_test)
print('准确率为：', accuracy_score(y_test, y_rfc))

其中，`RandomForestClassifier`是sklearn中的随机森林模型，`n_estimators`表示树的数量。