KNN

 

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# 1、加载数据
iris = datasets.load_iris()
x = iris.data
y = iris.target
target_names = ['setosa', 'versicolor', 'virginica']
feature_names = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

# 2、数据预处理,这里划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

# 3、创建模型
model = KNeighborsClassifier()

# 4、训练数据
model.fit(x_train, y_train)

# 5、测试数据
acc = model.score(x_test, y_test)
print(acc)