# -*- coding: utf-8 -*-
import numpy
from sklearn import datasets
#引入数据集
iris = datasets.load_iris()
iris
#查看数据的规模
iris.data.shape
#查看训练目标的总类
numpy.unique(iris.target)
from sklearn.model_selection import train_test_split
data_train, data_test, target_train, target_test = train_test_split(
iris.data,
iris.target,
test_size=0.3
)
data_train.shape
data_test.shape
target_train.shape
target_test.shape
from sklearn import neighbors
knnModel = neighbors.KNeighborsClassifier(n_neighbors=3)
knnModel.fit(data_train, target_train)
knnModel.score(data_test, target_test)
from sklearn.model_selection import cross_val_score
cross_val_score(
knnModel,
iris.data, iris.target, cv=5
)
#使用模型进行预测
knnModel.predict([[0.1, 0.2, 0.3, 0.4]])
