作用：简化语法。

准备数据

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
# 载入数据
digits = datasets.load_digits()
X = digits.data
y = digits.target
# 分割数据
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)

网格搜索语法

超参数k、p结合的搜索

参数设置

from sklearn.model_selection import GridSearchCV
# 设置两组参数
param_grid = [
    {
        'weights': ['uniform'], 
        'n_neighbors': [i for i in range(1, 11)]
    },
    {
        'weights': ['distance'],
        'n_neighbors': [i for i in range(1, 11)], 
        'p': [i for i in range(1, 6)]
    }
]

建模

knn_clf = KNeighborsClassifier()
from sklearn.model_selection import GridSearchCV
grid_search = GridSearchCV(knn_clf, param_grid)
grid_search.fit(X_train, y_train)

更多超参数

n_jobs： 代表CPU核数，-1代表使用所有核