sklearn调参有一个核武器—gridsearchcv，它存在的意义就是自动调参，只要把参数输进去，就可以对算法进行相应的调优，找到合适的参数。然而，在小数据集上还可以小打小闹。一旦数据的量级上去了，就很难得到结果，而且会报出memory error等错误
#coding:utf-8 """ sklearn 0.18 python 3 KNN参数调优 """ from sklearn.model_selection import GridSearchCV from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score,confusion_matrix,classification_report import input_data import numpy as np mnist = input_data.read_data_sets('mnist/',one_hot=False) #小数据量加快gridsearch的速度 x = mnist.train.images[0:1000,:] y = mnist.train.labels[0:1000] train_data,validation_data,train_labels,validation_labels = train_test_split(x,y,test_size=0.1) #开始调优使用GridSearchCV找到,最优参数 knn = KNeighborsClassifier() #设置k的范围 k_range = list(range(1,10)) leaf_range = list(range(1,2)) weight_options = ['uniform','distance'] algorithm_options = ['auto','ball_tree','kd_tree','brute'] param_gridknn = dict(n_neighbors = k_range,weights = weight_options,algorithm=algorithm_options,leaf_size=leaf_range) gridKNN = GridSearchCV(knn,param_gridknn,cv=10,scoring='accuracy',verbose=1) gridKNN.fit(train_data,train_labels) print('best score is:',str(gridKNN.best_score_)) print('best params are:',str(gridKNN.best_params_))