交叉验证

导包

import numpy as np

from sklearn.neighbors import KNeighborsClassifier

from sklearn import datasets

#model_selection :模型选择
# cross_val_score: 交叉 ,validation:验证(测试)
#交叉验证
from sklearn.model_selection import cross_val_score

读取datasets中鸢尾花(yuan1wei3hua)数据

X,y= datasets.load_iris(True)
X.shape

(150, 4)

一般情况不会超过数据的开方数

#参考
150**0.5
#K 选择 1~13

12.24744871391589

knn = KNeighborsClassifier()

score = cross_val_score(knn,X,y,scoring='balanced_accuracy',cv=11)
score.mean()

0.968181818181818

应用cross_val_score筛选 n_neighbors k值

errors =[]
for k in range(1,14):
    knn = KNeighborsClassifier(n_neighbors=k)
    score = cross_val_score(knn,X,y, scoring='accuracy',cv = 6).mean()

    #误差越小 说明K选择越合适 越好
    errors.append(1-score)

import matplotlib.pyplot as plt
%matplotlib inline

#k = 11时 误差最小 说明最合适的k值
plt.plot(np.arange(1,14),errors)

[] 在这里插入图片描述

应用cross_val_score筛选 weights

weights =['uniform','distance']

for w  in weights:
    knn  = KNeighborsClassifier(n_neighbors = 11,weights= w)
    print(w,cross_val_score(knn,X,y, scoring='accuracy',cv = 6).mean())

uniform 0.98070987654321 distance 0.9799382716049383

模型如何调参的,参数调节

result = {}
for k in range(1,14):
    for w  in weights:
        knn = KNeighborsClassifier(n_neighbors=k,weights=w)
        sm = cross_val_score(knn,X,y,scoring='accuracy',cv=6).mean()
        result[w+str(k)] =sm

a =result.values()
list(a)

np.array(list(a)).argmax()

20

list(result)[20]

'uniform11'

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