【机器学习】交叉验证筛选参数K值和weight

交叉验证

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导包

import numpy as npfrom sklearn.neighbors import KNeighborsClassifierfrom 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)

[<matplotlib.lines.Line2D at 0x17ece9ff0b8>]
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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)] =sma =result.values()
list(a)np.array(list(a)).argmax()