Python 데이터분석 기초 70 - K-NN (K-Nearest Neighber)

 

breast_cancer dataset

 

# KNN
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt

cancer = load_breast_cancer()

x_train, x_test, y_train, y_test = train_test_split(cancer.data, cancer.target,
                                                    stratify = cancer.target, random_state = 66)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) # (426, 30) (143, 30) (426,) (143,)

train_acc = []
test_acc = []

neighbors_setting = range(1, 10)

for n_neigh in neighbors_setting:
    clf = KNeighborsClassifier(n_neighbors=n_neigh)
    clf.fit(x_train, y_train)
    train_acc.append(clf.score(x_train, y_train))
    test_acc.append(clf.score(x_test, y_test))
    
import numpy as np
print('train 분류 평균 정확도 :', np.mean(train_acc))
print('test 분류 평균 정확도 :', np.mean(test_acc))

plt.plot(neighbors_setting, train_acc, label = 'train acc')
plt.plot(neighbors_setting, test_acc, label = 'test acc')
plt.ylabel('accuracy')
plt.xlabel('k')
plt.legend()
plt.show()



<console>
(426, 30) (143, 30) (426,) (143,)
train 분류 평균 정확도 : 0.9559207094418363
test 분류 평균 정확도 : 0.9191919191919192

train과 test의 분류 평균 정확도의 차이가 크지 않으면 좋다.

 

 

train, test 정확도 시각화