（机器学习）KNN学习曲线绘制

通过学习曲线得到KNN最优k值

import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import sklearn.datasets as datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
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iris = datasets.load_iris()
# 2.提取样本数据
feature = iris['data']
target = iris['target']
x_train, x_test, y_train, y_test = train_test_split(feature, target, test_size=0.2, random_state=2020)
scores = []
ks = []
for i in range(1, 120):
    knn = KNeighborsClassifier(n_neighbors=i)
    knn = knn.fit(x_train, y_train)
    score = knn.score(x_test, y_test)
    scores.append(score)
    ks.append(i)
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scores_arr = np.array(scores)
ks_arr = np.array(ks)
plt.plot(ks_arr, scores_arr)
plt.xlabel('k_value')
plt.ylabel('score')
print("最大时k值：", ks_arr[scores_arr.argmax()])
print("最大分数:", scores_arr[scores_arr.argmax()])
plt.show()
# 感觉和ls_arr没什么关系，直接找出scores_arr中最大的便为相对最优k值
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最大时k值： 8
最大分数: 0.9666666666666667