利用sklearn 交叉验证训练参数

from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
k_range = range(1, 31)
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, iris_X, iris_y, cv = 10, scoring = 'accuracy')
    k_scores.append(scores.mean())

plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')
plt.show()

我们要训练的参数是k，表示模型KNeighborsClassifier中的周围邻居的个数，可以看出模型准确率最好时，对应的k的取值范围。