【Python机器学习】k近邻——模型复杂度与泛化能力的关系

以某数据进行研究，先将数据集分为训练集和测试集，然后用不同的邻居数对训练集合测试集的新能进行评估：

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
)
plt.rcParams['font.sans-serif']=['SimHei']
training_accuracy=[]
test_accuracy=[]
neighbors_settings=range(1,11)

for n_neighbors in neighbors_settings:
    clf=KNeighborsClassifier(n_neighbors=n_neighbors)
    clf.fit(X_train,y_train)
    training_accuracy.append(clf.score(X_train,y_train))
    test_accuracy.append(clf.score(X_test,y_test))

plt.plot(neighbors_settings,training_accuracy,label='训练集精度')
plt.plot(neighbors_settings,test_accuracy,label='泛化精度')
plt.xlabel('邻居数')
plt.ylabel('精度')
plt.legend()
plt.show()

从结果上来看，进考虑单一近邻时，数据集上的预测结果非常完美，随着邻居个数的增多，模型变得简单，精度也随之下降。最佳性能在中间处，大概邻居为6个的时候。