from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_digits from sklearn import svm, metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.metrics import plot_confusion_matrix import numpy as np #使绘图支持中文字符 from matplotlib import rcParams rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False # In[2]: digits = load_digits() data = digits.data print(data[0]) print(digits.images[0]) print(digits.target[0]) plt.imshow(digits.images[0]) plt.show() train_x, test_x, train_y, test_y = train_test_split(data, digits.target, test_size=0.3, random_state=93) print(train_x) knn = KNeighborsClassifier(n_neighbors=5) knn.fit(train_x, train_y) print("KNN训练集得分: %.4lf" % knn.score(train_x, train_y)) print("KNN测试集得分: %.4lf" % knn.score(test_x, test_y)) print(knn.predict(data)) plot_confusion_matrix(knn, test_x, test_y) plt.show() # In[5]: ################################################################# fig = plt.figure(figsize=(6,13)) for i in range(40): y_pred = knn.predict([data[i]]) plt.subplot(8,5,i+1) plt.imshow(digits.images[i], interpolation='none') plt.title("%d---->%d"%(digits.target[i],y_pred)) plt.show() 帮我修改一下代码，让测试集结果更准确

为了提高测试集结果的准确性，你可以尝试以下几个方法：

1. 标准化数据：使用`StandardScaler`对训练集和测试集的数据进行标准化处理。标准化可以使特征的均值为0，方差为1，有助于提高模型的性能。

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
train_x_scaled = scaler.fit_transform(train_x)
test_x_scaled = scaler.transform(test_x)

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(train_x_scaled, train_y)
print("KNN训练集得分: %.4lf" % knn.score(train_x_scaled, train_y))
print("KNN测试集得分: %.4lf" % knn.score(test_x_scaled, test_y))

2. 调整超参数：尝试不同的`n_neighbors`值，找到最佳的超参数。可以使用交叉验证进行网格搜索来寻找最佳的超参数。

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_neighbors': [3, 5, 7, 9, 11]
}

grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5)
grid_search.fit(train_x_scaled, train_y)

best_knn = grid_search.best_estimator_
print("最佳KNN模型参数: ", grid_search.best_params_)
print("最佳KNN训练集得分: %.4lf" % best_knn.score(train_x_scaled, train_y))
print("最佳KNN测试集得分: %.4lf" % best_knn.score(test_x_scaled, test_y))

3. 增加训练样本数量：如果训练样本数量较少，可以考虑增加训练样本的数量，以提高模型的泛化能力。

additional_train_x, additional_train_y = load_additional_data()  # 加载额外的训练数据
train_x_combined = np.concatenate((train_x, additional_train_x))
train_y_combined = np.concatenate((train_y, additional_train_y))

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(train_x_combined, train_y_combined)
print("KNN训练集得分: %.4lf" % knn.score(train_x_combined, train_y_combined))
print("KNN测试集得分: %.4lf" % knn.score(test_x_scaled, test_y))

尝试以上方法后，可以比较不同模型的性能，并选择表现最好的模型作为最终的选择。