我正在使用RandomizedSearchCV和KNeighborsClassifier尝试预测贷款违约情况。
理论上使用RandomizedSearchCV似乎很不错，但在实际测试中，它找到的最佳best_estimator_总是预测出所有相同的标签。
（数据集的分割是75%已还清，25%违约），所以我得到了75%的准确率，但它只是预测所有已还清。
n_neighbors = [int(x) for x in np.linspace(start = 1, stop = len(X_train)/3, num = 5)]weights = ['uniform', 'distance']algorithm = ["auto","ball_tree","kd_tree","brute"]leaf_size = [int(x) for x in np.linspace(10, 100, num = 5)]p = [1,2] random_grid = {'n_neighbors': n_neighbors, 'weights': weights, 'algorithm': algorithm, 'leaf_size': leaf_size, 'p': p}knn_clf = KNeighborsClassifier()knn_random = RandomizedSearchCV(estimator = knn_clf, param_distributions = random_grid, n_iter = 25, cv = 3, verbose=1,)knn_random.fit(X_train, y_train)有什么方法可以解决这个问题吗？有没有可以传递的参数来阻止这种情况发生？或者我可以对数据做些什么吗？
y_test:
38 PAIDOFF189 PAIDOFF140 PAIDOFF286 COLLECTION142 PAIDOFF101 PAIDOFF187 PAIDOFF139 PAIDOFF149 PAIDOFF11 PAIDOFF269 COLLECTION231 PAIDOFF258 PAIDOFF84 PAIDOFF242 PAIDOFF344 COLLECTION104 PAIDOFF214 PAIDOFF109 PAIDOFF76 PAIDOFF41 PAIDOFF262 COLLECTION125 PAIDOFF107 PAIDOFF27 PAIDOFF14 PAIDOFF92 PAIDOFF194 PAIDOFF113 PAIDOFF333 COLLECTION ... 320 COLLECTION15 PAIDOFF72 PAIDOFF122 PAIDOFF243 PAIDOFF184 PAIDOFF294 COLLECTION280 COLLECTION218 PAIDOFF197 PAIDOFF133 PAIDOFF143 PAIDOFF179 PAIDOFF249 PAIDOFF80 PAIDOFF331 COLLECTION137 PAIDOFF103 PAIDOFF120 PAIDOFF248 PAIDOFF5 PAIDOFF236 PAIDOFF219 PAIDOFF322 COLLECTION283 COLLECTION135 PAIDOFF124 PAIDOFF293 COLLECTION166 PAIDOFF85 PAIDOFF预测结果:
array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)回答：
这是经典的类别不平衡问题。你可以尝试的一些简单方法是增加少数类别的样本（上采样）或减少多数类别的样本（下采样），然后再试一次。更好的方法是改变你的算法，使用支持向量机（SVC）或神经网络，这些算法可以对少数类别的损失进行加权处理。
例如，sklearn中的sklearn.svm.SVC有一个class_weights = 'balanced'参数，可以帮助解决这个问题。它会根据输入数据中各类别的比例，自动调整少数类别的权重。
“balanced”模式会根据输入数据中各类别的频率自动调整权重，使其与类别频率成反比。
