python - 防止 RandomizedSearchCV 预测 KNN 分类器的所有一类

Question

我正在使用 RandomizedSearchCV 和 KNeighborsClassifier 来尝试预测贷款违约。

使用 RandomizedSearchCV 在理论上似乎很棒，但当我对其进行测试时，它发现最好的 best_esimator_ 是预测所有相同标签的一个。

(数据分为 75% 付费和 25% 默认)，因此我得到的准确率为 75%，但它只是预测所有付费。

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)

我能做些什么来解决这个问题吗？我可以传递一个参数来阻止这种情况发生吗？或者我可以在我的数据中做些什么？

y_测试:

38        PAIDOFF
189       PAIDOFF
140       PAIDOFF
286    COLLECTION
142       PAIDOFF
101       PAIDOFF
187       PAIDOFF
139       PAIDOFF
149       PAIDOFF
11        PAIDOFF
269    COLLECTION
231       PAIDOFF
258       PAIDOFF
84        PAIDOFF
242       PAIDOFF
344    COLLECTION
104       PAIDOFF
214       PAIDOFF
109       PAIDOFF
76        PAIDOFF
41        PAIDOFF
262    COLLECTION
125       PAIDOFF
107       PAIDOFF
27        PAIDOFF
14        PAIDOFF
92        PAIDOFF
194       PAIDOFF
113       PAIDOFF
333    COLLECTION
          ...    
320    COLLECTION
15        PAIDOFF
72        PAIDOFF
122       PAIDOFF
243       PAIDOFF
184       PAIDOFF
294    COLLECTION
280    COLLECTION
218       PAIDOFF
197       PAIDOFF
133       PAIDOFF
143       PAIDOFF
179       PAIDOFF
249       PAIDOFF
80        PAIDOFF
331    COLLECTION
137       PAIDOFF
103       PAIDOFF
120       PAIDOFF
248       PAIDOFF
5         PAIDOFF
236       PAIDOFF
219       PAIDOFF
322    COLLECTION
283    COLLECTION
135       PAIDOFF
124       PAIDOFF
293    COLLECTION
166       PAIDOFF
85        PAIDOFF

预测:

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', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',
       'PAIDOFF', 'PAIDOFF'], dtype=object)

Answer 1

这是一个典型的数据不平衡问题。您可以尝试的一些简单操作是对少数类进行上采样或对多数类进行下采样，然后重试。更好的方法是更改​​算法并使用 SVC 或神经网络这可能会严重影响少数案件的损失。

例如，sklearn sklearn.svm.SVC 的 class_weights = 'balanced' 参数将对此有所帮助。它基本上会根据输入数据中少数群体的比例来衡量少数群体的成本。

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as