I am trying to perform binary classification with a highly imbalanced dataset. My target values are 0(84%) and 1 (16%). I used class\_weight in my model but the precision and recall for the minority class is always 0. I am not sure if i am using class\_weights correctly. Would really appreciate any help on this!

Below is my code:

```
class_weight = {0:1,1:50}
numpy.random.seed(5)

model = Sequential()
model.add(Dense(13,input_dim = 5, activation='relu'))
model.add(Dense(13, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss="binary_crossentropy", optimizer = "adam", metrics = ['accuracy'])
model.fit(X_train,Y_train, epochs = 10, batch_size = 30, class_weight = class_weight, validation_data = (X_test, Y_test))
preds = model.predict_classes(X_test)
print (classification_report(Y_test, preds))

           precision    recall  f1-score   support

      0       0.83      1.00      0.91     24126
      1       0.00      0.00      0.00      4879

```