Can tanh be used as an output for a binary classifier? I am creating a binary classifier in Keras and here;s the code

model = Sequential()
    model.add(Dense(30, input_dim=60, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


I was wondering, if the dataset's range is changed from [0,1] to [-1,1], shouldn't I be able to use tanh as the activation of the output layer? If so, what are the advantages of one activation over the other?
If you label your data using -1 and 1 as classes, then yes you can. However, there are two reasons why data scientists normally prefer Sigmoid activations:


Loss functions, such as cross entropy based, are designed for data in the [0, 1] interval.
Better interpretability: data in [0, 1] can be thought as probabilities of belonging to acertain class, or as a model's confidence about it.


But yeah, you can use Tanh and train useful models with it.