Binary Cross Entropy Loss Curve
I am trying to understand my loss curve using : tf.keras.losses.BinaryCrossentropy()
Question 1: Based on my loss curve/accuracy, would it be wise to proceed to feed it into a ensemble learning model alongside with other 10 similar binary classification model?
Question 2: I would know the reason why the apparent divergence between train / validation and whether is it a good/bad thing or any room for improvement/unrepresentative data
Question 3: (I understand mse/mae loss curve divergence/convergence; are they similar too in terms of interpretation?)
Tried looking around for learning loss curve but only found mse/mae interpretation so far but barely anything about BinaryCrossentropy. could someone help me please? TIA