Keras decision threshold for Multiple Label prediction I'm training a Neural Network to predict multiple labels for a given input. My input is a 200 sized vector of integers and the output should be a boolean vector of size 28. My y has a 1 on the corresponding classes the example corresponds to, i.e. the y should look like:
[0, 0, 1, 0, ... 1, 0, 0]. 

Now, I've used a sigmoid function on my final layer, and I'm training with binary crossentropy since I want my model to treat each class as independent from each other.

When I fit my model, I get a pretty decent categorical accuracy, i.e. ~75%, but I'm wondering which is the threshold that Keras uses to say a class is or isn't present on the example, I mean, which is the threshold where it decides:

prediction[prediction &gt;= threshold] = 1
prediction[prediction &lt; threshold] = 0
You can look at your multi class classification as 28 separate binary classifiers (1 for each output feature).

The output of a binary classification is the probability of a sample belonging to a class, so the threshold per each feature is 0.5.