I am unsure how to interpret the default behavior of Keras in the following situation:

My Y (ground truth) was set up using scikit-learn&#39;s `MultilabelBinarizer`().

Therefore, to give a random example, one row of my `y` column is one-hot encoded as such:
`[0,0,0,1,0,1,0,0,0,0,1]`.

So I have 11 classes that could be predicted, and more than one can be true; hence the multilabel nature of the problem.  There are three labels for this particular sample.

I train the model as I would for a non multilabel problem (business as usual) and I get no errors.

    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation
    from keras.optimizers import SGD
    
    model = Sequential()
    model.add(Dense(5000, activation=&#39;relu&#39;, input_dim=X_train.shape[1]))
    model.add(Dropout(0.1))
    model.add(Dense(600, activation=&#39;relu&#39;))
    model.add(Dropout(0.1))
    model.add(Dense(y_train.shape[1], activation=&#39;softmax&#39;))
    
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss=&#39;categorical_crossentropy&#39;,
                  optimizer=sgd,
                  metrics=[&#39;accuracy&#39;,])
    
    model.fit(X_train, y_train,epochs=5,batch_size=2000)
    
    score = model.evaluate(X_test, y_test, batch_size=2000)
    score

What does Keras do when it encounters my `y_train` and sees that it is &quot;multi&quot; one-hot encoded, meaning there is more than one &#39;one&#39; present in each row of `y_train`?  Basically, does Keras automatically perform multilabel classification?  Any differences in the interpretation of the scoring metrics?

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How does Keras handle multilabel classification?