title: how to interpret a probability predictions of a deep learning model that is an output of a sigmoid activation of last layer?
body: <p>I have trained a binary classification task (pos. vs. neg.) and have a <code>.h5</code> model. And I have external data (which was never used in training nor in the validation). There are 20 of samples overall belonging to both classes.</p>
  <pre><code>preds = model.predict(img)
  y_classes = np.argmax(preds , axis=1)
  </code></pre>
  <p>The above code is supposed to calculate probability (preds) and class labels (0 or 1) if it were trained with <em>softmax</em> as the last output layer. But, <em>preds</em> is only a single number between [0;1] and <em>y_classes</em> is always 0.</p>
  <p>To go back a little, the model was evaluated with mean AUC with the area being around 0.75.
  I can see the probabilities of those 20 samples mostly (17) lie between 0 - 0.15, the rest are 0.74, 0.51 and 0.79.</p>
  <p>How do I make a conclusion from this?</p>
  <p><strong>EDIT:</strong>
  10 among 20 samples for testing the model belong to positive class, the other 10 belong to negative class. All 10 which belong to pos. class have very low prabability (0 - 0.15). 7 out 10 negative classes have the same low probability, only 3 being (0.74, 0.51 and 0.79).</p>
  <p>The question: Why is the model predicting the samples with such a low probability even though its AUC was quite higher?</p>