title: Confusion matrix of autoencoder shows more failure occurrences (FN+TP) than the actual failure occurrences in the entire dataset
body: <p>I'm trying to implement extreme rare event prediction using autoencoder with the help of this <a href="https://towardsdatascience.com/extreme-rare-event-classification-using-autoencoders-in-keras-a565b386f098" rel="nofollow noreferrer">code</a>. I'm using my own data (severely unbalanced data) where there are only 6 failure events. The confusion matrix that I get is as follows:</p>
  <p><a href="https://i.stack.imgur.com/ZPj2Z.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ZPj2Z.png" alt="confusion matrix" /></a></p>
  <p>It shows that my autoencoder model is able to predict <strong>three</strong> of the <strong>15</strong> (FN+TP) true failure events. However, there are only <strong>six</strong> failure events in the entire dataset. How come the confusion matrix contains <strong>15</strong> true failure events?</p>
  <p>Am I missing something here?</p>