Should I use additional empty category in some categorical problems? I try to create autonomous car using keyboard data so this is a multi class classification problem. I have keys W,A,S and D. So I have four categories. My model should decide what key should be pressed based on the screenshot (or some other data, I have some ideas). I have some API that I can use to capture keyboard data and screen (while gathering data) and also to simulate keyboard events (in autonomous mode when the car is driven by neural network).
Should I create another category called for example &quot;NOKEY&quot;? I will use sigmoid function on each output neuron (instead of using softmax on the all neurons) to have probabilities from 0 to one for each category. But I could have very low probabilities for each neuron. And it can mean either that no key should be pressed or that the network doesn't know what to do. So maybe I should just create additional &quot;artificial&quot; category?
What is the standard way to deal with such situations?
In short: yes, you must allow &quot;do nothing&quot; decision as a first level result.
Your system must decide the action to be taken, including &quot;do nothing&quot; action. This is different to low network outputs, that can be translated as &quot;don't know what to do&quot;.
In other words, the network can result in:

&quot;I don't know what to do now&quot; when all results in the output have low probabilities. (Obviously, this is a bad network result, to be fixed as much as possible).
&quot;I know I must do nothing&quot;, when &quot;do nothing&quot; action has high probability, greater than the others.
&quot;I know I must do W&quot;, when &quot;W&quot; action has high probability, greater than the others.
...

Kind regards.