Is a neural network (for example a MLPClassifier in Python) able to learn to map a completely (or very) different input feature set to the same output class? Or is it better to work in this case with more than one output class and map these recognized output classes afterwards to the same class manually?
Topic feature-engineering neural-network machine-learning
Category Data Science
If I understand your question correctly then yes, neural networks are exceptional in spotting patterns in data (even unstructured like images) and output the correct label
Or is it better to work in this case with more than one output class and map these recognized output classes afterwards to the same class manually
I am not sure I understand what you mean by this but essentially the output layer of your neural network should have just as many neurons as you have possible outputs when it comes to classification (except for binary classification where only one neuron is enough).
Yes, neural networks excel at finding highly non-linear decision boundaries. For example, take a look at this demo (don't forget to click the play button to make the network learn). Even this very simple network is able to learn that the opposite corners of the input space belong to the same class.
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