Regression problem with Deep Learning

Question

I'm working on Housing Price dataset, where target is to predict the housing price.
Price of the house will always be positive and according to me, it's possible that model can predict a negative outcome for some of the samples.

If it's correct, is there any way to control the training such that
model always predict at least the positive value.

As in case of classification case we use Sigmoid/Softmax activation
function to normalized the outcome in probability. Can we have some
activation function for positive value?

Can I use Poisson loss?

Ashwin Geet D'Sa · Answer

The ability of the model to predict negative value for the housing price depends on the data. On the large amount of data, where there are no negative pricing, the model does not predict a negative number. However, in rare case, where the model is not trained well or has not seen such samples, then it is still possible.

The models prediction on the positive value can be still controlled post predictions. Just like using a treshold. y = y if y>0 else 0; Where the housing cost (y) is as it is if it's positive, 0 otherwise.

ReLu function, works in the way you desire. Negative values gets converted to 0 by the activation.

I am not very sure about the Poisson  loss, you may try it.

Regression problem with Deep Learning

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