Choosing between Regression and Classification I want to build a model using a neural network that will be able to extract some features from landscape pictures.

In order to improve the efficiency of my model, I first want to extract the "vertical orientation" of the picture. This "vertical orientation" would be next a feature for my neural network.

Now, to compute this feature I see two solutions.


Build a Regression model that would return a result in degrees (0 to 180)
Build a Classification model that would return the class of orientation (ex: High, Medium High, Medium, Medium Low, Low)


Is there a way to decide which solution to use, or should I test both solutions to find the better one ?

Note : 


The dataset is not a problem. I can have pictures labeled in degrees or in class easily
If you are trying to build a neural network you should stick with a the orientation as a numeric value since neural networks only accept numeric data as input.  If you want to use the class values, you will need to one hot encode them.  It will also result in a loss of information.

Of course, it depends what kind of neural network you are trying to implement. In most cases, I don't think this step is necessary and I think it may complicate the architecture of your model.

If you are open to suggestions, instead of building a regression model, I would investigate Principal Component Analysis.  It could alleviate the need to have the photographs labeled before input into the neural network.