# How and why would MLPs for classification differ from MLPs for regression? Different backpropagation and transfer functions?

I'm using two 3-layer feedforward multi-layer perceptrons (MLPs). With the same input data (14 input neurons), I do one classification (true/false), and one regression (if true, "how much")¹. Until now, I've lazily used Matlabs patternnet and fitnet, respectively. Lazily, because I haven't taken the time to really understand what's going on — and I should. Moreover, I need to make the transition to an OSS library (probably FANN), that will likely require more manual setup than the Matlab NN Toolbox. Therefore, I'm trying to understand more precisely what's going on.

The networks created by patternnet and fitnet are nearly identical: 14 inputs neurons, 11 hidden neurons, 1 target neuron (2 for the fitnet, but only 1 piece of informatio). But, they're not completely identical. The differences by default are:

Should those differences be?

What kind of backpropagation functions are optimal for classification, and what kind for regression, and why?

What kind of transfer functions are optimal for classification, and what kind for regression, and why?

¹The classification is for "cloudy" or "cloud-free" (2 complementary targets), the regression is for quantifying "how much cloud" (1 target).

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