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Applying to real-valued target outputs #197

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hpoit opened this issue Apr 26, 2016 · 4 comments
Open

Applying to real-valued target outputs #197

hpoit opened this issue Apr 26, 2016 · 4 comments

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@hpoit
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hpoit commented Apr 26, 2016

What are the advantages of applying Mocha to real-valued target outputs, and how do I do it?

@mcreel
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mcreel commented Apr 26, 2016

With real valued outputs, you're using the net to do nonlinear regression.
An example of how to do it is at
https://github.com/mcreel/NeuralNetsForIndirectInference.jl

On Tue, Apr 26, 2016 at 8:37 PM, Henry Poitras notifications@github.com
wrote:

What are the advantages of applying Mocha to real-valued target outputs,
and how do I do it?


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#197

@hpoit
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hpoit commented Apr 26, 2016

Thanks

@mcreel
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mcreel commented Apr 27, 2016

Hi Henry,
With no hidden layer and without using any nonlinear activation function, a
simple NN with resl valued outputs is just a linear regression model.
Training the net by backpropagation is a slow way to learn the parameters.
The fast way is to use the OLS solution. But conceptually, the net is a
linear regression model. Once the activation function is nonlinear, and
with hidden layers, the net is a nonlinear regression model, often with a
great number of parameters. The nice thing about neural nets is that even
if they have many parameters, compared to what someone who works with
ordinary regression models would be used to, they learn to set the
superfluous parameters to zero, or close to it. This is supposing that you
have a large training and testing set of data, with small data sets,
superfluous parameters is more of a problem.

In my experience, working with problems where data sets can be very large,
trying out several different net configurations (numbers of neurons and
layers) and choosing the net that gives best performance with the testing
set has given very nice results. I don't have experience with problems with
small data sets, where selecting the configuration would be more difficult
(I imagine).

On Tue, Apr 26, 2016 at 11:05 PM, Henry Poitras notifications@github.com
wrote:

Thanks Michael. I wonder if it makes sense to program the net to identify
and apply linear and/or nonlinear regressions when applicable?


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#197 (comment)

@hpoit
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hpoit commented Apr 27, 2016

@pluskid Could you guide me in adjusting the net to correctly predict the targets on train.csv?

https://s3-sa-east-1.amazonaws.com/nu-static/workable-data-science/data-science-puzzle.zip

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