How to select the number of samples to train a Machine Learning algorithm?

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Jose Marques
Jose Marques 2019-1-31
评论： Greg Heath 2019-2-4
I working in a dataset of 12000 samples concerning about 5 years of an industrial process.
It is likely that during this time the plant has undergone changes (equipments, the performance drop itself, chemical products).
Is there a tool for identifying the best subset of this data? In my view, a temporal cut in the data could increase the quality of the models created.
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Jose Marques
Jose Marques 2019-1-31
Thanks for the comment!
The dataset has 426 inputs (I am using techniques for feature selection too).
I am using four algorithms to create the models: Regression Tree, Bagged Trees, SVM and Neural Networks.
Greg Heath
Greg Heath 2019-2-4
As a common sense rule of thumb I try to use at least 10 to 30 times as many training points as unknown parameters that have to be estimated.
In addition I use 10 to 20 sets of random initial weights.
I assume , of course, that you ave examined plots of the data to initialize your common sense.
Hope this Helps
Greg

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BERGHOUT Tarek
BERGHOUT Tarek 2019-2-3
u can use deep belif networks ; they are the best for feature sellection and mapping; and train you network by driven chunks of data "by randomly chosing a pairs of (inputs,targets)" and in the same time pire attention to your approximation function you must keep your error function in its local minimam. deep belif nets depands on a set of stacked auto_encoders that allows to tune all the parameters of the networks with small amount of training data
https://www.youtube.com/watch?v=E2Mt_7qked0

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