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I'm looking to tune the parameters for sklearn's MLP classifier but don't know which to tune/how many options to give them? Example is learning rate. should i give it[.0001,.001,.01,.1,.2,.3]? or is that too many, too little etc.. i have no basis to know what is a good range for any of the parameters. Processing power is limited so i can't just test the full range. If anyone has a general guide of which are the most important to tune and a general range that would help me a lot moving forward. Thanks!

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  • $\begingroup$ this is the art of machine learning, all these parameters have no magic values, it is a systemartic trial and error process in the end $\endgroup$ – Nikos M. Jul 29 '20 at 19:19
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Usually learning rate would be between 0.0 to 1.0 , please go ahead by specifying values in those range. This thread would be definitely helpful for your further research and reference

Thanks, Durga

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  • $\begingroup$ That's great for learning rate thank you, for the other parameters such as alpha, max_itr, and hidden layer size do you have any suggestions? $\endgroup$ – Joseph Hodson Jul 29 '20 at 17:50

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