Hello ,
So I have this Data set (9230 * 460) where 460 is the number of features and the target is (9230 * 1) which contains 10 different types of classes [1…10] , I have tried RFC with grid search and gave me 75% accuracy now I am going to try a Forward Neural Network with pytorch . My question is how can one know which is the best approach (algorithm) to have the best classification accuracy of course without getting over-fitting and thank you.
P.S : I’ll have more data soon , the data that I have now is for the year 2016 , I’ll have for 2015 and 2017 , is the RNN with LSTM the best answer there ? ( my features are 460 observations from crops taken from a satellite image for parcels  on different dates )
Cheers.