I have a dataset of ~13k entries, 1025 features, 28 classes, cleaned. I did feature selection then scaling then fitted into an mlpclassifier and with some hyper parameters tuning got 75% score.

I’m looking for ideas to improve my results. Mplclassifier got the highest result in comparison to random forest, Hgradient boosting or svm on a stratified sample. Oh and I can’t use tensorflow on my hardware.