Not OP. This question is being reposted to preserve technical content removed from elsewhere. Feel free to add your own answers/discussion.
Original question:
I got a data set from high performance liquid chromatography, because hplc is expensive we only got about 39 data point. Each data point is 9 dimension, representing 9different substances concentration. I tried different network and the accuracy is not higher than 50%. (We have four classes) however the KNN has a accuracy of more than 90%. I remember hearing that neural network is not good on small data set. Is this the reason? I have not tried svm or other traditional machine learning models yet. Should I try them if yes which one
That’s almost definitely it. Neural networks are good for high-dimensional problems with lots of available training data.