I've been working with the Adult Census Income dataset from UCI http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, and one with a neural net using Keras/Tensorflow.
So I'm not interested in code hints or anything, but I have a general question about machine learning-I have a significantly higher accuracy using the gradient boosted classifier than I do with the neural net.
In general, is it possible for my (or any, really) neural net to reach the same accuracy as any other kind of supervised learning? Does it just take a lot of hard work and elbow grease to tune the neural net well enough? I'm working with the same dataset, using the same feature engineering for both the NN and the gradient boosted classifier.