Bioengineering data comprises of 512 binary features and a single Boolean label: if the particular mixture is worth further research. There are about 1,200,000 results of previous experiments available.
Every "physical" test costs, so it is tempting to train a model to predict if a certain combination of features is worth real testing.
I tried a few models, all failed to render better accuracy than 52% on test data, which is very close to blind guess considering binary classification. Namely, a Feed-forward Multi-layer Perceptron, Random Forest, Naive Bayes models.
For a plain simple XOR example from ML starter courses, for two binary inputs, all 4 cases are required to train the network.
1.2 mln samples are, perhaps, still a too small dataset to train for 512 inputs, considering a 512-bit number of all combinations and complex cross-influence.
Thinking from the opposite, how to prove that it is impossible to train any model given available training data?