3 ms·
The features are all encrypted using homomorphic encryption, which ends up mapping the overall distribuiton uniformly onto [0, 1]. If you play with the dataset
by antognini 10y ago
The features are all encrypted using homomorphic encryption, which ends up mapping the overall distribuiton uniformly onto [0, 1]. If you play with the dataset, though, you'll find there are some significant correlations between the different features and you can make a model that does substantially better than chance.
- daveguy 10y agoI would like to see a comparison between random generated uniform features and these features. Can you fit the noise and come up with "statistically significant" predictions? If so, is anyone doing any better than can be done on random data? If no one is doing better than that, what is the likelihood that these aren't any better than monkey with a dartboard? I would love to see peer reviewed articles from numerai with some of their behind the scenes results.
- antognini 10y agoWhenever I make a model I always do cross-validation to make sure that I'm not overfitting. If we were just fitting random noise I would always see the performance of my test set being no better than chance (or worse).