Hi Richard,
Umap generates useful features from the dataset, while reducing dimensionality. Unlike PCA, Umap works with the Numerai dataset.
I got the idea, from Marcos Lopez de Prado’s lecture:
I’ve been using it for a while.
See here:
Numerai (a newer but apparently improved version)
Numerai (since round 275)
It won’t hit the #1 spot on the leaderboard based on CORR, but the created model has ~10% correlation with the metamodel and MMC/CORR ratio is good. It will be interesting to see it’s TC score.
Best part of it is, that it’s very simple:
fit = umap.UMAP(n_components=100, min_dist=0)
transformed_data = fit.fit_transform(data)
Because it’s learns embedding without the labels, I can use test AND live for the umap model as well.
Once the dataset is transformed you can train any model on it.