I have a dataset that contains roughly 21m 768-dimension vectors (which comes in at 60GB large!). I am looking to using Sklearn to train an 8-nearest neighbour model on this data, but I'm not sure how long it would take or if it would be feasible. The following is my code, which is fairly standard:
knn = KNeighborsClassifier(n_neighbors = 8) knn.fit(X_train,y_train)
I've tried looking into this but there appears to be little on it. How long, very roughly, would this take to complete? Would PCA be a good way to reduce the amount of data being used during learning?
I know this may not be a StackExchange-type question, but sklearn is very scarce on forums and communities. Thanks