1

I have a dataset of feature/label pairs. My labels are probabilities of each feature vector to belong to the K classes. Here is an example for K = 3:

`D1 = { (V0, [0.33,0.33,0.33]), (V1, [0.9,0.07,0.03]), (V2, [0.5,0.25,0.25])... }`

The probabilities are normalized for a given data point. Yet the task is more a multilabel one, and it would make more sense to have independent Bernoulli distributions e.g.

`D2 = { (V0, [0.9,0.9,0.9]), (V1, [0.99,0.0,0.0]), (V2, [0.9,0.2,0.5])... }`

Is there a trick (smart heuristic) out there which would allow me to transform D1 into D2 based on the way the probability weights are distributed in D1?

Can you be more specific? How did you exactly got from

`(V0, [0.33,0.33,0.33])`

to`(V0, [0.9,0.9,0.9])`

or from`(V0, [0.9,0.9,0.9])`

to`(V1, [0.99,0.0,0.0])`

? – Antonio Jurić – 2019-02-25T13:05:39.430That's the question :) – user3091275 – 2019-02-26T16:27:37.573