That was it, Thanks. (Posting here so people know it's the right answer in
case they have the same need :) ).
sowen wrote
Probabilities won't sum to 1 since this expression doesn't incorporate
the probability of the evidence, I imagine? it's constant across
classes so is usually excluded. It
Probabilities won't sum to 1 since this expression doesn't incorporate
the probability of the evidence, I imagine? it's constant across
classes so is usually excluded. It would appear as a -
log(P(evidence)) term.
On Tue, Dec 2, 2014 at 10:44 AM, MariusFS marius.fete...@sien.com wrote:
Are we
Are we sure that exponentiating will give us the probabilities? I did some
tests by cloning the MLLIb class and adding the required code but the
calculated probabilities do not add up to 1.
I tried something like :
def predictProbs(testData: Vector): (BDV[Double], BDV[Double]) = {
val
Not directly. If you could access brzPi and brzTheta in the
NaiveBayesModel, you could repeat its same computation in predict() and
exponentiate it to get back class probabilities, since input and internal
values are in log space.
Hm I wonder how people feel about exposing those fields or a
Thanks for the answer. The variables brzPi and brzTheta are declared private.
I am writing my code with Java otherwise I could have replicated the scala
class and performed desired computation, which is as I observed a
multiplication of brzTheta with test vector and adding this value to brzPi.
It's hacky, but you could access these fields via reflection. It'd be
better to propose opening them up in a PR.
On Mon, Nov 10, 2014 at 9:25 AM, jatinpreet jatinpr...@gmail.com wrote:
Thanks for the answer. The variables brzPi and brzTheta are declared private.
I am writing my code with Java
Thanks, I will try it out and raise a request for making the variables
accessible.
An unrelated question, do you think the probability value thus calculated
will be a good measure of confidence in prediction? I have been reading
mixed opinions about the same.
Jatin
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Novice Big Data
