Yes,
https://github.com/apache/spark/blob/v1.5.0/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala#L158
is the method you are interested in. It does normalize the
probabilities and return them to non-log-space. So you can use
predictProbabilities to get the actual posteri
Thanks Sean. As far as I can see probabilities are NOT normalized;
denominator isn't implemented in either v1.1.0 or v1.5.0 (by denominator,
I refer to the probability of feature X). So, for given lambda, how to
compute the denominator? FYI:
https://github.com/apache/spark/blob/v1.5.0/mllib/src/mai
The log probabilities are unlikely to be very large, though the
probabilities may be very small. The direct answer is to exponentiate
brzPi + brzTheta * testData.toBreeze -- apply exp(x).
I have forgotten whether the probabilities are normalized already
though. If not you'll have to normalize to g
great. so, provided that *model.theta* represents the log-probabilities and
(hence the result of *brzPi + brzTheta * testData.toBreeze* is a big number
too), how can I get back the *non-*log-probabilities which - apparently -
are bounded between *0.0 and 1.0*?
*// Adamantios*
On Tue, Sep 1, 2
(pedantic: it's the log-probabilities)
On Tue, Sep 1, 2015 at 10:48 AM, Yanbo Liang wrote:
> Actually
> brzPi + brzTheta * testData.toBreeze
> is the probabilities of the input Vector on each class, however it's a
> Breeze Vector.
> Pay attention the index of this Vector need to map to the corres
Actually
brzPi + brzTheta * testData.toBreeze
is the probabilities of the input Vector on each class, however it's a
Breeze Vector.
Pay attention the index of this Vector need to map to the corresponding
label index.
2015-08-28 20:38 GMT+08:00 Adamantios Corais :
> Hi,
>
> I am trying to change t
Hi,
I am trying to change the following code so as to get the probabilities of
the input Vector on each class (instead of the class itself with the
highest probability). I know that this is already available as part of the
most recent release of Spark but I have to use Spark 1.1.0.
Any help is ap
