2
$\begingroup$

I am fitting a Gaussian Mixture model:
gm = GaussianMixture(n_components=K) gm.fit(features)

When I do:

gm.score_samples(features)

All of the scores, which are supposed to be: "weighted log probabilities for each sample." are positive.
Are they actually log-probabilities?

$\endgroup$

closed as off-topic by Xi'an, kjetil b halvorsen, Peter Flom Dec 21 '18 at 12:48

This question appears to be off-topic. The users who voted to close gave this specific reason:

  • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Xi'an, kjetil b halvorsen, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.

5
$\begingroup$

They supposedly are probability densities, not probabilities.

A probability density can be larger than 1, hence the log can be positive.

The documentation of sklearn should probably be fixed to reflect this.

$\endgroup$

Not the answer you're looking for? Browse other questions tagged or ask your own question.