Scikit-Learn Gaussian Mixture: How can log-probabilities be positive? [closed]
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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?
probability clustering python scikit-learn gaussian-mixture
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
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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?
probability clustering python scikit-learn gaussian-mixture
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
add a comment |
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?
probability clustering python scikit-learn gaussian-mixture
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?
probability clustering python scikit-learn gaussian-mixture
probability clustering python scikit-learn gaussian-mixture
asked Dec 20 '18 at 18:50
Eduardo G
364
364
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
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
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1 Answer
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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.
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
add a comment |
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.
add a comment |
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.
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.
answered Dec 20 '18 at 22:05
Anony-Mousse
29.6k54178
29.6k54178
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