WEKA Random Forest J48 Attribute Importance
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I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. This process of generating these indices is quite resource intensive.
I would like to determine the importance of the various attributes. Is there a way for these classifiers to report this?
I see J48 produces a decision tree, is it safe to day the attributes used at the root of the tree are most important?
machine-learning random-forest decision-trees weka
asked Jan 11 at 0:34
JamesWatJamesWat
61
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bumped to the homepage by Community♦ 12 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. This process of generating these indices is quite resource intensive.
I would like to determine the importance of the various attributes. Is there a way for these classifiers to report this?
I see J48 produces a decision tree, is it safe to day the attributes used at the root of the tree are most important?
machine-learning random-forest decision-trees weka
asked Jan 11 at 0:34
JamesWatJamesWat
61
$endgroup$
bumped to the homepage by Community♦ 12 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. This process of generating these indices is quite resource intensive.
I would like to determine the importance of the various attributes. Is there a way for these classifiers to report this?
I see J48 produces a decision tree, is it safe to day the attributes used at the root of the tree are most important?
machine-learning random-forest decision-trees weka
asked Jan 11 at 0:34
JamesWatJamesWat
61
$endgroup$
I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. This process of generating these indices is quite resource intensive.
I would like to determine the importance of the various attributes. Is there a way for these classifiers to report this?
I see J48 produces a decision tree, is it safe to day the attributes used at the root of the tree are most important?
machine-learning random-forest decision-trees weka
machine-learning random-forest decision-trees weka
asked Jan 11 at 0:34
JamesWatJamesWat
61
asked Jan 11 at 0:34
JamesWatJamesWat
61
asked Jan 11 at 0:34
JamesWatJamesWat
61
asked Jan 11 at 0:34
JamesWatJamesWat
61
asked Jan 11 at 0:34
JamesWatJamesWat
61
61
bumped to the homepage by Community♦ 12 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 12 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
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If you must use WEKA, I would suggest looking in the Attribute Selection panel. In this panel, you can rank attributes by information gain, as well as look at which subsets of attributes perform best. To my knowledge, you can't get this information directly from the classifier in the WEKA Explorer, but it is quite easy to get in other frameworks such as scikit-learn: see this example in the docs.
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
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$begingroup$
If you must use WEKA, I would suggest looking in the Attribute Selection panel. In this panel, you can rank attributes by information gain, as well as look at which subsets of attributes perform best. To my knowledge, you can't get this information directly from the classifier in the WEKA Explorer, but it is quite easy to get in other frameworks such as scikit-learn: see this example in the docs.
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
$endgroup$
add a comment |
$begingroup$
If you must use WEKA, I would suggest looking in the Attribute Selection panel. In this panel, you can rank attributes by information gain, as well as look at which subsets of attributes perform best. To my knowledge, you can't get this information directly from the classifier in the WEKA Explorer, but it is quite easy to get in other frameworks such as scikit-learn: see this example in the docs.
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
$endgroup$
add a comment |
$begingroup$
If you must use WEKA, I would suggest looking in the Attribute Selection panel. In this panel, you can rank attributes by information gain, as well as look at which subsets of attributes perform best. To my knowledge, you can't get this information directly from the classifier in the WEKA Explorer, but it is quite easy to get in other frameworks such as scikit-learn: see this example in the docs.
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
$endgroup$
If you must use WEKA, I would suggest looking in the Attribute Selection panel. In this panel, you can rank attributes by information gain, as well as look at which subsets of attributes perform best. To my knowledge, you can't get this information directly from the classifier in the WEKA Explorer, but it is quite easy to get in other frameworks such as scikit-learn: see this example in the docs.
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
answered Jan 11 at 3:12
timleatharttimleathart
2,169726
2,169726
add a comment |
add a comment |
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