Unsupervised Learning via Supervised Learning
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I am interested in exploring some data using unsupervised learning, but I do not wish to limit myself to simple clustering [for example]. Given N features, would it be reasonable to simply run a decision tree [or neural network] N times, each time using a different of those N features as the label [with the remaining subset as features], and then simply examine the "feature importance" w.r.t. various features and labels from this set of N trained models? This would unearth various relationships assuming I have a reasonable number of features [say 100]? This kind of reminds of of association rule learning?
unsupervised-learning supervised-learning
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acarteracarter
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I am interested in exploring some data using unsupervised learning, but I do not wish to limit myself to simple clustering [for example]. Given N features, would it be reasonable to simply run a decision tree [or neural network] N times, each time using a different of those N features as the label [with the remaining subset as features], and then simply examine the "feature importance" w.r.t. various features and labels from this set of N trained models? This would unearth various relationships assuming I have a reasonable number of features [say 100]? This kind of reminds of of association rule learning?
unsupervised-learning supervised-learning
asked 2 mins ago
acarteracarter
1
New contributor
acarter is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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$begingroup$
I am interested in exploring some data using unsupervised learning, but I do not wish to limit myself to simple clustering [for example]. Given N features, would it be reasonable to simply run a decision tree [or neural network] N times, each time using a different of those N features as the label [with the remaining subset as features], and then simply examine the "feature importance" w.r.t. various features and labels from this set of N trained models? This would unearth various relationships assuming I have a reasonable number of features [say 100]? This kind of reminds of of association rule learning?
unsupervised-learning supervised-learning
asked 2 mins ago
acarteracarter
1
New contributor
acarter is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am interested in exploring some data using unsupervised learning, but I do not wish to limit myself to simple clustering [for example]. Given N features, would it be reasonable to simply run a decision tree [or neural network] N times, each time using a different of those N features as the label [with the remaining subset as features], and then simply examine the "feature importance" w.r.t. various features and labels from this set of N trained models? This would unearth various relationships assuming I have a reasonable number of features [say 100]? This kind of reminds of of association rule learning?
unsupervised-learning supervised-learning
unsupervised-learning supervised-learning
asked 2 mins ago
acarteracarter
1
New contributor
acarter is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
acarteracarter
1
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asked 2 mins ago
acarteracarter
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asked 2 mins ago
acarteracarter
1
asked 2 mins ago
acarteracarter
1
1
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acarter is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
acarter is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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