Insights from RandomForestRegressor (or any RF output)
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B eyond the feature importance calculations from a RandomForestRegressor in sklearn, what are some good strategies to draw conclusions from the trees generated from the training? Number of features is ~20, being able to show how certain features impact each other is what I'm looking for.
The graphical representation of the trees are a little to deep to analyse. I've seen other methods to show which features impact positive or negative outcomes for the target, but I'm more looking for how feature values affect each other.
scikit-learn random-forest decision-trees
asked 56 secs ago
joelangdom23023joelangdom23023
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$begingroup$
B eyond the feature importance calculations from a RandomForestRegressor in sklearn, what are some good strategies to draw conclusions from the trees generated from the training? Number of features is ~20, being able to show how certain features impact each other is what I'm looking for.
The graphical representation of the trees are a little to deep to analyse. I've seen other methods to show which features impact positive or negative outcomes for the target, but I'm more looking for how feature values affect each other.
scikit-learn random-forest decision-trees
asked 56 secs ago
joelangdom23023joelangdom23023
1
New contributor
joelangdom23023 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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add a comment |
$begingroup$
B eyond the feature importance calculations from a RandomForestRegressor in sklearn, what are some good strategies to draw conclusions from the trees generated from the training? Number of features is ~20, being able to show how certain features impact each other is what I'm looking for.
The graphical representation of the trees are a little to deep to analyse. I've seen other methods to show which features impact positive or negative outcomes for the target, but I'm more looking for how feature values affect each other.
scikit-learn random-forest decision-trees
asked 56 secs ago
joelangdom23023joelangdom23023
1
New contributor
joelangdom23023 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
B eyond the feature importance calculations from a RandomForestRegressor in sklearn, what are some good strategies to draw conclusions from the trees generated from the training? Number of features is ~20, being able to show how certain features impact each other is what I'm looking for.
The graphical representation of the trees are a little to deep to analyse. I've seen other methods to show which features impact positive or negative outcomes for the target, but I'm more looking for how feature values affect each other.
scikit-learn random-forest decision-trees
scikit-learn random-forest decision-trees
asked 56 secs ago
joelangdom23023joelangdom23023
1
New contributor
joelangdom23023 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 56 secs ago
joelangdom23023joelangdom23023
1
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asked 56 secs ago
joelangdom23023joelangdom23023
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asked 56 secs ago
joelangdom23023joelangdom23023
1
asked 56 secs ago
joelangdom23023joelangdom23023
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