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How to determine number of leaves in decision tree analysis?
The Next CEO of Stack Overflow2019 Community Moderator ElectionDecision tree or logistic regression?How is cross validation used to prune a decision treeOrdinal feature in decision treeForecasting: How Decision Tree work?Decision tree orderingWhat are limitations of decision tree approaches to data analysis?Decision Trees Nodes vs Leaves DefinitionMulticollinearity in Decision TreeDisadvantage of decision treeHow to extract the sample split (values) of decision tree leaves ( terminal nodes) applying h2o library
$begingroup$
Would be grateful if some expert on the forum can help me understand how to decide optimum number of leaves in a decision tree analysis.
I am using SAS and if I supply leaves=6 in my model then miss-classification rates for validation & training data sets are 18.6% & 18.8% respectively. And SAS lists 5 variables which are significant.
And if I don't supply leaves count in the code and let SAS decide it, then SAS after pruning takes 10 as leaves count and miss-classification rates for validation & training data sets are 17.5% & 16.9% respectively. And SAS lists 6 variables which are significant.
Now that the miss-classification rates have reduced & trees after pruning have increased from 4 to 10, is it a good thing or it indicates overfitting?
Looking forward to opinions of experts in this group. Thanks
classification decision-trees cross-validation
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
$endgroup$
add a comment |
$begingroup$
Would be grateful if some expert on the forum can help me understand how to decide optimum number of leaves in a decision tree analysis.
I am using SAS and if I supply leaves=6 in my model then miss-classification rates for validation & training data sets are 18.6% & 18.8% respectively. And SAS lists 5 variables which are significant.
And if I don't supply leaves count in the code and let SAS decide it, then SAS after pruning takes 10 as leaves count and miss-classification rates for validation & training data sets are 17.5% & 16.9% respectively. And SAS lists 6 variables which are significant.
Now that the miss-classification rates have reduced & trees after pruning have increased from 4 to 10, is it a good thing or it indicates overfitting?
Looking forward to opinions of experts in this group. Thanks
classification decision-trees cross-validation
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
$endgroup$
add a comment |
$begingroup$
Would be grateful if some expert on the forum can help me understand how to decide optimum number of leaves in a decision tree analysis.
I am using SAS and if I supply leaves=6 in my model then miss-classification rates for validation & training data sets are 18.6% & 18.8% respectively. And SAS lists 5 variables which are significant.
And if I don't supply leaves count in the code and let SAS decide it, then SAS after pruning takes 10 as leaves count and miss-classification rates for validation & training data sets are 17.5% & 16.9% respectively. And SAS lists 6 variables which are significant.
Now that the miss-classification rates have reduced & trees after pruning have increased from 4 to 10, is it a good thing or it indicates overfitting?
Looking forward to opinions of experts in this group. Thanks
classification decision-trees cross-validation
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
$endgroup$
Would be grateful if some expert on the forum can help me understand how to decide optimum number of leaves in a decision tree analysis.
I am using SAS and if I supply leaves=6 in my model then miss-classification rates for validation & training data sets are 18.6% & 18.8% respectively. And SAS lists 5 variables which are significant.
And if I don't supply leaves count in the code and let SAS decide it, then SAS after pruning takes 10 as leaves count and miss-classification rates for validation & training data sets are 17.5% & 16.9% respectively. And SAS lists 6 variables which are significant.
Now that the miss-classification rates have reduced & trees after pruning have increased from 4 to 10, is it a good thing or it indicates overfitting?
Looking forward to opinions of experts in this group. Thanks
classification decision-trees cross-validation
classification decision-trees cross-validation
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
edited Mar 27 at 5:54
Vikrant Arora
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
asked Mar 25 at 13:33
Vikrant AroraVikrant Arora
82
82
add a comment |
add a comment |
1 Answer
1
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oldest
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I'll assume that your test and validation datasets have been created appropriately (e.g. no observations are in both test and validation sets, both sets are of appropriate size, etc.).
Overfitting means that your model fits very well to your training data but does not generalise well on unseen data (i.e. will perform poorly on your validation dataset).
Your misclassification rate on the validation set (unseen data) is decreasing, and is therefore a good thing. However, if the misclassification rate on the validation set were to increase, that would indicate overfitting.
answered Mar 27 at 9:46
bradSbradS
653113
$endgroup$
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
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active
oldest
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active
oldest
votes
$begingroup$
I'll assume that your test and validation datasets have been created appropriately (e.g. no observations are in both test and validation sets, both sets are of appropriate size, etc.).
Overfitting means that your model fits very well to your training data but does not generalise well on unseen data (i.e. will perform poorly on your validation dataset).
Your misclassification rate on the validation set (unseen data) is decreasing, and is therefore a good thing. However, if the misclassification rate on the validation set were to increase, that would indicate overfitting.
answered Mar 27 at 9:46
bradSbradS
653113
$endgroup$
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
add a comment |
$begingroup$
I'll assume that your test and validation datasets have been created appropriately (e.g. no observations are in both test and validation sets, both sets are of appropriate size, etc.).
Overfitting means that your model fits very well to your training data but does not generalise well on unseen data (i.e. will perform poorly on your validation dataset).
Your misclassification rate on the validation set (unseen data) is decreasing, and is therefore a good thing. However, if the misclassification rate on the validation set were to increase, that would indicate overfitting.
answered Mar 27 at 9:46
bradSbradS
653113
$endgroup$
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
add a comment |
$begingroup$
I'll assume that your test and validation datasets have been created appropriately (e.g. no observations are in both test and validation sets, both sets are of appropriate size, etc.).
Overfitting means that your model fits very well to your training data but does not generalise well on unseen data (i.e. will perform poorly on your validation dataset).
Your misclassification rate on the validation set (unseen data) is decreasing, and is therefore a good thing. However, if the misclassification rate on the validation set were to increase, that would indicate overfitting.
answered Mar 27 at 9:46
bradSbradS
653113
$endgroup$
I'll assume that your test and validation datasets have been created appropriately (e.g. no observations are in both test and validation sets, both sets are of appropriate size, etc.).
Overfitting means that your model fits very well to your training data but does not generalise well on unseen data (i.e. will perform poorly on your validation dataset).
Your misclassification rate on the validation set (unseen data) is decreasing, and is therefore a good thing. However, if the misclassification rate on the validation set were to increase, that would indicate overfitting.
answered Mar 27 at 9:46
bradSbradS
653113
answered Mar 27 at 9:46
bradSbradS
653113
answered Mar 27 at 9:46
bradSbradS
653113
answered Mar 27 at 9:46
bradSbradS
653113
653113
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
add a comment |
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
Thanks Brad, I understand now. Really appreciate your help. Have a nice day.
$endgroup$
– Vikrant Arora
Mar 28 at 10:47
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
$begingroup$
No problem. If you're happy with the solution, then please mark as the answer so the question can be closed.
$endgroup$
– bradS
Mar 28 at 14:43
add a comment |
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