Random Forest Classifier - KFold CV Tunes Very Deep Trees --> Overfitting?
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I'm tuning a random forest in python and am wondering if/why my model is overfit. The dataset is described below:
- 1700 Positive Cases / 54000 total cases ~ 3.2% (unbalanced)
- 50 Numerical Features,~450 label/hot encoded features (post data
reduction) - 10Fold CV using 85% of data, with 15% hold out for final test
- Classification Metrics = AUC or F1 (as data is imbalanced)
The results I get tend to suggest using very deep trees i.e depth 18 with no restriction on number of samples per split = 2(default). In this case, Train AUC was 99.9% , Max Test AUC was 84%. My scores are also almost monotonically increasing in max depth of trees. Given the results and how deep the trees are - I suspect the model is overfit? If this is the case then why would I not observe some sort of out of sample reduction in AUC as depth and min_samples_split typically constrain the random forest? Or have I overlooked anything in tuning?
My ranges in CV Grid Search are more or less:
- n_estimates : range(100,1000,by=100)
- max_features : {sqrt(p),0.3,0.4,0.5}
- max_depth : range(2,20,by=1)
- min_samples_split : range(2,50,by=1)
- class_weights : {balanced,None}
Thanks
machine-learning random-forest cross-validation unbalanced-classes
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
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bumped to the homepage by Community♦ 23 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'm tuning a random forest in python and am wondering if/why my model is overfit. The dataset is described below:
- 1700 Positive Cases / 54000 total cases ~ 3.2% (unbalanced)
- 50 Numerical Features,~450 label/hot encoded features (post data
reduction) - 10Fold CV using 85% of data, with 15% hold out for final test
- Classification Metrics = AUC or F1 (as data is imbalanced)
The results I get tend to suggest using very deep trees i.e depth 18 with no restriction on number of samples per split = 2(default). In this case, Train AUC was 99.9% , Max Test AUC was 84%. My scores are also almost monotonically increasing in max depth of trees. Given the results and how deep the trees are - I suspect the model is overfit? If this is the case then why would I not observe some sort of out of sample reduction in AUC as depth and min_samples_split typically constrain the random forest? Or have I overlooked anything in tuning?
My ranges in CV Grid Search are more or less:
- n_estimates : range(100,1000,by=100)
- max_features : {sqrt(p),0.3,0.4,0.5}
- max_depth : range(2,20,by=1)
- min_samples_split : range(2,50,by=1)
- class_weights : {balanced,None}
Thanks
machine-learning random-forest cross-validation unbalanced-classes
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
$endgroup$
bumped to the homepage by Community♦ 23 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'm tuning a random forest in python and am wondering if/why my model is overfit. The dataset is described below:
- 1700 Positive Cases / 54000 total cases ~ 3.2% (unbalanced)
- 50 Numerical Features,~450 label/hot encoded features (post data
reduction) - 10Fold CV using 85% of data, with 15% hold out for final test
- Classification Metrics = AUC or F1 (as data is imbalanced)
The results I get tend to suggest using very deep trees i.e depth 18 with no restriction on number of samples per split = 2(default). In this case, Train AUC was 99.9% , Max Test AUC was 84%. My scores are also almost monotonically increasing in max depth of trees. Given the results and how deep the trees are - I suspect the model is overfit? If this is the case then why would I not observe some sort of out of sample reduction in AUC as depth and min_samples_split typically constrain the random forest? Or have I overlooked anything in tuning?
My ranges in CV Grid Search are more or less:
- n_estimates : range(100,1000,by=100)
- max_features : {sqrt(p),0.3,0.4,0.5}
- max_depth : range(2,20,by=1)
- min_samples_split : range(2,50,by=1)
- class_weights : {balanced,None}
Thanks
machine-learning random-forest cross-validation unbalanced-classes
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
$endgroup$
I'm tuning a random forest in python and am wondering if/why my model is overfit. The dataset is described below:
- 1700 Positive Cases / 54000 total cases ~ 3.2% (unbalanced)
- 50 Numerical Features,~450 label/hot encoded features (post data
reduction) - 10Fold CV using 85% of data, with 15% hold out for final test
- Classification Metrics = AUC or F1 (as data is imbalanced)
The results I get tend to suggest using very deep trees i.e depth 18 with no restriction on number of samples per split = 2(default). In this case, Train AUC was 99.9% , Max Test AUC was 84%. My scores are also almost monotonically increasing in max depth of trees. Given the results and how deep the trees are - I suspect the model is overfit? If this is the case then why would I not observe some sort of out of sample reduction in AUC as depth and min_samples_split typically constrain the random forest? Or have I overlooked anything in tuning?
My ranges in CV Grid Search are more or less:
- n_estimates : range(100,1000,by=100)
- max_features : {sqrt(p),0.3,0.4,0.5}
- max_depth : range(2,20,by=1)
- min_samples_split : range(2,50,by=1)
- class_weights : {balanced,None}
Thanks
machine-learning random-forest cross-validation unbalanced-classes
machine-learning random-forest cross-validation unbalanced-classes
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
asked Jun 19 '18 at 22:32
NahyyzNahyyz
61
61
bumped to the homepage by Community♦ 23 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♦ 23 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 |
2 Answers
2
active
oldest
votes
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A drop in performance between train and test datasets is a sign of overfitting.
Given the extremely unbalanced data, passing sample_weight argument to RandomForest().fit() to rebalance the classes should help.
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
$endgroup$
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
add a comment |
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Random Forests don't overfit, the more depth you add, the more accuracy and less performance you will get.
answered Jun 20 '18 at 0:03
EugenEugen
795
$endgroup$
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
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$begingroup$
A drop in performance between train and test datasets is a sign of overfitting.
Given the extremely unbalanced data, passing sample_weight argument to RandomForest().fit() to rebalance the classes should help.
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
$endgroup$
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
add a comment |
$begingroup$
A drop in performance between train and test datasets is a sign of overfitting.
Given the extremely unbalanced data, passing sample_weight argument to RandomForest().fit() to rebalance the classes should help.
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
$endgroup$
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
add a comment |
$begingroup$
A drop in performance between train and test datasets is a sign of overfitting.
Given the extremely unbalanced data, passing sample_weight argument to RandomForest().fit() to rebalance the classes should help.
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
$endgroup$
A drop in performance between train and test datasets is a sign of overfitting.
Given the extremely unbalanced data, passing sample_weight argument to RandomForest().fit() to rebalance the classes should help.
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
edited Jun 20 '18 at 14:12
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
answered Jun 20 '18 at 0:20
Brian SpieringBrian Spiering
4,2881129
4,2881129
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
add a comment |
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
$begingroup$
I'm using class_weights = "balanced" to take into account imbalances in the dataset and penalize the cost function. It seems the two are related per stackoverflow.com/questions/32492550/…
$endgroup$
– Nahyyz
Jun 20 '18 at 3:15
add a comment |
$begingroup$
Random Forests don't overfit, the more depth you add, the more accuracy and less performance you will get.
answered Jun 20 '18 at 0:03
EugenEugen
795
$endgroup$
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
add a comment |
$begingroup$
Random Forests don't overfit, the more depth you add, the more accuracy and less performance you will get.
answered Jun 20 '18 at 0:03
EugenEugen
795
$endgroup$
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
add a comment |
$begingroup$
Random Forests don't overfit, the more depth you add, the more accuracy and less performance you will get.
answered Jun 20 '18 at 0:03
EugenEugen
795
$endgroup$
Random Forests don't overfit, the more depth you add, the more accuracy and less performance you will get.
answered Jun 20 '18 at 0:03
EugenEugen
795
answered Jun 20 '18 at 0:03
EugenEugen
795
answered Jun 20 '18 at 0:03
EugenEugen
795
answered Jun 20 '18 at 0:03
EugenEugen
795
795
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
add a comment |
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
$begingroup$
Using deep trees is the problem, you have to increase the depth of your forest instead. Random Forests use very simple trees but thousands or 10th of thousands of them for that they can't overfit: Read the paper instead of downvoting.. projecteuclid.org/download/pdf_1/euclid.aos/1032181157
$endgroup$
– Eugen
Jun 20 '18 at 1:48
add a comment |
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