How to create an ensemble that gives precedence to a specific classifier2019 Community Moderator ElectionMeasuring performance of different classifiers with different sample sizesMulti-label text classification with minimum confidence thresholdunbalanced data classificationBad classification performance of logistic regression on imbalanced data in testing as compared to trainingHow much should I pay attention to the f1 score on this case?Sci-kit learn function to select threshold for higher recall than precisionBinary classification, precision-recall curve and thresholdsClassifier that optimizes performance on only a subset of the data?Why is the area under the precision-recall curve not used as scoring function more often?Improve precision of binary classification - SVM in Matlab
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How to create an ensemble that gives precedence to a specific classifier
2019 Community Moderator ElectionMeasuring performance of different classifiers with different sample sizesMulti-label text classification with minimum confidence thresholdunbalanced data classificationBad classification performance of logistic regression on imbalanced data in testing as compared to trainingHow much should I pay attention to the f1 score on this case?Sci-kit learn function to select threshold for higher recall than precisionBinary classification, precision-recall curve and thresholdsClassifier that optimizes performance on only a subset of the data?Why is the area under the precision-recall curve not used as scoring function more often?Improve precision of binary classification - SVM in Matlab
$begingroup$
Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I use A alone, I will get a high precision, but low recall. In other words, the number of true positives are very high, but it also incorrectly tags the rest of the labels as False. B, and C have much lower precision, but when used separately, they may (or may not) result in better recall. How can I define an ensemble classifier that gives precedence to classifier A only in cases where it labels the data as True and give more weight to the predictions of other classifiers when A predicts the label as False.
The idea is, A is already outperforming others in catching true positives and I only want to improve the recall without hurting precision.
classification prediction ensemble-modeling binary ensemble
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
$endgroup$
bumped to the homepage by Community♦ 16 hours 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 |
$begingroup$
Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I use A alone, I will get a high precision, but low recall. In other words, the number of true positives are very high, but it also incorrectly tags the rest of the labels as False. B, and C have much lower precision, but when used separately, they may (or may not) result in better recall. How can I define an ensemble classifier that gives precedence to classifier A only in cases where it labels the data as True and give more weight to the predictions of other classifiers when A predicts the label as False.
The idea is, A is already outperforming others in catching true positives and I only want to improve the recall without hurting precision.
classification prediction ensemble-modeling binary ensemble
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
$endgroup$
bumped to the homepage by Community♦ 16 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
4
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35
add a comment |
$begingroup$
Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I use A alone, I will get a high precision, but low recall. In other words, the number of true positives are very high, but it also incorrectly tags the rest of the labels as False. B, and C have much lower precision, but when used separately, they may (or may not) result in better recall. How can I define an ensemble classifier that gives precedence to classifier A only in cases where it labels the data as True and give more weight to the predictions of other classifiers when A predicts the label as False.
The idea is, A is already outperforming others in catching true positives and I only want to improve the recall without hurting precision.
classification prediction ensemble-modeling binary ensemble
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
$endgroup$
Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I use A alone, I will get a high precision, but low recall. In other words, the number of true positives are very high, but it also incorrectly tags the rest of the labels as False. B, and C have much lower precision, but when used separately, they may (or may not) result in better recall. How can I define an ensemble classifier that gives precedence to classifier A only in cases where it labels the data as True and give more weight to the predictions of other classifiers when A predicts the label as False.
The idea is, A is already outperforming others in catching true positives and I only want to improve the recall without hurting precision.
classification prediction ensemble-modeling binary ensemble
classification prediction ensemble-modeling binary ensemble
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
asked Jan 11 '18 at 19:13
Clement AttleeClement Attlee
111
111
bumped to the homepage by Community♦ 16 hours 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♦ 16 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
4
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35
add a comment |
4
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35
4
4
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Feature-Weighted Linear Stacking might be what you are looking for.
FWLS combines model predictions linearly using coefficients that are
themselves linear functions of meta-features.
In your example you can use the meta-feature "Does A label the example as True?"
answered Jan 11 '18 at 20:06
ImranImran
1,756619
$endgroup$
add a comment |
$begingroup$
based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier.
for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.
if you have data imbalance problem using stack based classifier is a little bit more challenging.
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Feature-Weighted Linear Stacking might be what you are looking for.
FWLS combines model predictions linearly using coefficients that are
themselves linear functions of meta-features.
In your example you can use the meta-feature "Does A label the example as True?"
answered Jan 11 '18 at 20:06
ImranImran
1,756619
$endgroup$
add a comment |
$begingroup$
Feature-Weighted Linear Stacking might be what you are looking for.
FWLS combines model predictions linearly using coefficients that are
themselves linear functions of meta-features.
In your example you can use the meta-feature "Does A label the example as True?"
answered Jan 11 '18 at 20:06
ImranImran
1,756619
$endgroup$
add a comment |
$begingroup$
Feature-Weighted Linear Stacking might be what you are looking for.
FWLS combines model predictions linearly using coefficients that are
themselves linear functions of meta-features.
In your example you can use the meta-feature "Does A label the example as True?"
answered Jan 11 '18 at 20:06
ImranImran
1,756619
$endgroup$
Feature-Weighted Linear Stacking might be what you are looking for.
FWLS combines model predictions linearly using coefficients that are
themselves linear functions of meta-features.
In your example you can use the meta-feature "Does A label the example as True?"
answered Jan 11 '18 at 20:06
ImranImran
1,756619
answered Jan 11 '18 at 20:06
ImranImran
1,756619
answered Jan 11 '18 at 20:06
ImranImran
1,756619
answered Jan 11 '18 at 20:06
ImranImran
1,756619
1,756619
add a comment |
add a comment |
$begingroup$
based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier.
for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.
if you have data imbalance problem using stack based classifier is a little bit more challenging.
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
$endgroup$
add a comment |
$begingroup$
based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier.
for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.
if you have data imbalance problem using stack based classifier is a little bit more challenging.
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
$endgroup$
add a comment |
$begingroup$
based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier.
for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.
if you have data imbalance problem using stack based classifier is a little bit more challenging.
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
$endgroup$
based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier.
for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.
if you have data imbalance problem using stack based classifier is a little bit more challenging.
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
answered Apr 12 '18 at 0:39
Bashar HaddadBashar Haddad
1,2621413
1,2621413
add a comment |
add a comment |
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4
$begingroup$
Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site!
$endgroup$
– Emre
Jan 11 '18 at 19:27
$begingroup$
can you describe the data? what kind of classifiers you are using?
$endgroup$
– Bashar Haddad
Apr 12 '18 at 0:35