Combining Classifiers with different Precision and Recall values2019 Community Moderator ElectionMeasuring performance of different classifiers with different sample sizesWhy are precision and recall used in the F1 score, rather than precision and NPV?How to create an ensemble that gives precedence to a specific classifierIs recall more important than precision for mass mailings?Binary classification, precision-recall curve and thresholdsPrecision and Recall if not binarySelect two best classifier using F1-score,Recall and precisionHow to get accuracy, F1, precision and recall, for a keras model?Why am I getting such a low precision after performing oversampling and undersampling?Loss function minimizing by pushing precision and recall to 0
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Combining Classifiers with different Precision and Recall values
2019 Community Moderator ElectionMeasuring performance of different classifiers with different sample sizesWhy are precision and recall used in the F1 score, rather than precision and NPV?How to create an ensemble that gives precedence to a specific classifierIs recall more important than precision for mass mailings?Binary classification, precision-recall curve and thresholdsPrecision and Recall if not binarySelect two best classifier using F1-score,Recall and precisionHow to get accuracy, F1, precision and recall, for a keras model?Why am I getting such a low precision after performing oversampling and undersampling?Loss function minimizing by pushing precision and recall to 0
$begingroup$
Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is high and the recall is low, whereas the precision for B is low and the recall is high. Is there a way to combine these two models where I can get the precision from one and recall from the other, or possibly use the metrics from one to improve those of the other?
For example, let's say these are the metrics:
A
Precision: 0.91
Recall: 0.35
B
Precision: 0.43
Recall: 0.90
As these are binary classifiers, my labels are 1 and 0, and my class of interesting is 1 (so the metrics above are for predicting 1s).
Let's say model A predicts 10 1s, and model B predicts 70 1s.
It is safe for me to say that of the 10 1s that model A has predicted, 9 are true positives.
It is also safe for me to say that of the 70 1s that model B has predicted, 60% are false positives, but the rest are 90% of the true positives in the dataset.
My question is, is there some method for me to combine these results so that I can obtain all 50 true positives from the dataset?
machine-learning classification ensemble
asked 30 mins ago
user8115948user8115948
11
$endgroup$
add a comment |
$begingroup$
Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is high and the recall is low, whereas the precision for B is low and the recall is high. Is there a way to combine these two models where I can get the precision from one and recall from the other, or possibly use the metrics from one to improve those of the other?
For example, let's say these are the metrics:
A
Precision: 0.91
Recall: 0.35
B
Precision: 0.43
Recall: 0.90
As these are binary classifiers, my labels are 1 and 0, and my class of interesting is 1 (so the metrics above are for predicting 1s).
Let's say model A predicts 10 1s, and model B predicts 70 1s.
It is safe for me to say that of the 10 1s that model A has predicted, 9 are true positives.
It is also safe for me to say that of the 70 1s that model B has predicted, 60% are false positives, but the rest are 90% of the true positives in the dataset.
My question is, is there some method for me to combine these results so that I can obtain all 50 true positives from the dataset?
machine-learning classification ensemble
asked 30 mins ago
user8115948user8115948
11
$endgroup$
add a comment |
$begingroup$
Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is high and the recall is low, whereas the precision for B is low and the recall is high. Is there a way to combine these two models where I can get the precision from one and recall from the other, or possibly use the metrics from one to improve those of the other?
For example, let's say these are the metrics:
A
Precision: 0.91
Recall: 0.35
B
Precision: 0.43
Recall: 0.90
As these are binary classifiers, my labels are 1 and 0, and my class of interesting is 1 (so the metrics above are for predicting 1s).
Let's say model A predicts 10 1s, and model B predicts 70 1s.
It is safe for me to say that of the 10 1s that model A has predicted, 9 are true positives.
It is also safe for me to say that of the 70 1s that model B has predicted, 60% are false positives, but the rest are 90% of the true positives in the dataset.
My question is, is there some method for me to combine these results so that I can obtain all 50 true positives from the dataset?
machine-learning classification ensemble
asked 30 mins ago
user8115948user8115948
11
$endgroup$
Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is high and the recall is low, whereas the precision for B is low and the recall is high. Is there a way to combine these two models where I can get the precision from one and recall from the other, or possibly use the metrics from one to improve those of the other?
For example, let's say these are the metrics:
A
Precision: 0.91
Recall: 0.35
B
Precision: 0.43
Recall: 0.90
As these are binary classifiers, my labels are 1 and 0, and my class of interesting is 1 (so the metrics above are for predicting 1s).
Let's say model A predicts 10 1s, and model B predicts 70 1s.
It is safe for me to say that of the 10 1s that model A has predicted, 9 are true positives.
It is also safe for me to say that of the 70 1s that model B has predicted, 60% are false positives, but the rest are 90% of the true positives in the dataset.
My question is, is there some method for me to combine these results so that I can obtain all 50 true positives from the dataset?
machine-learning classification ensemble
machine-learning classification ensemble
asked 30 mins ago
user8115948user8115948
11
asked 30 mins ago
user8115948user8115948
11
edited 13 mins ago
user8115948
asked 30 mins ago
user8115948user8115948
11
asked 30 mins ago
user8115948user8115948
11
asked 30 mins ago
user8115948user8115948
11
11
add a comment |
add a comment |
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