Train classifier on balanced dataset and apply on imbalanced dataset?2019 Community Moderator ElectionSklearn: How to adjust data set proportion during training, but not testingPython : How to use Multinomial Logistic Regression using SKlearnSklearn Random Forest Prediction Correlation IssueBinary Classification on small dataset < 200 samplesTraining XGBoost sequentiallyCreating categorical variable, without knowing true categories (through binning time series data)?How to add noise to supervised (binary-classifier)?Unbalanced multi-class : distribution might change as more data come inIs there any way how to make samples balanced?Create an API from EDA or ML outcome?
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Train classifier on balanced dataset and apply on imbalanced dataset?
2019 Community Moderator ElectionSklearn: How to adjust data set proportion during training, but not testingPython : How to use Multinomial Logistic Regression using SKlearnSklearn Random Forest Prediction Correlation IssueBinary Classification on small dataset < 200 samplesTraining XGBoost sequentiallyCreating categorical variable, without knowing true categories (through binning time series data)?How to add noise to supervised (binary-classifier)?Unbalanced multi-class : distribution might change as more data come inIs there any way how to make samples balanced?Create an API from EDA or ML outcome?
$begingroup$
I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works best with F-Score ~0.83.
Now, I have to apply the trained classifier on an unlabelled dataset DS2 with ~ 5 million entries (and same features). However, for DS2, the target distribution is expected to be highly unbalanced.
Is this a problem? Will the model reproduce the trained target distribution from DS1 when applied on DS2?
If yes, would another algorithm be more robust?
All the best,
Greg
python scikit-learn data ml
asked Mar 5 at 16:10
user3240855user3240855
111
$endgroup$
bumped to the homepage by Community♦ 4 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 |
$begingroup$
I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works best with F-Score ~0.83.
Now, I have to apply the trained classifier on an unlabelled dataset DS2 with ~ 5 million entries (and same features). However, for DS2, the target distribution is expected to be highly unbalanced.
Is this a problem? Will the model reproduce the trained target distribution from DS1 when applied on DS2?
If yes, would another algorithm be more robust?
All the best,
Greg
python scikit-learn data ml
asked Mar 5 at 16:10
user3240855user3240855
111
$endgroup$
bumped to the homepage by Community♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
$endgroup$
– Pedro Henrique Monforte
3 hours ago
add a comment |
$begingroup$
I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works best with F-Score ~0.83.
Now, I have to apply the trained classifier on an unlabelled dataset DS2 with ~ 5 million entries (and same features). However, for DS2, the target distribution is expected to be highly unbalanced.
Is this a problem? Will the model reproduce the trained target distribution from DS1 when applied on DS2?
If yes, would another algorithm be more robust?
All the best,
Greg
python scikit-learn data ml
asked Mar 5 at 16:10
user3240855user3240855
111
$endgroup$
I have a labelled training dataset DS1 with 1000 entries. The targets (True/False) are nearly balanced. With sklearn, I have tried several algorithms, of which the GradientBoostingClassifier works best with F-Score ~0.83.
Now, I have to apply the trained classifier on an unlabelled dataset DS2 with ~ 5 million entries (and same features). However, for DS2, the target distribution is expected to be highly unbalanced.
Is this a problem? Will the model reproduce the trained target distribution from DS1 when applied on DS2?
If yes, would another algorithm be more robust?
All the best,
Greg
python scikit-learn data ml
python scikit-learn data ml
asked Mar 5 at 16:10
user3240855user3240855
111
asked Mar 5 at 16:10
user3240855user3240855
111
asked Mar 5 at 16:10
user3240855user3240855
111
asked Mar 5 at 16:10
user3240855user3240855
111
asked Mar 5 at 16:10
user3240855user3240855
111
111
bumped to the homepage by Community♦ 4 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♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
$endgroup$
– Pedro Henrique Monforte
3 hours ago
add a comment |
1
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
$endgroup$
– Pedro Henrique Monforte
3 hours ago
1
1
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
$endgroup$
– Pedro Henrique Monforte
3 hours ago
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
$endgroup$
– Pedro Henrique Monforte
3 hours ago
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
For prediction, the GradientBoostingClassifier will only take those features in account that you fed it during training and it will then classify each observation on its own. That means that usually you don't have to worry about the target-distribution of your prediction-dataset, as long you trained your model on a sufficiently extensive training dataset.
answered Mar 5 at 17:21
georg_ungeorg_un
836
$endgroup$
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier withfeature_importances_.
$endgroup$
– georg_un
Mar 7 at 11:26
add a comment |
$begingroup$
A GBM will ultimately try to split your data into rectangular regions and assign each one a constant predicted probability, the proportion of positive training examples in that region. So yes, on the whole the model has baked in the training sample's average response rate.
I think that effect will be lessened if your data is particularly cleanly separable: if each rectangular region is pure, and your test data just happens to be more heavily inclined toward the negative regions, then it will naturally get closer to "the right" answer.
I'm not sure about other models that would be more robust in this way...an SVM probably, not being naturally probabilistic in the first place.
If your context is downsampling, logistic regression has a well-known adjustment for exactly this problem. The same adjustment (to log-odds) seems likely to help in the GBM context as well, though I'm not aware of any analysis to back it up.
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
$endgroup$
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
$begingroup$
For prediction, the GradientBoostingClassifier will only take those features in account that you fed it during training and it will then classify each observation on its own. That means that usually you don't have to worry about the target-distribution of your prediction-dataset, as long you trained your model on a sufficiently extensive training dataset.
answered Mar 5 at 17:21
georg_ungeorg_un
836
$endgroup$
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier withfeature_importances_.
$endgroup$
– georg_un
Mar 7 at 11:26
add a comment |
$begingroup$
For prediction, the GradientBoostingClassifier will only take those features in account that you fed it during training and it will then classify each observation on its own. That means that usually you don't have to worry about the target-distribution of your prediction-dataset, as long you trained your model on a sufficiently extensive training dataset.
answered Mar 5 at 17:21
georg_ungeorg_un
836
$endgroup$
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier withfeature_importances_.
$endgroup$
– georg_un
Mar 7 at 11:26
add a comment |
$begingroup$
For prediction, the GradientBoostingClassifier will only take those features in account that you fed it during training and it will then classify each observation on its own. That means that usually you don't have to worry about the target-distribution of your prediction-dataset, as long you trained your model on a sufficiently extensive training dataset.
answered Mar 5 at 17:21
georg_ungeorg_un
836
$endgroup$
For prediction, the GradientBoostingClassifier will only take those features in account that you fed it during training and it will then classify each observation on its own. That means that usually you don't have to worry about the target-distribution of your prediction-dataset, as long you trained your model on a sufficiently extensive training dataset.
answered Mar 5 at 17:21
georg_ungeorg_un
836
answered Mar 5 at 17:21
georg_ungeorg_un
836
answered Mar 5 at 17:21
georg_ungeorg_un
836
answered Mar 5 at 17:21
georg_ungeorg_un
836
836
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier withfeature_importances_.
$endgroup$
– georg_un
Mar 7 at 11:26
add a comment |
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier withfeature_importances_.
$endgroup$
– georg_un
Mar 7 at 11:26
1
1
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
Hm, my training data has 35% positive target ratio. If applied to DS2, the model finds ~35% positive ratio there as well, even though I'd expect it to be <5% ...
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
A balanced training dataset has a more or less equal distribution of the contained classes. In your case, this would be a distribution of about 50% positive. There are a lot of methods of how to generate an equal distribution by downsampling or oversampling (see this link)
$endgroup$
– georg_un
Mar 7 at 11:25
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier with
feature_importances_.$endgroup$
– georg_un
Mar 7 at 11:26
$begingroup$
At the moment there are two possibilities: either your expectation regarding the prediction data set is wrong or the classifier is inaccurate. For this reason, it is common practice to divide the labeled data into a training and a test data set (e.g. in the ratio 70:30 or 80:20) and to examine the performance of the classifier on the test data set. I would recommend doing so and then investigating your results further. Don't forget that you can print out feature importances of the GradientBoostingClassifier with
feature_importances_.$endgroup$
– georg_un
Mar 7 at 11:26
add a comment |
$begingroup$
A GBM will ultimately try to split your data into rectangular regions and assign each one a constant predicted probability, the proportion of positive training examples in that region. So yes, on the whole the model has baked in the training sample's average response rate.
I think that effect will be lessened if your data is particularly cleanly separable: if each rectangular region is pure, and your test data just happens to be more heavily inclined toward the negative regions, then it will naturally get closer to "the right" answer.
I'm not sure about other models that would be more robust in this way...an SVM probably, not being naturally probabilistic in the first place.
If your context is downsampling, logistic regression has a well-known adjustment for exactly this problem. The same adjustment (to log-odds) seems likely to help in the GBM context as well, though I'm not aware of any analysis to back it up.
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
$endgroup$
add a comment |
$begingroup$
A GBM will ultimately try to split your data into rectangular regions and assign each one a constant predicted probability, the proportion of positive training examples in that region. So yes, on the whole the model has baked in the training sample's average response rate.
I think that effect will be lessened if your data is particularly cleanly separable: if each rectangular region is pure, and your test data just happens to be more heavily inclined toward the negative regions, then it will naturally get closer to "the right" answer.
I'm not sure about other models that would be more robust in this way...an SVM probably, not being naturally probabilistic in the first place.
If your context is downsampling, logistic regression has a well-known adjustment for exactly this problem. The same adjustment (to log-odds) seems likely to help in the GBM context as well, though I'm not aware of any analysis to back it up.
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
$endgroup$
add a comment |
$begingroup$
A GBM will ultimately try to split your data into rectangular regions and assign each one a constant predicted probability, the proportion of positive training examples in that region. So yes, on the whole the model has baked in the training sample's average response rate.
I think that effect will be lessened if your data is particularly cleanly separable: if each rectangular region is pure, and your test data just happens to be more heavily inclined toward the negative regions, then it will naturally get closer to "the right" answer.
I'm not sure about other models that would be more robust in this way...an SVM probably, not being naturally probabilistic in the first place.
If your context is downsampling, logistic regression has a well-known adjustment for exactly this problem. The same adjustment (to log-odds) seems likely to help in the GBM context as well, though I'm not aware of any analysis to back it up.
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
$endgroup$
A GBM will ultimately try to split your data into rectangular regions and assign each one a constant predicted probability, the proportion of positive training examples in that region. So yes, on the whole the model has baked in the training sample's average response rate.
I think that effect will be lessened if your data is particularly cleanly separable: if each rectangular region is pure, and your test data just happens to be more heavily inclined toward the negative regions, then it will naturally get closer to "the right" answer.
I'm not sure about other models that would be more robust in this way...an SVM probably, not being naturally probabilistic in the first place.
If your context is downsampling, logistic regression has a well-known adjustment for exactly this problem. The same adjustment (to log-odds) seems likely to help in the GBM context as well, though I'm not aware of any analysis to back it up.
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
answered Mar 7 at 21:23
Ben ReinigerBen Reiniger
343210
343210
add a comment |
add a comment |
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1
$begingroup$
I think this may depend quite a bit on why your training data was balanced but your test data is not.
$endgroup$
– Ben Reiniger
Mar 5 at 22:07
$begingroup$
@BenReiniger, could you elaborate on that?
$endgroup$
– user3240855
Mar 7 at 6:58
$begingroup$
If you know the real distribution (or have an idea of it) you can use it weight training samples. (You could try using an Inner Product Detector or a Kernel Inner Product Detector if you have the probabilities of the classes, I can provide you with the module but I never used it outside of Computer Vision applications, actually no one ever did, KIPD was published 3 months ago)
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– Pedro Henrique Monforte
3 hours ago