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Training on accurate data versus noisy data
2019 Community Moderator ElectionShould I go for a 'balanced' dataset or a 'representative' dataset?Classification technique for unsupervised data?Predicting future airfare using past dataRprop is too noisyThe connection between optimization and generalizationReducing noisy data from non normal distribution of data with std deviation?Paramaeter estimation in noisy conditions with Machine Learning, possible?In Machine Learning, what is the point of using stratified sampling in selecting test set data?Is a good shuffle random state for training data really good for the model?Training deep CNN with noisy dataset
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I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. But in the future, my trained classifiers will not be seeing and performing decisions on this cleaned data; instead, it will likely have a lot more noise following some unknown distribution(s). Thus I am wondering, is it 'better' to train on noisy data if I'm going to likely see noisy data in the future, or train on good data since the noisy data should (ideally) correspond to the cleaned data if noise was removed?
Intuitively, if my goal is to simply perform classifications, then training on noisy data seems the 'better' approach since this is better representative of my expected future inputs. But if my goal is to learn about the data and what constitutes a particular decision, then training on cleaned data appears the 'better' approach.
But am I overlooking anything? Would training on the clean and/or noisy data be preferable for different reasons (e.g. generalization, overfitting, reducing dimensionality)?
machine-learning error-handling noise generalization
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
$endgroup$
add a comment |
$begingroup$
I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. But in the future, my trained classifiers will not be seeing and performing decisions on this cleaned data; instead, it will likely have a lot more noise following some unknown distribution(s). Thus I am wondering, is it 'better' to train on noisy data if I'm going to likely see noisy data in the future, or train on good data since the noisy data should (ideally) correspond to the cleaned data if noise was removed?
Intuitively, if my goal is to simply perform classifications, then training on noisy data seems the 'better' approach since this is better representative of my expected future inputs. But if my goal is to learn about the data and what constitutes a particular decision, then training on cleaned data appears the 'better' approach.
But am I overlooking anything? Would training on the clean and/or noisy data be preferable for different reasons (e.g. generalization, overfitting, reducing dimensionality)?
machine-learning error-handling noise generalization
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
$endgroup$
$begingroup$
Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55
add a comment |
$begingroup$
I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. But in the future, my trained classifiers will not be seeing and performing decisions on this cleaned data; instead, it will likely have a lot more noise following some unknown distribution(s). Thus I am wondering, is it 'better' to train on noisy data if I'm going to likely see noisy data in the future, or train on good data since the noisy data should (ideally) correspond to the cleaned data if noise was removed?
Intuitively, if my goal is to simply perform classifications, then training on noisy data seems the 'better' approach since this is better representative of my expected future inputs. But if my goal is to learn about the data and what constitutes a particular decision, then training on cleaned data appears the 'better' approach.
But am I overlooking anything? Would training on the clean and/or noisy data be preferable for different reasons (e.g. generalization, overfitting, reducing dimensionality)?
machine-learning error-handling noise generalization
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
$endgroup$
I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. But in the future, my trained classifiers will not be seeing and performing decisions on this cleaned data; instead, it will likely have a lot more noise following some unknown distribution(s). Thus I am wondering, is it 'better' to train on noisy data if I'm going to likely see noisy data in the future, or train on good data since the noisy data should (ideally) correspond to the cleaned data if noise was removed?
Intuitively, if my goal is to simply perform classifications, then training on noisy data seems the 'better' approach since this is better representative of my expected future inputs. But if my goal is to learn about the data and what constitutes a particular decision, then training on cleaned data appears the 'better' approach.
But am I overlooking anything? Would training on the clean and/or noisy data be preferable for different reasons (e.g. generalization, overfitting, reducing dimensionality)?
machine-learning error-handling noise generalization
machine-learning error-handling noise generalization
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
asked Sep 12 '18 at 20:05
Mathews24Mathews24
1056
1056
$begingroup$
Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55
add a comment |
$begingroup$
Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55
$begingroup$
Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55
$begingroup$
Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55
add a comment |
1 Answer
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$begingroup$
The answer to this severely depends on what you mean by 'noisy' data. Are the labels noisy i.e. wrong? Or are the features noisy? Or both? If only the features are noisy, definitely use the noisy data and probably also the clean data. If only the labels, definitely do not use the noisy data. If both, is it possible to correct the labels? At the very least, can you get a reliable test set (representative with correct labels)? You could try training on both the noisy and clean data and see which gives better performance. Important methods to consider here would be regularization and early stopping.
It also depends on what algorithm you are using. Linear regression, for example, is unlikely to over-fit, while neural networks are extremely sensitive to noise, and there are a variety of approaches in between those two extremes.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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$begingroup$
The answer to this severely depends on what you mean by 'noisy' data. Are the labels noisy i.e. wrong? Or are the features noisy? Or both? If only the features are noisy, definitely use the noisy data and probably also the clean data. If only the labels, definitely do not use the noisy data. If both, is it possible to correct the labels? At the very least, can you get a reliable test set (representative with correct labels)? You could try training on both the noisy and clean data and see which gives better performance. Important methods to consider here would be regularization and early stopping.
It also depends on what algorithm you are using. Linear regression, for example, is unlikely to over-fit, while neural networks are extremely sensitive to noise, and there are a variety of approaches in between those two extremes.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The answer to this severely depends on what you mean by 'noisy' data. Are the labels noisy i.e. wrong? Or are the features noisy? Or both? If only the features are noisy, definitely use the noisy data and probably also the clean data. If only the labels, definitely do not use the noisy data. If both, is it possible to correct the labels? At the very least, can you get a reliable test set (representative with correct labels)? You could try training on both the noisy and clean data and see which gives better performance. Important methods to consider here would be regularization and early stopping.
It also depends on what algorithm you are using. Linear regression, for example, is unlikely to over-fit, while neural networks are extremely sensitive to noise, and there are a variety of approaches in between those two extremes.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The answer to this severely depends on what you mean by 'noisy' data. Are the labels noisy i.e. wrong? Or are the features noisy? Or both? If only the features are noisy, definitely use the noisy data and probably also the clean data. If only the labels, definitely do not use the noisy data. If both, is it possible to correct the labels? At the very least, can you get a reliable test set (representative with correct labels)? You could try training on both the noisy and clean data and see which gives better performance. Important methods to consider here would be regularization and early stopping.
It also depends on what algorithm you are using. Linear regression, for example, is unlikely to over-fit, while neural networks are extremely sensitive to noise, and there are a variety of approaches in between those two extremes.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The answer to this severely depends on what you mean by 'noisy' data. Are the labels noisy i.e. wrong? Or are the features noisy? Or both? If only the features are noisy, definitely use the noisy data and probably also the clean data. If only the labels, definitely do not use the noisy data. If both, is it possible to correct the labels? At the very least, can you get a reliable test set (representative with correct labels)? You could try training on both the noisy and clean data and see which gives better performance. Important methods to consider here would be regularization and early stopping.
It also depends on what algorithm you are using. Linear regression, for example, is unlikely to over-fit, while neural networks are extremely sensitive to noise, and there are a variety of approaches in between those two extremes.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 21 at 19:27
PrachiPrachi
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 21 at 19:27
PrachiPrachi
1
answered Mar 21 at 19:27
PrachiPrachi
1
1
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Prachi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Training on the clean data could definitely result in overfitting. If you don't have to choose, why not try both? Training on the clean data could help you understand the data you already have better. I would definitely recommend training on noisy data for predictions. Unless there's a way to clean incoming noisy data? What's the nature of the noise?
$endgroup$
– Adrian Keister
Sep 12 '18 at 20:55