Setting best SVM hyper parameters2019 Community Moderator ElectionTune hyperparameters for cost-sensitive classificationDoes importance of SVM parameters vary for subsample of data?$chi^2$ kernel SVM performance issuehow to explain the behaviour: linear svm does better than non-linear RBFUsing GridSearchCV for custom kernel SVM in scikit-learnNyström approximation of the non-linear mapping $phi$ for a RBF kernel - what is the impact of weak duality?Poor performance of SVM after training for rare eventsIs splitting the data set into train and validation applicable in unsupervised learning?Improve precision of binary classification - SVM in MatlabHow to set hyperparameters in SVM classification
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Setting best SVM hyper parameters
2019 Community Moderator ElectionTune hyperparameters for cost-sensitive classificationDoes importance of SVM parameters vary for subsample of data?$chi^2$ kernel SVM performance issuehow to explain the behaviour: linear svm does better than non-linear RBFUsing GridSearchCV for custom kernel SVM in scikit-learnNyström approximation of the non-linear mapping $phi$ for a RBF kernel - what is the impact of weak duality?Poor performance of SVM after training for rare eventsIs splitting the data set into train and validation applicable in unsupervised learning?Improve precision of binary classification - SVM in MatlabHow to set hyperparameters in SVM classification
$begingroup$
I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm
. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?
classification svm matlab hyperparameter hyperparameter-tuning
$endgroup$
add a comment |
$begingroup$
I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm
. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?
classification svm matlab hyperparameter hyperparameter-tuning
$endgroup$
add a comment |
$begingroup$
I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm
. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?
classification svm matlab hyperparameter hyperparameter-tuning
$endgroup$
I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm
. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?
classification svm matlab hyperparameter hyperparameter-tuning
classification svm matlab hyperparameter hyperparameter-tuning
edited Mar 26 at 6:51
gin
asked Mar 26 at 6:45
gingin
1667
1667
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:
Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.
Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)
Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.
Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.
$endgroup$
add a comment |
$begingroup$
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)
You can use various techniques for that, for example:
- Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.
That can help you find an average solution fast, but remember that this comes without warranty.
Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms
Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.
Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)
$endgroup$
add a comment |
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2 Answers
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oldest
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2 Answers
2
active
oldest
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active
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$begingroup$
Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:
Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.
Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)
Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.
Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.
$endgroup$
add a comment |
$begingroup$
Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:
Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.
Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)
Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.
Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.
$endgroup$
add a comment |
$begingroup$
Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:
Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.
Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)
Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.
Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.
$endgroup$
Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:
Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.
Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)
Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.
Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.
answered Mar 26 at 11:24
georg_ungeorg_un
586
586
add a comment |
add a comment |
$begingroup$
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)
You can use various techniques for that, for example:
- Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.
That can help you find an average solution fast, but remember that this comes without warranty.
Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms
Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.
Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)
$endgroup$
add a comment |
$begingroup$
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)
You can use various techniques for that, for example:
- Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.
That can help you find an average solution fast, but remember that this comes without warranty.
Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms
Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.
Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)
$endgroup$
add a comment |
$begingroup$
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)
You can use various techniques for that, for example:
- Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.
That can help you find an average solution fast, but remember that this comes without warranty.
Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms
Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.
Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)
$endgroup$
Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)
You can use various techniques for that, for example:
- Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.
That can help you find an average solution fast, but remember that this comes without warranty.
Exhaustive Grid Search
Randomized Parameter Optimization
Genetic Algorithms
Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.
Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)
edited Mar 26 at 13:21
answered Mar 26 at 11:22
Pedro Henrique MonfortePedro Henrique Monforte
1157
1157
add a comment |
add a comment |
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