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https://issues.apache.org/jira/browse/MADLIB-998?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Rahul Iyer updated MADLIB-998:
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Description:
Add a class weight parameter to add weights to specific dependent variable
values. This is useful for data with unbalanced classes i.e. situations where 1
class has (far) fewer data points compared to other class(es).
The general format will be similar to that in scikit-learn, described below:
class_weight: Sets the weight for the positive and negative classes. If not
given, all classes are set to have weight one.
If class_weight = balanced, values of y are automatically adjusted as inversely
proportional to class frequencies in the input data i.e. the weights are set as
n_samples / (n_classes * bincount ( y )).
Alternatively, class_weight can be a mapping, giving the weight for each class.
Eg. For dependent variable values 'a' and 'b', the class_weight can be
{a: 2, b: 3}. This would lead to each 'a' tuple's y value multiplied by 2 and
each 'b' y value will be multiplied by 3.
For regression, the class weights are always one.
'class_weight' will be part of the optional 'params' argument.
was:
Add a class weight parameter to add weights to specific dependent variable
values. This is useful for data with unbalanced classes i.e. situations where 1
class has (far) fewer data points compared to other class(es).
The general format will be similar to that in scikit-learn, described below:
class_weight: Sets the weight for the positive and negative classes. If not
given, all classes are set to have weight one.
If class_weight = balanced, values of y are automatically adjusted as inversely
proportional to class frequencies in the input data i.e. the weights are set as
n_samples / (n_classes * bincount(y)).
Alternatively, class_weight can be a mapping, giving the weight for each class.
Eg. For dependent variable values 'a' and 'b', the class_weight can be
{a: 2, b: 3}. This would lead to each 'a' tuple's y value multiplied by 2 and
each 'b' y value will be multiplied by 3.
For regression, the class weights are always one.
'class_weight' will be part of the optional 'params' argument.
> Class weights for SVM
> ---------------------
>
> Key: MADLIB-998
> URL: https://issues.apache.org/jira/browse/MADLIB-998
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Support Vector Machines
> Reporter: Rahul Iyer
>
> Add a class weight parameter to add weights to specific dependent variable
> values. This is useful for data with unbalanced classes i.e. situations where
> 1 class has (far) fewer data points compared to other class(es).
> The general format will be similar to that in scikit-learn, described below:
> class_weight: Sets the weight for the positive and negative classes. If not
> given, all classes are set to have weight one.
> If class_weight = balanced, values of y are automatically adjusted as
> inversely proportional to class frequencies in the input data i.e. the
> weights are set as n_samples / (n_classes * bincount ( y )).
> Alternatively, class_weight can be a mapping, giving the weight for each
> class.
> Eg. For dependent variable values 'a' and 'b', the class_weight can be
> {a: 2, b: 3}. This would lead to each 'a' tuple's y value multiplied by 2 and
> each 'b' y value will be multiplied by 3.
> For regression, the class weights are always one.
> 'class_weight' will be part of the optional 'params' argument.
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