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https://issues.apache.org/jira/browse/SPARK-9610?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14738596#comment-14738596
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Nickolay Yakushev edited comment on SPARK-9610 at 9/10/15 11:22 AM:
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Sometimes an algorithm for non-weighted data may be transformed to weighted in
more than one way. It may depend on what the weight is.
I wish I could give a better example.
For example, cardinality of the union of two sets c = |A U B|.
* Non-weighted case (identical weight): A=\{1\}, B=\{1\}, c=1;
* Weight is the degree of truth: A=\{1 -> 0.8\}, B=\{1 -> 0.5\}, c=0.8 or 1.0;
* Weight is quantity: A=\{1 -> 0.8\}, B=\{1 -> 0.5\}, c = 1.3
I don't know if there's any difference for the algorithms in the list.
was (Author: quanty):
Sometimes an algorithm for non-weighted data may be transformed to weighted in
more than one way. It may depend on what the weight is.
I wish I could give a better example.
For example, cardinality of the union of two sets c = |A U B|.
Non-weighted case (identical weight): A = {1}, B = {1}, c = 1;
Weight is the degree of truth: A = {1 -> 0.8}, B = {1 -> 0.5}, c = 0.8 or 1.0;
Weight is quantity: A = {1 -> 0.8}, B = {1 -> 0.5}, c = 1.3
I don't know if there's any difference for the algorithms in the list.
> Class and instance weighting for ML
> -----------------------------------
>
> Key: SPARK-9610
> URL: https://issues.apache.org/jira/browse/SPARK-9610
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Reporter: Joseph K. Bradley
>
> This umbrella is for tracking tasks for adding support for label or instance
> weights to ML algorithms. These additions will help handle skewed or
> imbalanced data, ensemble methods, etc.
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