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Nickolay Yakushev edited comment on SPARK-9610 at 9/10/15 11:20 AM: -------------------------------------------------------------------- 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org