[jira] [Assigned] (SPARK-8078) Spark MLlib Decision Trees Improvement
[ https://issues.apache.org/jira/browse/SPARK-8078?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-8078: --- Assignee: (was: Apache Spark) > Spark MLlib Decision Trees Improvement > -- > > Key: SPARK-8078 > URL: https://issues.apache.org/jira/browse/SPARK-8078 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 1.0.1, 1.0.2, 1.1.0, 1.1.1, 1.2.0, 1.2.1, 1.2.2, 1.3.0, > 1.3.1 > Environment: ubuntu14.04 >Reporter: yangqiao >Priority: Minor > Labels: performance > Fix For: 1.3.1 > > Original Estimate: 24h > Remaining Estimate: 24h > > In Spark MLlib, Decision Trees use Gini impurity, Entropy and Variance as > impurity. The Entropy impurity implement by calculating the Info Gain, which > is put forward by J. Ross Quinlan in ID3 algorithm. And it can be improved by > implementing C4.5 algorithm,which using Info Gain Ratio instead of Info Gain > to calculate impurity. By implementing C4.5 algorithm, the Decision Trees > model can achieve higher forecast accuracy. -- 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
[jira] [Assigned] (SPARK-8078) Spark MLlib Decision Trees Improvement
[ https://issues.apache.org/jira/browse/SPARK-8078?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-8078: --- Assignee: Apache Spark > Spark MLlib Decision Trees Improvement > -- > > Key: SPARK-8078 > URL: https://issues.apache.org/jira/browse/SPARK-8078 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 1.0.1, 1.0.2, 1.1.0, 1.1.1, 1.2.0, 1.2.1, 1.2.2, 1.3.0, > 1.3.1 > Environment: ubuntu14.04 >Reporter: yangqiao >Assignee: Apache Spark >Priority: Minor > Labels: performance > Fix For: 1.3.1 > > Original Estimate: 24h > Remaining Estimate: 24h > > In Spark MLlib, Decision Trees use Gini impurity, Entropy and Variance as > impurity. The Entropy impurity implement by calculating the Info Gain, which > is put forward by J. Ross Quinlan in ID3 algorithm. And it can be improved by > implementing C4.5 algorithm,which using Info Gain Ratio instead of Info Gain > to calculate impurity. By implementing C4.5 algorithm, the Decision Trees > model can achieve higher forecast accuracy. -- 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
