[SPARK-17017] Add a chiSquare Selector based on False Positive Rate (FPR) test

• Type: New Feature
• Status: Resolved
• Priority: Minor
• Resolution: Fixed
• Fix Version/s: 2.1.0
• Assignee: Peng Meng
• Reporter: Peng Meng
• Created: 11/Aug/16 07:19
• Updated: 25/Oct/16 18:52
• Resolved: 21/Sep/16 09:17

Description

Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. Is it necessary to add a chiSquare Selector based on False Positive Rate (FPR) test, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection

Issue Links

• is duplicated by: SPARK-16843 Select features according to a percentile of the highest scores of ChiSqSelector
• is related to: SPARK-17870 ML/MLLIB: ChiSquareSelector based on Statistics.chiSqTest(RDD) is wrong
• relates to: SPARK-18088 ChiSqSelector FPR PR cleanups
• links to: [Github] Pull Request #14597 (mpjlu)
• links to: [Github] Pull Request #15214 (yanboliang)
• links to: [Github] Pull Request #15236 (lins05)