Github user mpjlu commented on a diff in the pull request: https://github.com/apache/spark/pull/14597#discussion_r74412725 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala --- @@ -197,3 +197,28 @@ class ChiSqSelector @Since("1.3.0") ( new ChiSqSelectorModel(indices) } } + +/** + * Creates a ChiSquared feature selector by False Positive Rate (FPR) test. + * @param alpha the highest p-value for features to be kept + */ +@Since("2.1.0") +class ChiSqSelectorByFpr @Since("2.1.0") ( + @Since("2.1.0") val alpha: Double) extends Serializable { + + /** + * Returns a ChiSquared feature selector by FPR. + * + * @param data an `RDD[LabeledPoint]` containing the labeled dataset with categorical features. + * Real-valued features will be treated as categorical for each distinct value. + * Apply feature discretizer before using this function. + */ + @Since("2.1.0") + def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = { + val indices = Statistics.chiSqTest(data) + .zipWithIndex.filter { case (res, _) => res.pValue < alpha } --- End diff -- Two or more models based on the same chi-squared test is reasonable, because the chi-squared test results contain "pValue, degreesOfFreedom, statistic,,,". numTopFeatures selection method uses statistic, and FPR selection method uses pValue. Expose the p-value to the caller maybe useful, but we can expose ChiSqTestResult, not only p-value.
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org