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Christos Iraklis Tsatsoulis edited comment on SPARK-11530 at 11/9/15 1:50 PM: ------------------------------------------------------------------------------ I edited it to target both; there are PCA.scala scripts for both ML & MLlib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib. was (Author: ctsats): I edited it to target both; there are PCA.scala scripts for both ML & MLLib, but since I am using it via PySpark, where it is available only via ML, I initially omitted MLlib > Return eigenvalues with PCA model > --------------------------------- > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib > Affects Versions: 1.5.1 > Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- 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