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Tobi Bosede commented on SPARK-17001: ------------------------------------- This can be implemented in a similar fashion to scikit learn's maxabs_scale. See http://scikit-learn.org/stable/modules/preprocessing.html#scaling-sparse-data for more info. > Enable standardScaler to standardize sparse vectors when withMean=True > ---------------------------------------------------------------------- > > Key: SPARK-17001 > URL: https://issues.apache.org/jira/browse/SPARK-17001 > Project: Spark > Issue Type: Improvement > Affects Versions: 2.0.0 > Reporter: Tobi Bosede > Priority: Minor > > When withMean = true, StandardScaler will not handle sparse vectors, and > instead throw an exception. This is presumably because subtracting the mean > makes a sparse vector dense, and this can be undesirable. > However, VectorAssembler generates vectors that may be a mix of sparse and > dense, even when vectors are smallish, depending on their values. It's common > to feed this into StandardScaler, but it would fail sometimes depending on > the input if withMean = true. This is kind of surprising. > StandardScaler should go ahead and operate on sparse vectors and subtract the > mean, if explicitly asked to do so with withMean, on the theory that the user > knows what he/she is doing, and there is otherwise no way to make this work. -- 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