Umbrella for improvements to RFormula and to how R handles feature and label processing in general
RFormula output wrong features when formula w/o intercept
SparkR spark.naiveBayes throws error when label is numeric type
getModelFeatures of ml.api.r.SparkRWrapper cannot (always) reveal the original column names
Expose ColumnPruner as feature transformer
SparkR formula syntax to turn strings/factors into numerics
OneHotEncoder support drop first category alphabetically in the encoded vector
R MLlib algorithms should support input columns "features" and "label"
Should ML Models contains single models or Pipelines?