Package org.apache.spark.ml.feature

Interface QuantileDiscretizerBase

All Superinterfaces:
HasHandleInvalid, HasInputCol, HasInputCols, HasOutputCol, HasOutputCols, HasRelativeError, Identifiable, Params, Serializable, scala.Serializable
All Known Implementing Classes:
QuantileDiscretizer
public interface QuantileDiscretizerBase extends Params, HasHandleInvalid, HasInputCol, HasOutputCol, HasInputCols, HasOutputCols, HasRelativeError
Params for QuantileDiscretizer.

  • Method Details

    • getNumBuckets

      int getNumBuckets()

    • getNumBucketsArray

      int[] getNumBucketsArray()

    • handleInvalid

      Param<String> handleInvalid()
      Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple columns case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc. Default: "error"
      Specified by:
      handleInvalid in interface HasHandleInvalid
      Returns:
      (undocumented)

    • numBuckets

      IntParam numBuckets()
      Number of buckets (quantiles, or categories) into which data points are grouped. Must be greater than or equal to 2.

      See also handleInvalid(), which can optionally create an additional bucket for NaN values.

      default: 2

      Returns:
      (undocumented)

    • numBucketsArray

      IntArrayParam numBucketsArray()
      Array of number of buckets (quantiles, or categories) into which data points are grouped. Each value must be greater than or equal to 2

      See also handleInvalid(), which can optionally create an additional bucket for NaN values.

      Returns:
      (undocumented)