tuning

Type Members

1. case class BestEstimator[E <: Estimator[_]](name: String, estimator: E, summary: Seq[ModelEvaluation]) extends Product with Serializable

   

   Best Estimator container

   Best Estimator container

   E

   model type

   name

   the name of the best model

   estimator

   best estimator

   summary

   optional metadata

2. class DataBalancer extends Splitter with DataBalancerParams

   

   Instance that will split the data into train and holdout and then balance the dataset before modeling binary classifications

3. trait DataBalancerParams extends Params

   

4. case class DataBalancerSummary(positiveLabels: Long, negativeLabels: Long, desiredFraction: Double, upSamplingFraction: Double, downSamplingFraction: Double) extends SplitterSummary with Product with Serializable

   

   Summary for data balancer run for storage in metadata

   Summary for data balancer run for storage in metadata

   positiveLabels

   count of positive labels

   negativeLabels

   count of negative labels

   desiredFraction

   desired min fraction of smaller label count

   upSamplingFraction

   up/down sampling for smaller class of label

   downSamplingFraction

   down sampling for larger class of label

5. class DataCutter extends Splitter with DataCutterParams

   

   Instance that will make a holdout set and prepare the data for multiclass modeling Creates instance that will split data into training and test set filtering out any labels that don't meet the minimum fraction cutoff or fall in the top N labels specified.

6. case class DataCutterSummary(preSplitterDataCount: Long = 0L, downSamplingFraction: Double = ..., labelsKept: Seq[Double], labelsDropped: Seq[Double], labelsDroppedTotal: Long) extends SplitterSummary with Product with Serializable

   

   Summary of results for data cutter

   Summary of results for data cutter

   labelsKept

   labels retained

   labelsDropped

   labels dropped by data cutter

7. class DataSplitter extends Splitter with SplitterParams

   

   Instance that will split the data into training and holdout for regressions

8. trait DataSplitterParams extends Params

   

9. case class DataSplitterSummary(preSplitterDataCount: Long, downSamplingFraction: Double) extends SplitterSummary with Product with Serializable

   

   Summary for data splitter run for storage in metadata

   Summary for data splitter run for storage in metadata

   downSamplingFraction

   down sampling fraction for training set

10. case class PrevalidationVal(summaryOpt: Option[SplitterSummary], dataFrame: Option[DataFrame]) extends Product with Serializable

    

11. abstract class Splitter extends SplitterParams

    

    Abstract class that will carry on the creation of training set + test set

12. trait SplitterParams extends Params

    

13. trait SplitterSummary extends MetadataLike