odkl

Type Members

class AutoAssembler extends Estimator[PipelineModel] with AutoAssemblerParams with DefaultParamsWritable

Utility for automatically assembling columns into a vector of features.
trait AutoAssemblerParams extends HasColumnsSets with HasOutputCol with HasColumnAttributeMap

Params for automatic feature-vector assembler.
class BinaryClassificationEvaluator extends Evaluator[BinaryClassificationEvaluator]

Simple evaluator based on the mllib.
class CRRSamplerEstimator extends Estimator[CRRSamplerModel] with DefaultParamsWritable with CRRSamplerParams

Estimator is used to select the proper item sample rate to achive desired size of the resulting sample.
class CRRSamplerModel extends Model[CRRSamplerModel] with DefaultParamsWritable with CRRSamplerParams with HasNetlibBlas

Model applied as a transformer, but the resulting data set is not determenistic (each pass produces different results).
trait CRRSamplerParams extends HasInputCol with HasGroupByColumns with HasLabelCol

Follows ideas from Combined Regression and Ranking paper (http://www.
class ColumnsExtractor extends Transformer with DefaultParamsWritable

Used to extract a set of columns from the underlying data frame based on names and/or SQL expresions.
class CombinedLinearModelUnwrappedFeatureSelector[M <: LinearModel[M], C <: CombinedModel[M, C]] extends GenericFeatureSelector[CombinedLinearModelUnwrappedFeatureSelector[M, C]] with ModelTransformer[C, CombinedLinearModelUnwrappedFeatureSelector[M, C]]
abstract class CombinedModel[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends Model[C] with ModelWithSummary[C] with HasDescriminantColumn with HasDirectTransformOption with HasPredictionCol with ForkedModelParams

Base class for combined model holding a named map of nested models.
class CrossValidator[M <: ModelWithSummary[M]] extends ForkedEstimatorSameType[M, Int] with HasIsTestCol with HasFolds

Used to train and evaluate model in folds.
abstract class DSVRGD[M <: ModelWithSummary[M]] extends Estimator[M] with SummarizableEstimator[M] with HasPredictionCol with HasFeaturesCol with HasLabelCol with HasRegParam with HasElasticNetParam with HasNetlibBlas with HasMaxIter with HasTol with HasCacheTrainData

Created by dmitriybugaichenko on 10.
abstract class DeVectorizedDSVRGD[M <: ModelWithSummary[M]] extends DSVRGD[M]

Helper class for training single-label models.
abstract class Evaluator[S <: Evaluator[S]] extends Transformer with HasLabelCol with HasPredictionCol

Base class for evaluators.
class ExtendedMultivariateOnlineSummarizer extends MultivariateOnlineSummarizer with Serializable with Logging

Created by dmitriybugaichenko on 30.
abstract class FoldedFeatureSelector[SelectingModel <: ModelWithSummary[SelectingModel] with HasWeights, Filter <: GenericFeatureSelector[Filter]] extends Estimator[Filter] with SummarizableEstimator[Filter] with HasWeights with HasFeaturesCol with HasFeaturesSignificance

Created by dmitriybugaichenko on 29.
abstract class ForkedEstimator[ModelIn <: ModelWithSummary[ModelIn], ForeKeyType, ModelOut <: ModelWithSummary[ModelOut]] extends Estimator[ModelOut] with SummarizableEstimator[ModelOut] with ForkedModelParams

Utility used to split training into forks (per type, per class, per fold).
abstract class ForkedEstimatorSameType[ModelIn <: ModelWithSummary[ModelIn], ForeKeyType] extends ForkedEstimator[ModelIn, ForeKeyType, ModelIn]

Specific case of forked estimator which does not change the type of the underlying model.
trait ForkedModelParams extends AnyRef
abstract class GenericFeatureSelector[M <: ModelWithSummary[M]] extends Model[M] with ModelWithSummary[M] with HasFeaturesCol
trait HasBatchSize extends Params

Used for evaluators with batch support
trait HasCacheTrainData extends AnyRef

For estimators capable of caching training data.
trait HasClassesCol extends Params

Adds parameter with column for instance classes.
trait HasClassesWeights extends Params

Adds parameter wot classes weights (defaults to 1.
trait HasColumnAttributeMap extends AnyRef

For vector assemblers used to provide better naming for metadata attrbiutes.
trait HasColumnsSets extends Params

Parameters for specifying which columns to include or exclude.
trait HasDescriminantColumn extends Params

Created by dmitriybugaichenko on 30.
trait HasDirectTransformOption extends Transformer

Supplementary train used for optimization (moving transformation out of the execution plan into UDF)
trait HasFeaturesSignificance extends AnyRef

Block with information regarding features significance stat, produced during the features selection stage.
trait HasFolds extends Params

Adds parameters for folding - number of folds and name of column with fold number.
trait HasGroupByColumns extends AnyRef

For transformers performing grouping by a certain columns.
trait HasIsTestCol extends Params

Adds parameter with the name of test/train split column
trait HasMetricsBlock extends AnyRef

Metrics block is added by the evaluators.
trait HasNetlibBlas extends AnyRef

Created by dmitriybugaichenko on 19.
trait HasRegularizeLast extends Params

Used to indicate that last weight should not be considered as a part of regularization (typically if it is the intercept)
trait HasSortByColumns extends AnyRef

For transformers performing sorting by a certain columns.
trait HasTypeCol extends Params

Adds parameter with column for instance type.
trait HasWeights extends AnyRef

Block produced by a models with concept of feature weights (eg.
class Interceptor extends Transformer with HasFeaturesCol with DefaultParamsWritable

Adds extra column to features vector with a fixed value of 1.
class IsotonicRegression extends regression.IsotonicRegression

:: Experimental :: Isotonic regression.
class JacksonParam[T] extends Param[T] with Logging

ml.
class LinearCombinationModel[N <: ModelWithSummary[N]] extends MultiClassCombinationModelBase[N, LinearCombinationModel[N]]

Combination model which evaluates ALL nested model and combines results based on linear weights.
class LinearDSVRGD extends DeVectorizedDSVRGD[LinearRegressionModel]

Single-label linear regresion with DSVRGD
abstract class LinearEstimator[M <: LinearModel[M], T <: LinearEstimator[M, T]] extends Predictor[Vector, T, M] with SummarizableEstimator[M] with LinearModelParams with HasWeights
class LinearMatrixDSVRGD extends DSVRGD[LinearCombinationModel[LinearRegressionModel]]

Multi-label linear regresion with DSVRGD
abstract class LinearModel[M <: LinearModel[M]] extends PredictionModel[Vector, M] with DirectPredictionModel[Vector, M] with ModelWithSummary[M] with LinearModelParams with HasWeights
trait LinearModelParams extends PredictorParams
class LinearModelUnwrappedFeatureSelector[M <: LinearModel[M]] extends GenericFeatureSelector[LinearModelUnwrappedFeatureSelector[M]] with ModelTransformer[M, LinearModelUnwrappedFeatureSelector[M]]
class LinearRegressionModel extends LinearModel[LinearRegressionModel]
class LinearRegressionSGD extends LinearRegressor[LinearRegressionModel, GradientDescent, LinearRegressionSGD] with HasRegParam with HasTol with HasMaxIter with HasStepSize
abstract class LinearRegressor[M <: LinearModel[M], O <: Optimizer, T <: LinearRegressor[M, O, T]] extends LinearEstimator[M, T] with DefaultParamsWritable with Logging with HasCacheTrainData
class LogisticDSVRGD extends DeVectorizedDSVRGD[LogisticRegressionModel]

Multi-label logistic regresion with DSVRGD
class LogisticMatrixDSVRGD extends DSVRGD[LinearCombinationModel[LogisticRegressionModel]]

Multi-label logistic regresion with DSVRGD
class LogisticRegressionLBFSG extends LinearRegressor[LogisticRegressionModel, LogisticRegressionLBFSG, LogisticRegressionLBFSG] with HasRegParam with HasTol with HasMaxIter with Optimizer with HasElasticNetParam with HasRegularizeLast with HasBatchSize
class LogisticRegressionModel extends LinearModel[LogisticRegressionModel]
class MLWrapper[M <: Model[M]] extends Estimator[MLWrapperModel[M]] with SummarizableEstimator[MLWrapperModel[M]]

Utility used to bridge default spark ML models into our advanced pipelines.
class MLWrapperModel[M <: Model[M]] extends Model[MLWrapperModel[M]] with ModelWithSummary[MLWrapperModel[M]]
class MatrixLBFGS extends Estimator[LinearCombinationModel[LogisticRegressionModel]] with SummarizableEstimator[LinearCombinationModel[LogisticRegressionModel]] with PredictorParams with HasTol with HasMaxIter with HasRegParam with HasRegularizeLast with HasBatchSize with HasNetlibBlas

Created by dmitriybugaichenko on 24.
class MetadataParam extends Param[Metadata]

Created by alexander.
class ModelSummary extends Serializable

One of main extensions to the base concept of model - each model might return a summary represented by a named collection of dataframes.
trait ModelTransformer[M <: ModelWithSummary[M], T <: ModelTransformer[M, T]] extends Model[T] with Logging

In case if we can avoid certain stages used during training while predicting we need to propagate some changes to the model (eg.
trait ModelWithSummary[M <: ModelWithSummary[M]] extends Model[M] with MLWritable

Model which has a summary.
class MultiClassCombinationModel[N <: ModelWithSummary[N]] extends MultiClassCombinationModelBase[N, MultiClassCombinationModel[N]]

Combination model which evaluates ALL nested model and returns vector.
abstract class MultiClassCombinationModelBase[N <: ModelWithSummary[N], M <: MultiClassCombinationModelBase[N, M]] extends CombinedModel[N, M] with HasClassesCol with HasPredictionCol with HasFeaturesCol

Base class for models, evaluated per each class.
class MultinominalExtractor extends Estimator[MultinominalExtractorModel] with MultinominalExtractorParams with DefaultParamsWritable

Utility for converting columns with string or a set of stings into a vector of 0/1 with the cardinality equal to the number of unique string values used.
class MultinominalExtractorModel extends Model[MultinominalExtractorModel] with MultinominalExtractorParams with DefaultParamsWritable

Model produced by the multinominal extractor.
trait MultinominalExtractorParams extends HasInputCol with HasOutputCol

Parameters for multinominal feature extractor.
class NaNToMeanReplacerEstimator extends Estimator[NaNToMeanReplacerModel] with NaNToMeanReplacerParams

Estimates mean values ignoring NaN's
class NaNToMeanReplacerModel extends Model[NaNToMeanReplacerModel] with NaNToMeanReplacerParams with DefaultParamsWritable

Model used to replace values with pre-computed defaults before training/predicting.
trait NaNToMeanReplacerParams extends HasInputCol with HasOutputCol

Set of parameters for the replacer
class NameAssigner extends Transformer with HasInputCols

Assuming there is a metadata attached to a integer field can be used to replace ints with corresponding attribute names.
class NullToDefaultReplacer extends Transformer with HasColumnsSets with DefaultParamsWritable

Utility used to replace null values with defaults (zero or false).
class NullToNaNVectorAssembler extends Transformer with HasInputCols with HasOutputCol with HasColumnAttributeMap with DefaultParamsWritable

:: Experimental :: A feature transformer that merges multiple columns into a vector column.
class PartitionedRankingEvaluator extends Evaluator[PartitionedRankingEvaluator] with HasOutputCol with HasGroupByColumns

Evaluator used to compute metrics for predictions grouped by a certain criteria (typically by a user id).
trait PartitioningParams extends Params with HasSortByColumns

Settings for partitioning, except the number of partitions.
class PipelinedFeatureSelector extends GenericFeatureSelector[PipelinedFeatureSelector]
class PipelinedFoldedFeatureSelector[SelectingModel <: ModelWithSummary[SelectingModel] with HasWeights] extends FoldedFeatureSelector[SelectingModel, PipelinedFeatureSelector]
class ScalerEstimator[M <: ModelWithSummary[M]] extends Estimator[Unscaler[M]] with ScalerParams with DefaultParamsWritable

This is a specific implementation of the scaler for linear models.
trait ScalerParams extends Params with HasFeaturesCol

Scaler parameters.
class SelectingModel[N <: ModelWithSummary[N]] extends CombinedModel[N, SelectingModel[N]] with HasTypeCol with HasFeaturesCol

Selecting model applies exactly one model based on instance type and return its result.
class SeriallizableAvlTreeDigest extends Serializer[SeriallizableAvlTreeDigest] with Serializable

Serializable wrapper over the TDigest
class SqlFilter extends Transformer with DefaultParamsWritable

Simple utility used to apply SQL WHERE filter
trait SummarizableEstimator[M <: ModelWithSummary[M]] extends Estimator[M]

Estimator with produces model with summary.
class TopKTransformer[B] extends Transformer with DefaultParamsWritable with HasGroupByColumns

Created by eugeny.
class TopKUDAF[B] extends UserDefinedAggregateFunction with Logging

Created by eugeny.
class UnwrappedStage[M <: ModelWithSummary[M], T <: ModelTransformer[M, T]] extends Estimator[M] with SummarizableEstimator[M]

In case if we can avoid certain stages used during training while predicting we need to propagate some changes to the model (eg.
class VectorExplode extends Transformer with DefaultParamsWritable

Utility used to extract nested values from vectors into dedicated columns.
class VectorStatCollector extends Transformer with HasInputCol with HasGroupByColumns with DefaultParamsWritable

Utility used to collect detailed stat for vectors grouped by a certain keys.
case class WeightedFeature(index: Int, name: String, weight: Double) extends Product with Serializable

Utility used for reporting single indexed feature weight.
case class WeightsStat(stats: Array[WeightsStatRecord]) extends Product with Serializable
case class WeightsStatRecord(index: Int, name: String, descriminant: String, average: Double, significance: Double, isRelevant: Boolean) extends Product with Serializable

Value Members

object AutoAssembler extends DefaultParamsReadable[AutoAssembler] with Serializable

Adds read logic
object ColumnsExtractor extends DefaultParamsReadable[ColumnsExtractor] with Serializable

Adds read ability.
object CombinedModel extends MLReadable[PipelineStage] with Serializable
object CrossValidator extends DefaultParamsReadable[CrossValidator[_]] with Serializable
object DSVRGD extends Serializable with HasNetlibBlas
object Evaluator extends Serializable
object FoldedFeatureSelector extends Serializable with Logging
object ForkedEstimator extends Serializable

Helper used to inject common task support with thread count limit into all forked estimators.
object HasNetlibBlas extends Serializable
object Interceptor extends DefaultParamsReadable[Interceptor] with Serializable
object IsotonicRegression extends DefaultParamsReadable[IsotonicRegression] with Serializable

Annotations
@Since( "1.6.0" )
object JacksonParam extends Serializable
object LinearDSVRGD extends DefaultParamsReadable[LinearDSVRGD] with Serializable
object LinearMatrixDSVRGD extends DefaultParamsReadable[LinearMatrixDSVRGD] with Serializable
object LinearRegressionModel extends MLReadable[LinearRegressionModel] with Serializable
object LogisticDSVRGD extends DefaultParamsReadable[LogisticDSVRGD] with Serializable
object LogisticMatrixDSVRGD extends DefaultParamsReadable[LogisticMatrixDSVRGD] with Serializable
object LogisticRegressionModel extends MLReadable[LogisticRegressionModel] with Serializable
object MLWrapperModel extends MLReadable[PipelineStage] with Serializable
object MatrixLBFGS extends Logging with HasNetlibBlas with Serializable
object ModelWithSummary extends MLReadable[PipelineStage] with Serializable

Helper for reading and writing models in a typed way.
object MultinominalExtractor extends DefaultParamsReadable[MultinominalExtractor] with Serializable

Adds read logic
object MultinominalExtractorModel extends DefaultParamsReadable[MultinominalExtractorModel] with Serializable

Adds read ability
object NaNToMeanReplacerModel extends DefaultParamsReadable[NaNToMeanReplacerModel] with Serializable

Adds support for reading.
object NullToDefaultReplacer extends DefaultParamsReadable[NullToDefaultReplacer] with Serializable

Adds read ability.
object NullToNaNVectorAssembler extends DefaultParamsReadable[NullToNaNVectorAssembler] with Serializable

Annotations
@Since( "1.6.0" )
object PartitionedRankingEvaluator extends Serializable
object Scaler extends Serializable
object SqlFilter extends DefaultParamsReadable[SqlFilter] with Serializable
object UnwrappedStage extends Serializable
package texts

package odkl

Type Members

class AutoAssembler extends Estimator[PipelineModel] with AutoAssemblerParams with DefaultParamsWritable

trait AutoAssemblerParams extends HasColumnsSets with HasOutputCol with HasColumnAttributeMap

class BinaryClassificationEvaluator extends Evaluator[BinaryClassificationEvaluator]

class CRRSamplerEstimator extends Estimator[CRRSamplerModel] with DefaultParamsWritable with CRRSamplerParams

class CRRSamplerModel extends Model[CRRSamplerModel] with DefaultParamsWritable with CRRSamplerParams with HasNetlibBlas

trait CRRSamplerParams extends HasInputCol with HasGroupByColumns with HasLabelCol

class ColumnsExtractor extends Transformer with DefaultParamsWritable

class CombinedLinearModelUnwrappedFeatureSelector[M <: LinearModel[M], C <: CombinedModel[M, C]] extends GenericFeatureSelector[CombinedLinearModelUnwrappedFeatureSelector[M, C]] with ModelTransformer[C, CombinedLinearModelUnwrappedFeatureSelector[M, C]]

abstract class CombinedModel[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends Model[C] with ModelWithSummary[C] with HasDescriminantColumn with HasDirectTransformOption with HasPredictionCol with ForkedModelParams

class CrossValidator[M <: ModelWithSummary[M]] extends ForkedEstimatorSameType[M, Int] with HasIsTestCol with HasFolds

abstract class DSVRGD[M <: ModelWithSummary[M]] extends Estimator[M] with SummarizableEstimator[M] with HasPredictionCol with HasFeaturesCol with HasLabelCol with HasRegParam with HasElasticNetParam with HasNetlibBlas with HasMaxIter with HasTol with HasCacheTrainData

abstract class DeVectorizedDSVRGD[M <: ModelWithSummary[M]] extends DSVRGD[M]

abstract class Evaluator[S <: Evaluator[S]] extends Transformer with HasLabelCol with HasPredictionCol

class ExtendedMultivariateOnlineSummarizer extends MultivariateOnlineSummarizer with Serializable with Logging

abstract class FoldedFeatureSelector[SelectingModel <: ModelWithSummary[SelectingModel] with HasWeights, Filter <: GenericFeatureSelector[Filter]] extends Estimator[Filter] with SummarizableEstimator[Filter] with HasWeights with HasFeaturesCol with HasFeaturesSignificance

abstract class ForkedEstimator[ModelIn <: ModelWithSummary[ModelIn], ForeKeyType, ModelOut <: ModelWithSummary[ModelOut]] extends Estimator[ModelOut] with SummarizableEstimator[ModelOut] with ForkedModelParams

abstract class ForkedEstimatorSameType[ModelIn <: ModelWithSummary[ModelIn], ForeKeyType] extends ForkedEstimator[ModelIn, ForeKeyType, ModelIn]

trait ForkedModelParams extends AnyRef

abstract class GenericFeatureSelector[M <: ModelWithSummary[M]] extends Model[M] with ModelWithSummary[M] with HasFeaturesCol

trait HasBatchSize extends Params

trait HasCacheTrainData extends AnyRef

trait HasClassesCol extends Params

trait HasClassesWeights extends Params

trait HasColumnAttributeMap extends AnyRef

trait HasColumnsSets extends Params

trait HasDescriminantColumn extends Params

trait HasDirectTransformOption extends Transformer

trait HasFeaturesSignificance extends AnyRef

trait HasFolds extends Params

trait HasGroupByColumns extends AnyRef

trait HasIsTestCol extends Params

trait HasMetricsBlock extends AnyRef

trait HasNetlibBlas extends AnyRef

trait HasRegularizeLast extends Params

trait HasSortByColumns extends AnyRef

trait HasTypeCol extends Params

trait HasWeights extends AnyRef

class Interceptor extends Transformer with HasFeaturesCol with DefaultParamsWritable

class IsotonicRegression extends regression.IsotonicRegression

class JacksonParam[T] extends Param[T] with Logging

class LinearCombinationModel[N <: ModelWithSummary[N]] extends MultiClassCombinationModelBase[N, LinearCombinationModel[N]]

class LinearDSVRGD extends DeVectorizedDSVRGD[LinearRegressionModel]

abstract class LinearEstimator[M <: LinearModel[M], T <: LinearEstimator[M, T]] extends Predictor[Vector, T, M] with SummarizableEstimator[M] with LinearModelParams with HasWeights

class LinearMatrixDSVRGD extends DSVRGD[LinearCombinationModel[LinearRegressionModel]]

abstract class LinearModel[M <: LinearModel[M]] extends PredictionModel[Vector, M] with DirectPredictionModel[Vector, M] with ModelWithSummary[M] with LinearModelParams with HasWeights

trait LinearModelParams extends PredictorParams

class LinearModelUnwrappedFeatureSelector[M <: LinearModel[M]] extends GenericFeatureSelector[LinearModelUnwrappedFeatureSelector[M]] with ModelTransformer[M, LinearModelUnwrappedFeatureSelector[M]]

class LinearRegressionModel extends LinearModel[LinearRegressionModel]

class LinearRegressionSGD extends LinearRegressor[LinearRegressionModel, GradientDescent, LinearRegressionSGD] with HasRegParam with HasTol with HasMaxIter with HasStepSize

abstract class LinearRegressor[M <: LinearModel[M], O <: Optimizer, T <: LinearRegressor[M, O, T]] extends LinearEstimator[M, T] with DefaultParamsWritable with Logging with HasCacheTrainData

class LogisticDSVRGD extends DeVectorizedDSVRGD[LogisticRegressionModel]

class LogisticMatrixDSVRGD extends DSVRGD[LinearCombinationModel[LogisticRegressionModel]]

class LogisticRegressionLBFSG extends LinearRegressor[LogisticRegressionModel, LogisticRegressionLBFSG, LogisticRegressionLBFSG] with HasRegParam with HasTol with HasMaxIter with Optimizer with HasElasticNetParam with HasRegularizeLast with HasBatchSize

class LogisticRegressionModel extends LinearModel[LogisticRegressionModel]

class MLWrapper[M <: Model[M]] extends Estimator[MLWrapperModel[M]] with SummarizableEstimator[MLWrapperModel[M]]

class MLWrapperModel[M <: Model[M]] extends Model[MLWrapperModel[M]] with ModelWithSummary[MLWrapperModel[M]]

class MatrixLBFGS extends Estimator[LinearCombinationModel[LogisticRegressionModel]] with SummarizableEstimator[LinearCombinationModel[LogisticRegressionModel]] with PredictorParams with HasTol with HasMaxIter with HasRegParam with HasRegularizeLast with HasBatchSize with HasNetlibBlas

class MetadataParam extends Param[Metadata]

class ModelSummary extends Serializable

trait ModelTransformer[M <: ModelWithSummary[M], T <: ModelTransformer[M, T]] extends Model[T] with Logging

trait ModelWithSummary[M <: ModelWithSummary[M]] extends Model[M] with MLWritable

class MultiClassCombinationModel[N <: ModelWithSummary[N]] extends MultiClassCombinationModelBase[N, MultiClassCombinationModel[N]]

abstract class MultiClassCombinationModelBase[N <: ModelWithSummary[N], M <: MultiClassCombinationModelBase[N, M]] extends CombinedModel[N, M] with HasClassesCol with HasPredictionCol with HasFeaturesCol

class MultinominalExtractor extends Estimator[MultinominalExtractorModel] with MultinominalExtractorParams with DefaultParamsWritable

class MultinominalExtractorModel extends Model[MultinominalExtractorModel] with MultinominalExtractorParams with DefaultParamsWritable

trait MultinominalExtractorParams extends HasInputCol with HasOutputCol

class NaNToMeanReplacerEstimator extends Estimator[NaNToMeanReplacerModel] with NaNToMeanReplacerParams

class NaNToMeanReplacerModel extends Model[NaNToMeanReplacerModel] with NaNToMeanReplacerParams with DefaultParamsWritable

trait NaNToMeanReplacerParams extends HasInputCol with HasOutputCol

class NameAssigner extends Transformer with HasInputCols

class NullToDefaultReplacer extends Transformer with HasColumnsSets with DefaultParamsWritable

class NullToNaNVectorAssembler extends Transformer with HasInputCols with HasOutputCol with HasColumnAttributeMap with DefaultParamsWritable

class PartitionedRankingEvaluator extends Evaluator[PartitionedRankingEvaluator] with HasOutputCol with HasGroupByColumns

trait PartitioningParams extends Params with HasSortByColumns

class PipelinedFeatureSelector extends GenericFeatureSelector[PipelinedFeatureSelector]

class PipelinedFoldedFeatureSelector[SelectingModel <: ModelWithSummary[SelectingModel] with HasWeights] extends FoldedFeatureSelector[SelectingModel, PipelinedFeatureSelector]

class ScalerEstimator[M <: ModelWithSummary[M]] extends Estimator[Unscaler[M]] with ScalerParams with DefaultParamsWritable