package ml

Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

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Type Members

  1. abstract class Estimator[M <: Model[M]] extends PipelineStage

    :: DeveloperApi :: Abstract class for estimators that fit models to data.

  2. abstract class Model[M <: Model[M]] extends Transformer

    :: DeveloperApi :: A fitted model, i.

  3. class Pipeline extends Estimator[PipelineModel] with MLWritable

    :: Experimental :: A simple pipeline, which acts as an estimator.

  4. class PipelineModel extends Model[PipelineModel] with MLWritable with Logging

    :: Experimental :: Represents a fitted pipeline.

  5. abstract class PipelineStage extends Params with Logging

    :: DeveloperApi :: A stage in a pipeline, either an Estimator or a Transformer.

  6. abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]] extends Model[M] with PredictorParams

    :: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).

  7. abstract class Predictor[FeaturesType, Learner <: Predictor[FeaturesType, Learner, M], M <: PredictionModel[FeaturesType, M]] extends Estimator[M] with PredictorParams

    :: DeveloperApi :: Abstraction for prediction problems (regression and classification).

  8. abstract class Transformer extends PipelineStage

    :: DeveloperApi :: Abstract class for transformers that transform one dataset into another.

  9. abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] extends Transformer with HasInputCol with HasOutputCol with Logging

    :: DeveloperApi :: Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.

Value Members

  1. object Pipeline extends MLReadable[Pipeline] with Serializable

    @Since( "1.6.0" )
  2. object PipelineModel extends MLReadable[PipelineModel] with Serializable

    @Since( "1.6.0" )
  3. package attribute

    The ML pipeline API uses DataFrames as ML datasets.

  4. package classification

  5. package clustering

  6. package evaluation

  7. package feature

    The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.

  8. package param

  9. package recommendation

  10. package regression

  11. package source

  12. package tree

  13. package tuning

  14. package util

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