org.apache.spark.ml (Spark 3.5.4 JavaDoc)

Interface Summary
Interface	Description
MLEvent	Event emitted by ML operations.
MLEvents	A small trait that defines some methods to send `MLEvent`.
PredictorParams	(private[ml]) Trait for parameters for prediction (regression and classification).

Class Summary
Class	Description
Estimator<M extends Model<M>>	Abstract class for estimators that fit models to data.
FitEnd<M extends Model<M>>	Event fired after `Estimator.fit`.
FitStart<M extends Model<M>>	Event fired before `Estimator.fit`.
functions
LoadInstanceEnd<T>	Event fired after `MLReader.load`.
LoadInstanceStart<T>	Event fired before `MLReader.load`.
Model<M extends Model<M>>	A fitted model, i.e., a `Transformer` produced by an `Estimator`.
Pipeline	A simple pipeline, which acts as an estimator.
Pipeline.SharedReadWrite$	Methods for `MLReader` and `MLWriter` shared between `Pipeline` and `PipelineModel`
PipelineModel	Represents a fitted pipeline.
PipelineStage	A stage in a pipeline, either an `Estimator` or a `Transformer`.
PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>>	Abstraction for a model for prediction tasks (regression and classification).
Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>>	Abstraction for prediction problems (regression and classification).
SaveInstanceEnd	Event fired after `MLWriter.save`.
SaveInstanceStart	Event fired before `MLWriter.save`.
TransformEnd	Event fired after `Transformer.transform`.
Transformer	Abstract class for transformers that transform one dataset into another.
TransformStart	Event fired before `Transformer.transform`.
UnaryTransformer<IN,OUT,T extends UnaryTransformer<IN,OUT,T>>	Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.

Package org.apache.spark.ml Description

DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.