org.apache.spark.ml.classification
Class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>

Object
  org.apache.spark.ml.PipelineStage
      org.apache.spark.ml.Transformer
          org.apache.spark.ml.Model<M>
              org.apache.spark.ml.PredictionModel<FeaturesType,M>
                  org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>

All Implemented Interfaces:

java.io.Serializable, Logging, Params

Direct Known Subclasses:

LogisticRegressionModel

public abstract class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>
extends PredictionModel<FeaturesType,M>

:: DeveloperApi ::

Model produced by a Classifier. Classes are indexed {0, 1, ..., numClasses - 1}.

See Also:

Serialized Form

Constructor Summary

ClassificationModel()

Method Summary

abstract int	numClasses() Number of classes (values which the label can take).
M	setRawPredictionCol(String value)
DataFrame	transform(DataFrame dataset) Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

Methods inherited from class org.apache.spark.ml.PredictionModel

setFeaturesCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Model

copy, hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

ClassificationModel

public ClassificationModel()

Method Detail

setRawPredictionCol

public M setRawPredictionCol(String value)

numClasses

public abstract int numClasses()

Number of classes (values which the label can take).

transform

public DataFrame transform(DataFrame dataset)

Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector.

Overrides:

transform in class PredictionModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>

Parameters:

dataset - input dataset

Returns:

transformed dataset

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema