Type Parameters:

FeaturesType - Type of input features. E.g., Vector

M - Concrete Model type

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, ClassifierParams, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasRawPredictionCol, PredictorParams, Identifiable

Direct Known Subclasses:

LinearSVCModel, ProbabilisticClassificationModel

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

See Also:

• Serialized Form

• Nested Class Summary

  Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

  org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

• Constructor Summary

  Constructors

• Method Summary

  abstract int

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

  double

  Predict label for the given features.

  Raw prediction for each possible label.

  Param for raw prediction (a.k.a.

  Check transform validity and derive the output schema from the input schema.

  Methods inherited from interface org.apache.spark.internal.Logging

  initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

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

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

• Constructor Details

  • ClassificationModel

    public ClassificationModel()

• Method Details

  • numClasses

    public abstract int numClasses()

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

  • predict

    Predict label for the given features. This method is used to implement transform() and output PredictionModel.predictionCol().

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Specified by:

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

    Parameters:

    features - (undocumented)

    Returns:

    (undocumented)

  • predictRaw

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol().

    Parameters:

    features - (undocumented)

    Returns:

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

  • rawPredictionCol

    public final Param<String> rawPredictionCol()

    Param for raw prediction (a.k.a. confidence) column name.

    Specified by:

    rawPredictionCol in interface HasRawPredictionCol

    Returns:

    (undocumented)

  • setRawPredictionCol

    public M setRawPredictionCol(String value)

  • transform

    Overrides:

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

    Parameters:

    dataset - input dataset

    Returns:

    transformed dataset

  • transformImpl

  • transformSchema

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Overrides:

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

    Parameters:

    schema - (undocumented)

    Returns:

    (undocumented)