org.apache.spark.mllib.classification
Class SVMModel

Object
  org.apache.spark.mllib.regression.GeneralizedLinearModel
      org.apache.spark.mllib.classification.SVMModel

All Implemented Interfaces:

java.io.Serializable, ClassificationModel, PMMLExportable, Saveable

public class SVMModel
extends GeneralizedLinearModel
implements ClassificationModel, scala.Serializable, Saveable, PMMLExportable

Model for Support Vector Machines (SVMs).

param: weights Weights computed for every feature. param: intercept Intercept computed for this model.

See Also:

Serialized Form

Constructor Summary

SVMModel(Vector weights, double intercept)

Method Summary

SVMModel	clearThreshold() :: Experimental :: Clears the threshold so that predict will output raw prediction scores.
scala.Option<Object>	getThreshold() :: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
double	intercept()
static SVMModel	load(SparkContext sc, String path)
void	save(SparkContext sc, String path) Save this model to the given path.
SVMModel	setThreshold(double threshold) :: Experimental :: Sets the threshold that separates positive predictions from negative predictions.
String	toString() Print a summary of the model.
Vector	weights()

Methods inherited from class org.apache.spark.mllib.regression.GeneralizedLinearModel

predict, predict

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.mllib.classification.ClassificationModel

predict, predict, predict

Methods inherited from interface org.apache.spark.mllib.pmml.PMMLExportable

toPMML, toPMML, toPMML, toPMML, toPMML

Constructor Detail

SVMModel

public SVMModel(Vector weights,
                double intercept)

Method Detail

load

public static SVMModel load(SparkContext sc,
                            String path)

weights

public Vector weights()

Overrides:

weights in class GeneralizedLinearModel

intercept

public double intercept()

Overrides:

intercept in class GeneralizedLinearModel

setThreshold

public SVMModel setThreshold(double threshold)

:: Experimental :: Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. The default value is 0.0.

Parameters:

threshold - (undocumented)

Returns:

(undocumented)

getThreshold

public scala.Option<Object> getThreshold()

:: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.

Returns:

(undocumented)

clearThreshold

public SVMModel clearThreshold()

:: Experimental :: Clears the threshold so that predict will output raw prediction scores.

Returns:

(undocumented)

save

public void save(SparkContext sc,
                 String path)

Description copied from interface: Saveable

Save this model to the given path.

This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/

The model may be loaded using Loader.load.

Specified by:

save in interface Saveable

Parameters:

sc - Spark context used to save model data.

path - Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

toString

public String toString()

Description copied from class: GeneralizedLinearModel

Print a summary of the model.

Overrides:

toString in class GeneralizedLinearModel

Returns:

(undocumented)