class SVMModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable
Model for Support Vector Machines (SVMs).
- Annotations
- @Since("0.8.0")
- Source
- SVM.scala
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- SVMModel
- PMMLExportable
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- ClassificationModel
- GeneralizedLinearModel
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- def clearThreshold(): SVMModel.this.type
Clears the threshold so that
predictwill output raw prediction scores.Clears the threshold so that
predictwill output raw prediction scores.- Annotations
- @Since("1.0.0")
- def clone(): AnyRef
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- def getThreshold: Option[Double]
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
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- @Since("1.3.0")
- def hashCode(): Int
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- val intercept: Double
- Definition Classes
- SVMModel → GeneralizedLinearModel
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- @Since("0.8.0")
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- def predict(testData: JavaRDD[Vector]): JavaRDD[Double]
Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
- testData
JavaRDD representing data points to be predicted
- returns
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
- Definition Classes
- ClassificationModel
- Annotations
- @Since("1.0.0")
- def predict(testData: Vector): Double
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
- testData
array representing a single data point
- returns
Double prediction from the trained model
- Definition Classes
- GeneralizedLinearModel
- Annotations
- @Since("1.0.0")
- def predict(testData: RDD[Vector]): RDD[Double]
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
- testData
RDD representing data points to be predicted
- returns
RDD[Double] where each entry contains the corresponding prediction
- Definition Classes
- GeneralizedLinearModel
- Annotations
- @Since("1.0.0")
- def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double
Predict the result given a data point and the weights learned.
Predict the result given a data point and the weights learned.
- dataMatrix
Row vector containing the features for this data point
- weightMatrix
Column vector containing the weights of the model
- intercept
Intercept of the model.
- Attributes
- protected
- Definition Classes
- SVMModel → GeneralizedLinearModel
- def save(sc: SparkContext, path: String): Unit
Save this model to the given path.
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.- 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.
- def setThreshold(threshold: Double): SVMModel.this.type
Sets the threshold that separates positive predictions from negative predictions.
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 a positive, and negative otherwise. The default value is 0.0.
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- @Since("1.0.0")
- final def synchronized[T0](arg0: => T0): T0
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- def toPMML(): String
Export the model to a String in PMML format
Export the model to a String in PMML format
- Definition Classes
- PMMLExportable
- Annotations
- @Since("1.4.0")
- def toPMML(outputStream: OutputStream): Unit
Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
- Definition Classes
- PMMLExportable
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- @Since("1.4.0")
- def toPMML(sc: SparkContext, path: String): Unit
Export the model to a directory on a distributed file system in PMML format
Export the model to a directory on a distributed file system in PMML format
- Definition Classes
- PMMLExportable
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- @Since("1.4.0")
- def toPMML(localPath: String): Unit
Export the model to a local file in PMML format
Export the model to a local file in PMML format
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- PMMLExportable
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- @Since("1.4.0")
- def toString(): String
Print a summary of the model.
Print a summary of the model.
- Definition Classes
- SVMModel → GeneralizedLinearModel → AnyRef → Any
- final def wait(arg0: Long, arg1: Int): Unit
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- val weights: Vector
- Definition Classes
- SVMModel → GeneralizedLinearModel
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- @Since("1.0.0")
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)