class RidgeRegressionModel extends GeneralizedLinearModel with RegressionModel with Serializable with Saveable with PMMLExportable
Regression model trained using RidgeRegression.
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- @Since( "0.8.0" )
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- RidgeRegression.scala
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- RidgeRegressionModel
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- RegressionModel
- GeneralizedLinearModel
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val
intercept: Double
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- RidgeRegressionModel → 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
- RegressionModel
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- @Since( "1.0.0" )
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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
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- @Since( "1.0.0" )
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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" )
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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.
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- RidgeRegressionModel → GeneralizedLinearModel
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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.
- Definition Classes
- RidgeRegressionModel → Saveable
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- @Since( "1.3.0" )
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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
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- @Since( "1.4.0" )
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def
toPMML(outputStream: OutputStream): Unit
Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
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- PMMLExportable
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- @Since( "1.4.0" )
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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
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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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def
toString(): String
Print a summary of the model.
Print a summary of the model.
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- GeneralizedLinearModel → AnyRef → Any
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val
weights: Vector
- Definition Classes
- RidgeRegressionModel → GeneralizedLinearModel
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- @Since( "1.0.0" )
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