org.apache.spark.mllib.classification
LogisticRegressionModel
Companion object LogisticRegressionModel
class LogisticRegressionModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable
Classification model trained using Multinomial/Binary Logistic Regression.
- Annotations
- @Since( "0.8.0" )
- Source
- LogisticRegression.scala
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- LogisticRegressionModel
- PMMLExportable
- Saveable
- ClassificationModel
- GeneralizedLinearModel
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Instance Constructors
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new
LogisticRegressionModel(weights: Vector, intercept: Double)
Constructs a LogisticRegressionModel with weights and intercept for binary classification.
Constructs a LogisticRegressionModel with weights and intercept for binary classification.
- Annotations
- @Since( "1.0.0" )
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new
LogisticRegressionModel(weights: Vector, intercept: Double, numFeatures: Int, numClasses: Int)
- weights
Weights computed for every feature.
- intercept
Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.)
- numFeatures
the dimension of the features.
- numClasses
the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.
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- @Since( "1.3.0" )
Value Members
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!=(arg0: Any): Boolean
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def
asInstanceOf[T0]: T0
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def
clearThreshold(): LogisticRegressionModel.this.type
Clears the threshold so that
predictwill output raw prediction scores.Clears the threshold so that
predictwill output raw prediction scores. It is only used for binary classification.- Annotations
- @Since( "1.0.0" )
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def
clone(): AnyRef
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def
getClass(): Class[_]
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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. It is only used for binary classification.
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- @Since( "1.3.0" )
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def
hashCode(): Int
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val
intercept: Double
- Definition Classes
- LogisticRegressionModel → GeneralizedLinearModel
- Annotations
- @Since( "1.0.0" )
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def
isInstanceOf[T0]: Boolean
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val
numClasses: Int
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- @Since( "1.3.0" )
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val
numFeatures: Int
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- @Since( "1.3.0" )
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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" )
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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
- Annotations
- @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.
- Attributes
- protected
- Definition Classes
- LogisticRegressionModel → 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
- LogisticRegressionModel → Saveable
- Annotations
- @Since( "1.3.0" )
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def
setThreshold(threshold: Double): LogisticRegressionModel.this.type
Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression.
Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression. An example with prediction score greater than or equal to this threshold is identified as a positive, and negative otherwise. The default value is 0.5. It is only used for binary classification.
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- @Since( "1.0.0" )
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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
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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
- Definition Classes
- 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
- Definition Classes
- PMMLExportable
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- @Since( "1.4.0" )
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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
- Definition Classes
- PMMLExportable
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- @Since( "1.4.0" )
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def
toString(): String
Print a summary of the model.
Print a summary of the model.
- Definition Classes
- LogisticRegressionModel → GeneralizedLinearModel → AnyRef → Any
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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val
weights: Vector
- Definition Classes
- LogisticRegressionModel → GeneralizedLinearModel
- Annotations
- @Since( "1.0.0" )