LogisticRegressionModel

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. val allowedParams: Seq[String]

   

5. final def asInstanceOf[T0]: T0

   

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6. def classifyInstances(featureMatrix: FeatureMatrix): Seq[(T, Double)]

   

   Give a score to every row in the feature matrix, according to the learned weights.

   Give a score to every row in the feature matrix, according to the learned weights.

   featureMatrix

   A feature matrix specified as a list of MatrixRow objects. Each row receives a score from the classifier.

   returns

   A map from source node to (target node, score) pairs, where the score is computed from the features in the feature matrix and the learned weights.

   Definition Classes

   BatchModel

7. def classifyMatrixRow(row: MatrixRow): Double

   

   Definition Classes

   LogisticRegressionModel → BatchModel

8. def clone(): AnyRef

   

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   protected[java.lang]

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9. def convertFeatureMatrixToMallet(featureMatrix: FeatureMatrix, dataset: Dataset[T], featureNames: Seq[String], data: InstanceList, alphabet: Alphabet): Unit

   

   Definition Classes

   BatchModel

10. final def eq(arg0: AnyRef): Boolean

    

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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13. final def getClass(): Class[_]

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. val l1Weight: Double

    

17. val l2Weight: Double

    

18. var lrWeights: Seq[Double]

    

19. def matrixRowToInstance(row: MatrixRow, alphabet: Alphabet): Instance

    

    Definition Classes

    BatchModel

20. final def ne(arg0: AnyRef): Boolean

    

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21. final def notify(): Unit

    

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22. final def notifyAll(): Unit

    

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23. final def synchronized[T0](arg0: ⇒ T0): T0

    

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24. def toString(): String

    

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25. def train(featureMatrix: FeatureMatrix, dataset: Dataset[T], featureNames: Seq[String]): Unit

    

    Given a feature matrix and a list of sources and targets that determines whether an instance is positive or negative, train a logistic regression classifier.

    Given a feature matrix and a list of sources and targets that determines whether an instance is positive or negative, train a logistic regression classifier.

    Definition Classes

    LogisticRegressionModel → BatchModel

26. final def wait(): Unit

    

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27. final def wait(arg0: Long, arg1: Int): Unit

    

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28. final def wait(arg0: Long): Unit

    

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