classification

Type Members

1. trait AbstractLogisticRegressionModel extends ProbabilisticClassificationModel

   

2. case class BinaryLogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Double) extends AbstractLogisticRegressionModel with Product with Serializable

   

3. trait ClassificationModel extends Model

   

   Trait for all classification models.

4. case class DecisionTreeClassifierModel(rootNode: Node, numFeatures: Int, numClasses: Int) extends ProbabilisticClassificationModel with DecisionTree with Serializable with Product

   

   Class for decision tree classification models.

   Class for decision tree classification models.

   rootNode

   root decision tree node

   numFeatures

   number of features used in prediction

   numClasses

   number of predictable classes

5. case class GBTClassifierModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int, thresholds: Option[Array[Double]] = None) extends ProbabilisticClassificationModel with TreeEnsemble with Serializable with Product

   

   Class for a gradient boost classifier model.

   Class for a gradient boost classifier model.

   trees

   trees in the gradient boost model

   treeWeights

   weights of each tree

   numFeatures

   number of features

6. case class LogisticRegressionModel(impl: AbstractLogisticRegressionModel) extends ProbabilisticClassificationModel with Product with Serializable

   

7. case class MultiLayerPerceptronClassifierModel(layers: Seq[Int], weights: Vector) extends Model with Product with Serializable

   

   Annotations

   @SparkCode()

8. case class NaiveBayesModel(numFeatures: Int, numClasses: Int, pi: Vector, theta: Matrix, modelType: ModelType) extends ProbabilisticClassificationModel with Model with Product with Serializable

   
   numFeatures

   number of features in feature vector

   numClasses

   number of labels or labels to classify predictions into

   pi

   log of class priors

   theta

   log of class conditional probabilities

   Annotations

   @SparkCode()

9. case class OneVsRestModel(classifiers: Array[ProbabilisticClassificationModel], numFeatures: Int) extends Model with Product with Serializable

   

   Class for multinomial one vs rest models.

   Class for multinomial one vs rest models.

   One vs rest models are comprised of a series of ProbabilisticClassificationModels which are used to predict each class.

   classifiers

   binary classification models

10. trait ProbabilisticClassificationModel extends ClassificationModel

    

    Trait for classification models.

    Trait for classification models.

    This trait handles multinomial classification models as well as binary classification models.

    Annotations

    @SparkCode()

11. case class ProbabilisticLogisticsRegressionModel(coefficientMatrix: Matrix, interceptVector: Vector, thresholds: Option[Array[Double]]) extends AbstractLogisticRegressionModel with Product with Serializable

    

12. case class RandomForestClassifierModel(trees: Seq[DecisionTreeClassifierModel], treeWeights: Seq[Double], numFeatures: Int, numClasses: Int) extends ProbabilisticClassificationModel with TreeEnsemble with Serializable with Product

    

    Class for random forest classification models.

    Class for random forest classification models.

    trees

    trees of the random forest

    numFeatures

    number of features in feature vector

    numClasses

    number of predictable classes

13. case class SupportVectorMachineModel(coefficients: Vector, intercept: Double, thresholds: Option[Array[Double]] = ...) extends ProbabilisticClassificationModel with Serializable with Product

    

    Class for support vector machine models.

    Class for support vector machine models.

    coefficients

    coefficients of SVM

    intercept

    intercept for SVM

    thresholds

    threshold for pegging prediction