noether

Type Members

1. case class AUC(metric: AUCMetric, samples: Int = 100) extends Aggregator[Prediction[Boolean, Double], Curve, Double] with Product with Serializable

   

   Compute the "Area Under the Curve" for a collection of predictions.

   Compute the "Area Under the Curve" for a collection of predictions. Uses the Trapezoid method to compute the area.

   Internally a linspace is defined using the given number of samples. Each point in the linspace represents a threshold which is used to build a confusion matrix. The area is then defined on this list of confusion matrices.

   AUCMetric which is given to the aggregate selects the function to apply on the confusion matrix prior to the AUC calculation.

   metric

   Which function to apply on the confusion matrix.

   samples

   Number of samples to use for the curve definition.

2. sealed trait AUCMetric extends AnyRef

   

   Which function to apply on the list of confusion matrices prior to the AUC calculation.

3. case class BinaryConfusionMatrix(threshold: Double = 0.5) extends Aggregator[Prediction[Boolean, Double], Map[(Int, Int), Long], DenseMatrix[Long]] with Product with Serializable

   

   Special Case for a Binary Confusion Matrix to make it easier to compose with other binary aggregators

   Special Case for a Binary Confusion Matrix to make it easier to compose with other binary aggregators

   threshold

   Threshold to apply on predictions

4. final case class ClassificationReport(threshold: Double = 0.5, beta: Double = 1.0) extends Aggregator[Prediction[Boolean, Double], Map[(Int, Int), Long], Report] with Product with Serializable

   

   Generate a Classification Report for a collection of binary predictions.

   Generate a Classification Report for a collection of binary predictions. The output of this aggregator will be a Report object.

   threshold

   Threshold to apply to get the predictions.

   beta

   Beta parameter used in the f-score calculation.

5. final case class ConfusionMatrix(labels: Seq[Int]) extends Aggregator[Prediction[Int, Int], Map[(Int, Int), Long], DenseMatrix[Long]] with Product with Serializable

   

   Generic Consfusion Matrix Aggregator for any dimension.

   Generic Consfusion Matrix Aggregator for any dimension. Thresholds must be applied to make a prediction prior to using this aggregator.

   labels

   List of possible label values

6. final case class MultiClassificationReport(labels: Seq[Int], beta: Double = 1.0) extends Aggregator[Prediction[Int, Int], Map[(Int, Int), Long], Map[Int, Report]] with Product with Serializable

   

   Generate a Classification Report for a collection of multiclass predictions.

   Generate a Classification Report for a collection of multiclass predictions. A report is generated for each class by treating the predictions as binary of either "class" or "not class". The output of this aggregator will be a map of classes and their Report objects.

   labels

   List of possible label values.

   beta

   Beta parameter used in the f-score calculation.

7. final case class Prediction[L, S](actual: L, predicted: S) extends Product with Serializable

   

   Generic Prediction Object used by most aggregators

   Generic Prediction Object used by most aggregators

   L

   Type of the Real Value

   S

   Type of the Predicted Value

   actual

   Real value for this entry. Also normally seen as label.

   predicted

   Predicted value. Can be a class or a score depending on the aggregator.

8. final case class Report(mcc: Double, fscore: Double, precision: Double, recall: Double, accuracy: Double, fpr: Double) extends Product with Serializable

   

   Classification Report

   Classification Report

   mcc

   Matthews Correlation Coefficient

   fscore

   f-score

   precision

   Precision

   recall

   Recall

   accuracy

   Accuracy

   fpr

   False Positive Rate