Class

classifiers

Classifier

Related Doc: package classifiers

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abstract class Classifier extends AnyRef

The base class of all classifiers

Linear Supertypes
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Known Subclasses
BoostedDecisionTreeClassifier, DecisionTreeClassifier, LogisticRegressionClassifier, NaiveBayesClassifier, NeuralNetworkClassifier, PerceptronClassifier, RandomClassifier, RandomForestClassifier, SVMClassifier, kNNClassifier
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Instance Constructors

  1. new Classifier()

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    Create a new classifier

Abstract Value Members

  1. abstract val name: String

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    The name of the classifier

  2. abstract def predict(X: List[List[Double]]): List[Int]

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    Applies the trained classifier to a dataset

    Applies the trained classifier to a dataset

    X

    List of data instances

    returns

    List of predictions

  3. abstract def train(X: List[List[Double]], y: List[Int], sampleWeight: List[Double] = Nil): Unit

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    Performs the training of the classifier

    Performs the training of the classifier

    X

    List of training instances

    y

    List of training labels

Concrete 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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. def diagnostics(): Map[String, List[(Double, Double)]]

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    Provides meta-information on the classifier

    Provides meta-information on the classifier

    returns

    Map object of metric names and metric values

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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

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  17. def toString(): String

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  18. final def wait(): Unit

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

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

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