Package

org.apache.spark.ml

classification

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package classification

Visibility
  1. Public
  2. All

Type Members

  1. class BinaryLogisticRegressionSummary extends LogisticRegressionSummary

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    :: Experimental :: Binary Logistic regression results for a given model.

    :: Experimental :: Binary Logistic regression results for a given model.

    Annotations
    @Experimental() @Since( "1.5.0" )
  2. class BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

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    :: Experimental :: Logistic regression training results.

    :: Experimental :: Logistic regression training results.

    Annotations
    @Experimental() @Since( "1.5.0" )
  3. abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with ClassifierParams

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Model produced by a Classifier. Classes are indexed {0, 1, ..., numClasses - 1}.

    FeaturesType

    Type of input features. E.g., Vector

    M

    Concrete Model type

    Annotations
    @DeveloperApi()
  4. abstract class Classifier[FeaturesType, E <: Classifier[FeaturesType, E, M], M <: ClassificationModel[FeaturesType, M]] extends Predictor[FeaturesType, E, M] with ClassifierParams

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Single-label binary or multiclass classification. Classes are indexed {0, 1, ..., numClasses - 1}.

    FeaturesType

    Type of input features. E.g., Vector

    E

    Concrete Estimator type

    M

    Concrete Model type

    Annotations
    @DeveloperApi()
  5. class DecisionTreeClassificationModel extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with DecisionTreeClassifierParams with MLWritable with Serializable

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    :: Experimental :: Decision tree model for classification.

    :: Experimental :: Decision tree model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  6. class DecisionTreeClassifier extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] with DecisionTreeClassifierParams with DefaultParamsWritable

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    :: Experimental :: Decision tree learning algorithm for classification.

    :: Experimental :: Decision tree learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  7. class GBTClassificationModel extends PredictionModel[Vector, GBTClassificationModel] with GBTClassifierParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

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    :: Experimental :: Gradient-Boosted Trees (GBTs) model for classification.

    :: Experimental :: Gradient-Boosted Trees (GBTs) model for classification. It supports binary labels, as well as both continuous and categorical features. Note: Multiclass labels are not currently supported.

    Annotations
    @Since( "1.6.0" ) @Experimental()
  8. class GBTClassifier extends Predictor[Vector, GBTClassifier, GBTClassificationModel] with GBTClassifierParams with DefaultParamsWritable with Logging

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    :: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for classification.

    :: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features. Note: Multiclass labels are not currently supported.

    The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

    Notes on Gradient Boosting vs. TreeBoost:

    • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
    • Both algorithms learn tree ensembles by minimizing loss functions.
    • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
    • We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]
    Annotations
    @Since( "1.4.0" ) @Experimental()
  9. class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

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    :: Experimental :: Logistic regression.

    :: Experimental :: Logistic regression. Currently, this class only supports binary classification. It will support multiclass in the future.

    Annotations
    @Since( "1.2.0" ) @Experimental()
  10. class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams with MLWritable

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    :: Experimental :: Model produced by LogisticRegression.

    :: Experimental :: Model produced by LogisticRegression.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  11. sealed trait LogisticRegressionSummary extends Serializable

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    Abstraction for Logistic Regression Results for a given model.

  12. sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary

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    Abstraction for multinomial Logistic Regression Training results.

    Abstraction for multinomial Logistic Regression Training results. Currently, the training summary ignores the training weights except for the objective trace.

  13. class MultilayerPerceptronClassificationModel extends PredictionModel[Vector, MultilayerPerceptronClassificationModel] with Serializable with MLWritable

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    :: Experimental :: Classification model based on the Multilayer Perceptron.

    :: Experimental :: Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.

    returns

    prediction model

    Annotations
    @Since( "1.5.0" ) @Experimental()
  14. class MultilayerPerceptronClassifier extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel] with MultilayerPerceptronParams with DefaultParamsWritable

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    :: Experimental :: Classifier trainer based on the Multilayer Perceptron.

    :: Experimental :: Classifier trainer based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax. Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels.

    Annotations
    @Since( "1.5.0" ) @Experimental()
  15. class NaiveBayes extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel] with NaiveBayesParams with DefaultParamsWritable

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    :: Experimental :: Naive Bayes Classifiers.

    :: Experimental :: Naive Bayes Classifiers. It supports both Multinomial NB (http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). The input feature values must be nonnegative.

    Annotations
    @Since( "1.5.0" ) @Experimental()
  16. class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams with MLWritable

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    :: Experimental :: Model produced by NaiveBayes

    :: Experimental :: Model produced by NaiveBayes

    Annotations
    @Since( "1.5.0" ) @Experimental()
  17. final class OneVsRest extends Estimator[OneVsRestModel] with OneVsRestParams with MLWritable

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    :: Experimental ::

    :: Experimental ::

    Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  18. final class OneVsRestModel extends Model[OneVsRestModel] with OneVsRestParams with MLWritable

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    :: Experimental :: Model produced by OneVsRest.

    :: Experimental :: Model produced by OneVsRest. This stores the models resulting from training k binary classifiers: one for each class. Each example is scored against all k models, and the model with the highest score is picked to label the example.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  19. abstract class ProbabilisticClassificationModel[FeaturesType, M <: ProbabilisticClassificationModel[FeaturesType, M]] extends ClassificationModel[FeaturesType, M] with ProbabilisticClassifierParams

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Model produced by a ProbabilisticClassifier. Classes are indexed {0, 1, ..., numClasses - 1}.

    FeaturesType

    Type of input features. E.g., Vector

    M

    Concrete Model type

    Annotations
    @DeveloperApi()
  20. abstract class ProbabilisticClassifier[FeaturesType, E <: ProbabilisticClassifier[FeaturesType, E, M], M <: ProbabilisticClassificationModel[FeaturesType, M]] extends Classifier[FeaturesType, E, M] with ProbabilisticClassifierParams

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Single-label binary or multiclass classifier which can output class conditional probabilities.

    FeaturesType

    Type of input features. E.g., Vector

    E

    Concrete Estimator type

    M

    Concrete Model type

    Annotations
    @DeveloperApi()
  21. class RandomForestClassificationModel extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel] with RandomForestClassificationModelParams with TreeEnsembleModel[DecisionTreeClassificationModel] with MLWritable with Serializable

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    :: Experimental :: Random Forest model for classification.

    :: Experimental :: Random Forest model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

    Annotations
    @Since( "1.4.0" ) @Experimental()
  22. class RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestClassifierParams with DefaultParamsWritable

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    :: Experimental :: Random Forest learning algorithm for classification.

    :: Experimental :: Random Forest learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

    Annotations
    @Since( "1.4.0" ) @Experimental()

Value Members

  1. object DecisionTreeClassificationModel extends MLReadable[DecisionTreeClassificationModel] with Serializable

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    Annotations
    @Since( "2.0.0" )
  2. object DecisionTreeClassifier extends DefaultParamsReadable[DecisionTreeClassifier] with Serializable

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    Annotations
    @Since( "1.4.0" ) @Experimental()
  3. object GBTClassificationModel extends MLReadable[GBTClassificationModel] with Serializable

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    Annotations
    @Since( "2.0.0" )
  4. object GBTClassifier extends DefaultParamsReadable[GBTClassifier] with Serializable

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    Annotations
    @Since( "1.4.0" ) @Experimental()
  5. object LogisticRegression extends DefaultParamsReadable[LogisticRegression] with Serializable

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    Annotations
    @Since( "1.6.0" )
  6. object LogisticRegressionModel extends MLReadable[LogisticRegressionModel] with Serializable

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    Annotations
    @Since( "1.6.0" )
  7. object MultilayerPerceptronClassificationModel extends MLReadable[MultilayerPerceptronClassificationModel] with Serializable

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    Annotations
    @Since( "2.0.0" )
  8. object MultilayerPerceptronClassifier extends DefaultParamsReadable[MultilayerPerceptronClassifier] with Serializable

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    Annotations
    @Since( "2.0.0" )
  9. object NaiveBayes extends DefaultParamsReadable[NaiveBayes] with Serializable

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    Annotations
    @Since( "1.6.0" )
  10. object NaiveBayesModel extends MLReadable[NaiveBayesModel] with Serializable

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    Annotations
    @Since( "1.6.0" )
  11. object OneVsRest extends MLReadable[OneVsRest] with Serializable

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    Annotations
    @Since( "2.0.0" )
  12. object OneVsRestModel extends MLReadable[OneVsRestModel] with Serializable

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    Annotations
    @Since( "2.0.0" )
  13. object RandomForestClassificationModel extends MLReadable[RandomForestClassificationModel] with Serializable

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    Annotations
    @Since( "2.0.0" )
  14. object RandomForestClassifier extends DefaultParamsReadable[RandomForestClassifier] with Serializable

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    Annotations
    @Since( "1.4.0" ) @Experimental()

Members