classification

Type Members

1. sealed trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary

   

   :: Experimental :: Abstraction for binary logistic regression results for a given model.

   :: Experimental :: Abstraction for binary logistic regression results for a given model.

   Currently, the summary ignores the instance weights.

   Annotations

   @Experimental()

2. sealed trait BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

   

   :: Experimental :: Abstraction for binary logistic regression training results.

   :: Experimental :: Abstraction for binary logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.

   Annotations

   @Experimental()

3. abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with ClassifierParams

   

   :: 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

   

   :: 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

   

   Decision tree model (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification.

   Decision tree model (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

   Annotations

   @Since( "1.4.0" )

6. class DecisionTreeClassifier extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] with DecisionTreeClassifierParams with DefaultParamsWritable

   

   Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification.

   Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

   Annotations

   @Since( "1.4.0" )

7. class GBTClassificationModel extends ProbabilisticClassificationModel[Vector, GBTClassificationModel] with GBTClassifierParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

   

   Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) model for classification.

   Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) model for classification. It supports binary labels, as well as both continuous and categorical features.

   Annotations

   @Since( "1.6.0" )

   Note

   Multiclass labels are not currently supported.

8. class GBTClassifier extends ProbabilisticClassifier[Vector, GBTClassifier, GBTClassificationModel] with GBTClassifierParams with DefaultParamsWritable with Logging

   

   Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) learning algorithm for classification.

   Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features.

   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" )

   Note

   Multiclass labels are not currently supported.

9. class LinearSVC extends Classifier[Vector, LinearSVC, LinearSVCModel] with LinearSVCParams with DefaultParamsWritable

   

   :: Experimental ::

   :: Experimental ::

   Linear SVM Classifier

   This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

   Annotations

   @Since( "2.2.0" ) @Experimental()

10. class LinearSVCModel extends ClassificationModel[Vector, LinearSVCModel] with LinearSVCParams with MLWritable

    

    :: Experimental :: Linear SVM Model trained by LinearSVC

    :: Experimental :: Linear SVM Model trained by LinearSVC

    Annotations

    @Since( "2.2.0" ) @Experimental()

11. class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

    

    Logistic regression.

    Logistic regression. Supports:

    • Multinomial logistic (softmax) regression.
    • Binomial logistic regression.

    This class supports fitting traditional logistic regression model by LBFGS/OWLQN and bound (box) constrained logistic regression model by LBFGSB.

    Annotations

    @Since( "1.2.0" )

12. class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams with MLWritable

    

    Model produced by LogisticRegression.

    Model produced by LogisticRegression.

    Annotations

    @Since( "1.4.0" )

13. sealed trait LogisticRegressionSummary extends Serializable

    

    :: Experimental :: Abstraction for logistic regression results for a given model.

    :: Experimental :: Abstraction for logistic regression results for a given model.

    Currently, the summary ignores the instance weights.

    Annotations

    @Experimental()

14. sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary

    

    :: Experimental :: Abstraction for multiclass logistic regression training results.

    :: Experimental :: Abstraction for multiclass logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.

    Annotations

    @Experimental()

15. class MultilayerPerceptronClassificationModel extends ProbabilisticClassificationModel[Vector, MultilayerPerceptronClassificationModel] with Serializable with MLWritable

    

    Classification model based on the Multilayer Perceptron.

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

    Annotations

    @Since( "1.5.0" )

16. class MultilayerPerceptronClassifier extends ProbabilisticClassifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel] with MultilayerPerceptronParams with DefaultParamsWritable

    

    Classifier trainer based on the Multilayer Perceptron.

    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" )

17. class NaiveBayes extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel] with NaiveBayesParams with DefaultParamsWritable

    

    Naive Bayes Classifiers.

    Naive Bayes Classifiers. It supports Multinomial NB (see here) 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 (see here). The input feature values must be nonnegative.

    Annotations

    @Since( "1.5.0" )

18. class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams with MLWritable

    

    Model produced by NaiveBayes

    Model produced by NaiveBayes

    Annotations

    @Since( "1.5.0" )

19. final class OneVsRest extends Estimator[OneVsRestModel] with OneVsRestParams with HasParallelism with MLWritable

    

    Reduction of Multiclass Classification to Binary Classification.

    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" )

20. final class OneVsRestModel extends Model[OneVsRestModel] with OneVsRestParams with MLWritable

    

    Model produced by OneVsRest.

    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" )

21. abstract class ProbabilisticClassificationModel[FeaturesType, M <: ProbabilisticClassificationModel[FeaturesType, M]] extends ClassificationModel[FeaturesType, M] with ProbabilisticClassifierParams

    

    :: 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()

22. abstract class ProbabilisticClassifier[FeaturesType, E <: ProbabilisticClassifier[FeaturesType, E, M], M <: ProbabilisticClassificationModel[FeaturesType, M]] extends Classifier[FeaturesType, E, M] with ProbabilisticClassifierParams

    

    :: 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()

23. class RandomForestClassificationModel extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel] with RandomForestClassifierParams with TreeEnsembleModel[DecisionTreeClassificationModel] with MLWritable with Serializable

    

    Random Forest model for classification.

    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" )

24. class RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestClassifierParams with DefaultParamsWritable

    

    Random Forest learning algorithm for classification.

    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" )