classification

Type Members

1. class BinaryLogisticRegressionSummary extends LogisticRegressionSummary

   

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

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

   Annotations

   @Experimental()

2. class BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

   

   :: Experimental :: Logistic regression training results.

   :: Experimental :: Logistic regression training results.

   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. final class DecisionTreeClassificationModel extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with Serializable

   

   :: 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

   @Experimental()

6. final class DecisionTreeClassifier extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] with DecisionTreeParams with TreeClassifierParams

   

   :: 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

   @Experimental()

7. final class GBTClassificationModel extends PredictionModel[Vector, GBTClassificationModel] with TreeEnsembleModel with Serializable

   

   :: 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

   @Experimental()

8. final class GBTClassifier extends Predictor[Vector, GBTClassifier, GBTClassificationModel] with GBTParams with TreeClassifierParams with Logging

   

   :: 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.

   Annotations

   @Experimental()

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

   

   :: Experimental :: Logistic regression.

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

   Annotations

   @Experimental()

10. class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams

    

    :: Experimental :: Model produced by LogisticRegression.

    :: Experimental :: Model produced by LogisticRegression.

    Annotations

    @Experimental()

11. sealed trait LogisticRegressionSummary extends Serializable

    

    Abstraction for Logistic Regression Results for a given model.

12. sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary

    

    Abstraction for multinomial Logistic Regression Training results.

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

    

    :: 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

    @Experimental()

14. class MultilayerPerceptronClassifier extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel] with MultilayerPerceptronParams

    

    :: 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

    @Experimental()

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

    

    :: 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

    @Experimental()

16. class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams

    

    :: Experimental :: Model produced by NaiveBayes

    :: Experimental :: Model produced by NaiveBayes

    Annotations

    @Experimental()

17. final class OneVsRest extends Estimator[OneVsRestModel] with OneVsRestParams

    

    :: 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

    @Experimental()

18. final class OneVsRestModel extends Model[OneVsRestModel] with OneVsRestParams

    

    :: 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

    @Experimental()

19. 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()

20. 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()

21. final class RandomForestClassificationModel extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable

    

    :: 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

    @Experimental()

22. final class RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestParams with TreeClassifierParams

    

    :: 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

    @Experimental()