Package org.apache.spark.ml.classification

Interface Summary

Interface	Description
LogisticRegressionSummary	Abstraction for Logistic Regression Results for a given model.
LogisticRegressionTrainingSummary	Abstraction for multinomial Logistic Regression Training results.

Class Summary

Class	Description
BinaryLogisticRegressionSummary	:: Experimental :: Binary Logistic regression results for a given model.
BinaryLogisticRegressionTrainingSummary	:: Experimental :: Logistic regression training results.
ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>	:: DeveloperApi ::
Classifier<FeaturesType,E extends Classifier<FeaturesType,E,M>,M extends ClassificationModel<FeaturesType,M>>	:: DeveloperApi ::
DecisionTreeClassificationModel	Decision tree model (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification.
DecisionTreeClassifier	Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification.
GBTClassificationModel	Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) model for classification.
GBTClassifier	Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) learning algorithm for classification.
LabelConverter	Label to vector converter.
LinearSVC	:: Experimental ::
LinearSVCAggregator	LinearSVCAggregator computes the gradient and loss for hinge loss function, as used in binary classification for instances in sparse or dense vector in an online fashion.
LinearSVCCostFun	LinearSVCCostFun implements Breeze's DiffFunction[T] for hinge loss function
LinearSVCModel	:: Experimental :: Linear SVM Model trained by LinearSVC
LogisticAggregator	LogisticAggregator computes the gradient and loss for binary or multinomial logistic (softmax) loss function, as used in classification for instances in sparse or dense vector in an online fashion.
LogisticCostFun	LogisticCostFun implements Breeze's DiffFunction[T] for a multinomial (softmax) logistic loss function, as used in multi-class classification (it is also used in binary logistic regression).
LogisticRegression	Logistic regression.
LogisticRegressionModel	Model produced by LogisticRegression.
MultilayerPerceptronClassificationModel	Classification model based on the Multilayer Perceptron.
MultilayerPerceptronClassifier	Classifier trainer based on the Multilayer Perceptron.
NaiveBayes	Naive Bayes Classifiers.
NaiveBayesModel	Model produced by NaiveBayes param: pi log of class priors, whose dimension is C (number of classes) param: theta log of class conditional probabilities, whose dimension is C (number of classes) by D (number of features)
OneVsRest	Reduction of Multiclass Classification to Binary Classification.
OneVsRestModel	Model produced by OneVsRest.
ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>	:: DeveloperApi ::
ProbabilisticClassifier<FeaturesType,E extends ProbabilisticClassifier<FeaturesType,E,M>,M extends ProbabilisticClassificationModel<FeaturesType,M>>	:: DeveloperApi ::
RandomForestClassificationModel	Random Forest model for classification.
RandomForestClassifier	Random Forest learning algorithm for classification.