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	:: Experimental :: Decision tree model for classification.
DecisionTreeClassifier	:: Experimental :: Decision tree learning algorithm for classification.
GBTClassificationModel	:: Experimental :: Gradient-Boosted Trees (GBTs) model for classification.
GBTClassifier	:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for classification.
LabelConverter	Label to vector converter.
LogisticAggregator	LogisticAggregator computes the gradient and loss for binary logistic loss function, as used in binary classification for instances in sparse or dense vector in a online fashion.
LogisticCostFun	LogisticCostFun implements Breeze's DiffFunction[T] for a multinomial logistic loss function, as used in multi-class classification (it is also used in binary logistic regression).
LogisticRegression	:: Experimental :: Logistic regression.
LogisticRegressionModel	:: Experimental :: Model produced by LogisticRegression.
MultilayerPerceptronClassificationModel	:: Experimental :: Classification model based on the Multilayer Perceptron.
MultilayerPerceptronClassifier	:: Experimental :: Classifier trainer based on the Multilayer Perceptron.
NaiveBayes	:: Experimental :: Naive Bayes Classifiers.
NaiveBayesModel	:: Experimental :: 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	:: Experimental ::
OneVsRestModel	:: Experimental :: 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	:: Experimental :: Random Forest model for classification.
RandomForestClassifier	:: Experimental :: Random Forest learning algorithm for classification.