Interface  Description 

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

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

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

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

Class  Description 

BinaryLogisticRegressionSummaryImpl 
Binary logistic regression results for a given model.

BinaryLogisticRegressionTrainingSummaryImpl 
Binary 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 
GradientBoosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
model for classification.

GBTClassifier 
GradientBoosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
learning algorithm for classification.

LinearSVC 
:: Experimental ::

LinearSVCModel 
:: Experimental ::
Linear SVM Model trained by
LinearSVC 
LogisticRegression 
Logistic regression.

LogisticRegressionModel 
Model produced by
LogisticRegression . 
LogisticRegressionSummaryImpl 
Multiclass logistic regression results for a given model.

LogisticRegressionTrainingSummaryImpl 
Multiclass logistic regression training results.

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.
