Interface  Description 

LogisticRegressionSummary 
Abstraction for Logistic Regression Results for a given model.

LogisticRegressionTrainingSummary 
Abstraction for multinomial Logistic Regression Training results.

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 ::
GradientBoosted Trees (GBTs)
model for classification. 
GBTClassifier 
:: Experimental ::
GradientBoosted 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 multiclass 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. 