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 
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.

LabelConverter 
Label to vector converter.

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 multiclass 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.
