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Class Summary | |
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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. |
LogisticAggregator | LogisticAggregator computes the gradient and loss for binary logistic loss function, as used in binary classification for samples 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 . |
OneVsRest | :: Experimental :: |
OneVsRestModel | :: Experimental ::
Model produced by OneVsRest . |
RandomForestClassificationModel | :: Experimental ::
Random Forest model for classification. |
RandomForestClassifier | :: Experimental ::
Random Forest learning algorithm for
classification. |
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