| Class | Description |
|---|---|
| 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. |