@@ -869,6 +870,14 @@ Finally, the Regressor plugin supports the following additional REST API actions
| train | Triggers a new training phase for the random forest model. Feature vectors are temporarily collected in-memory until _trainingSamples_ vectors are obtained. Until this moment, the old random forest model is still used to perform prediction.
| importances | Returns the sorted importance values for the input features, together with the respective labels, if available.
The _Classifier_ plugin, as the name implies, performs machine learning classification. It is based on the Regressor plugin, and as such it also uses OpenCV random forest models. The plugin supplies the same options and has the same behavior as the Regressor plugin, with the following two exceptions:
* The _target_ parameter here indicates a sensor which stores the labels (as numerical integer identifiers) to be used for training and on which classification will be based. The mapping from the integer labels to their text equivalent is left to the users. Moreover, unlike in the
Regressor plugin, the target sensor is always excluded from the feature vectors.
* The _targetDistance_ parameter is not used here, as it is only meaningful for regression.
The _Clustering_ plugin implements a gaussian mixture model for performance variation analysis and outlier detection. The plugin is based on the OpenCV library, similarly to the _Regressor_ plugin.