ExtraTrees
Extra Trees creates an ensemble of randomized Decision Trees. Each tree is trained to a subset of the data set and the partial results are averaged. This increases the accuracy of the prediction in comparison with the Decision Tree and the tendency toward overfitting is reduced.
Supported properties
ONNX support
- TreeEnsambleClassifier
- TreeEnsambleRegressor
Samples of the export of Extra Trees can be found here: ONNX export of Extra Trees.
 | Classification limitation With classification models, only the output of the labels is mapped in the PLC. The scores/probabilities are not available in the PLC. |
Supported data types
A distinction must be made between "supported datatype" and "preferred datatype". The preferred datatype corresponds to the precision of the execution engine.
The preferred datatype is floating point 64 (E_MLLDT_FP64-LREAL).
When using a supported datatype, an efficient type conversion automatically takes place in the library. Slight losses of performance can occur due to the type conversion.
A list of the supported datatypes can be found in ETcMllDataType.