Decision Tree
A Decision Tree is an ML model that uses a tree-like structure to make predictions. It is a simple, but powerful tool for the prediction of values (regression) or classes (classification) on the basis of several inputs, which works by dividing the data into smaller and smaller subsets until a final decision is made. The structure of the tree enables a simple interpretation and visualization of the model.
Supported properties
ONNX support
- TreeEnsambleClassifier
- TreeEnsambleRegressor
Samples of the export of Decision Tree models can be found here: ONNX export of a Decision Tree.
 | 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.