Histogram-based Gradient Boosting
A histogram-based Gradient Boosting model can be used both for classification and for regression.
The model is based on the Gradient Boosting; here, however, the continual inputs are discretized in bins with the help of a histogram. This hugely accelerates the training of the model, in particular with very large data sets.
Supported properties
ONNX support
- TreeEnsambleClassifier
- TreeEnsambleRegressor
Samples of the export of Hist Gradient Boosting models can be found here: ONNX export of Hist Gradient Boosting.
 | 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.