Classification Learner App Backward Compatibility
2 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Mohamed
am 16 Apr. 2025
Kommentiert: Mohamed
am 22 Apr. 2025
I have trained a coarse tree model using R2024b and it has been exported to simulink and integrated to a system. I need to export the whole system including the learner subsystem to R2018b. When I did that, The learner subsystem became empty, even though from what I know is that coarse tree is supported in R2018b. Are models not backward compatible? If not all of them, then what models are backward compatible from R2024b to R2018b?
Thanks in advance!
0 Kommentare
Akzeptierte Antwort
Shantanu Dixit
am 22 Apr. 2025
Bearbeitet: Shantanu Dixit
am 22 Apr. 2025
Hi Mohamed, If I understood the query correctly you are using 'Export Model' option to export the tree model to Simulink, which generates a Simulink subsystem with a dedicated block (eg. 'ClassificationTree Predict' block: https://www.mathworks.com/help/stats/classificationtreepredict.html).
While the tree model itself (here 'ClassificationTree') is supported in R2018b, the Simulink Prediction blocks (e.g., 'ClassificationTree Predict') are not available in older releases. Specifically 'ClassificationTree Predict' and'RegressionTree Predict' block were introduced in R2021a, which might be the reason that older MATLAB version might not be recognizing them, resulting in empty subsytems.
As a workaround, you can replace the unsupported prediction block with a MATLAB function block that calls the 'predict' method of the tree model. You can refer to the following documentation https://www.mathworks.com/help/stats/predict-class-labels-using-matlab-function-block.html which details on using a MATLAB function for predicting labels from an ML model.
Hope this helps!
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Subsystems finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!