Fig. 11
From: Edge machine learning over IoT for chipless RFID environmental sensing in smart agriculture
(a) XGBoost classification metrics for discrete humidity levels from 0 to 90% RH, (b) Residual plot for Gradient Boosting regression illustrating that most prediction errors lie within ± 10% RH with MAE = ± 2.1% RH.
