Numerical and Experimental Investigation of Meteorological Data Using Adaptive Linear M5 Model Tree for the Prediction of Rainfall

Predicting a class with continuous numeric value encounters many problems when applying machine learning to the data. Only few machine-learning techniques can do this, but it is still considered one of most complex tasks perform. In this study, we demonstrate called M5 Model Tree, which handle This technique stepwise algorithm and uses linear functions at leaf nodes any decision tree inducer (like CART) constructed. These model trees generate simple practical formulas like standard deviation (SD), reduction (SDR), cost-complexity pruning (CCP), etc., be easily applied by another user some other benchmark work assesses abilities Tree for assessment rainfall data across Kashmir province Union Territory Jammu & Kashmir, India. The construction developed using (70–30) % training test ratio, respectively, was best fit models, predicting an RMSE 2.593, MAE 1.68, correlation coefficient (R2) 0.478. Moreover, use small number trails develop models thus need less computational time are therefore more convenient use.