pcc

https://www.sciencedirect.com/science/article/pii/S0022169421001475 2.3. Input combination Because some input parameters are more important or relevant than others, it is important to exclude those that hamper the modeling performance without improving the effectiveness of the modeling. To select the best input array, four different input combinations were constructed based on the Pearson correlation coefficient and finally tested in order to find the most effective one. To start with, the parameter with the highest degree of Pearson correlation coefficient (r) was considered as the first input parameter to the model. The assumption is that the parameter with the highest correlation with the output has a better ability to predict the output with higher accuracy. Then, the next parameter with the next highest r value was added to the first input and the selection “input No. 2” was defined. This process continued until the parameter with the lowest r value was added to the combination of input parameters and the selection “input No. 4” was defined (Table 3). The most effective input combination was identified by comparing the effectiveness of each input combination using the root mean square error (RMSE). ise tumhe re-write karna hain and link is given, apne paper k hisab se u need to re-write using quillbot