Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective

squared error scoring parameter. GridSearchCV is an approach in machine learning that focuses on tuning hyperparameters to identify the most effective combination for a given model systematically. This method necessitates the definition of a parameter grid, which out- lines the potential values for each hyperparameter to be explored. In our investigation, the selection of the parameter grid is influenced by the recommendations outlined in [35, 42, 47]. We also employed the linear kernel support vector regression method as the meta regressor motivated by Panigrahi et al. [50]. The basic idea behind stacking is to use the strengths of multiple base models to compen- sate for their weaknesses. Each base model is trained on the training data using a different algorithm and generates predictions for the validation or test data. The predictions from the base models are then combined using a meta-model, which is trained on the predictions gen- erated by the base models. The meta-model learns how to weigh the contributions of the base models based on their past performance, and generates the final prediction as shown in Fig 1. The evaluation metrics of the model performances. In this study, statistical measures were utilized as metrics to assess the performance of the proposed stacking ensemble predic- tion model. To evaluate the model’s ability to fit the data, performance evaluation metrics were employed. The proposed stacking ensemble model and the base models were evaluated based on the mean square error (MSE) and the mean absolute error (MAE). The formulas for

MSE and MAE can be found in Eqs 11 and 12, respectively. Hence, the preference is for smaller values as they signify improved model performance in minimizing the discrepancies between predicted and actual values. MSE ¼ Pn i¼1 yi ^yi ð Þ2 n ð11Þ MAE ¼ Pn i¼1 jyi ^yij2 n ð12Þ Fig 1. The model framework of stacking ensemble learning model. https://doi.org/10.1371/journal.pone.0305762.g001 PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 10 / 24