A Fusion of LightGBM, PLSRegression, and ElasticNet Regressors in Advanced Ensemble Stacking for Empowering Predictive Modeling

Every expert domain in the world from finance to healthcare and elsewhere involve predictive modelling. The techniques are presented in this paper as Advanced Ensemble Stacking which successfully combines three regressors, namely LightGBM, PLSRegression and ElasticNet. We made LightGBM as our light ensemble because it is more efficient and predictive. This model utilizes data in an exceptional way to capture intricate details and non-linear integrations. In contrast, PLSRegression offers feature reduction and selection which helps to better define the model without sacrificing too much predictive power. This robust ensemble is provided with contributions from ElasticNet which adds regularization and sparsity to the ensemble's models, providing further protection against overfitting. Our method is further demonstrated in real world datasets. During testing, we report results with considerable improvement in predictive performance, especially when using previously ineffective isolated models. ElasticNet regularization properties further improve the ability of the ensemble in practical use while PLSRegression is used as a meta regressor. A new ensemble method is introduced in this paper which combines LightGBM, PLS Regression and Elastic Net to increase predictivity and robustness. The combination of these algorithms employs the strengths of each algorithm making the model developed more favourable than the traditional single-model focused strategies. Empirical testing demonstrates that the method is superior to its alternatives across several performance measures. To conclude, we would like to mention that our Advanced Ensemble Stacking method is a step forward in empowering predictive modelling in industry or market. Integrating such heterogeneous model frameworks like LightGBM, PLSRegression and ElasticNet, we provide a broader and more powerful approach preserving soundness with higher modelling accuracy. We think that this ensemble approach can be a reason for further improvement of predictive modelling in complex and difficult practical issues.