Ensemble models, a major concept in machine learning, combine several basic models to improve predictive performance (Sagi & Rokach, 2018). Ensemble models' main components include stacking, boosting, and bagging (Brownlee, 2021). When used in a voting panel, bagging aggregates the predictions of several separate models to arrive at a final decision. By assigning greater weight to previously incorrectly categorized data points, boosting iteratively enhances the performance of a weak learner, much like a sports team coach honing the skills of individual players over time. Stacking combines predictions from multiple models, frequently with a meta-learner, to make a more accurate final prediction, like a chef adding different ingredients to create a unique meal.

There is a standardized nine-step method in the domain of neural network projects. Problem definition is the first step in this process, followed by data collection, preprocessing, model selection, architecture design, training, tuning, deployment, and ongoing monitoring (Asghari et al., 2023). These procedures offer a systematic way to create successful neural network solutions, more like a recipe for a sophisticated cuisine.