mallinnusvaiheessa

Mallinnusvaiheessa is a term used in the context of machine learning and artificial intelligence to describe the phase of a model's development where the architecture and parameters of the model are being fine-tuned. This stage follows the initial training phase and precedes the deployment phase. During mallinnusvaiheessa, the model's performance is evaluated and optimized using validation datasets. Techniques such as hyperparameter tuning, regularization, and model pruning are commonly employed to improve the model's accuracy, efficiency, and generalization capabilities. The goal of this phase is to ensure that the model performs well on unseen data and meets the required performance criteria before it is deployed in a real-world application.