Which concept involves creating a balance for a model to prevent errors during predictions?

The concept that involves creating a balance for a model to prevent errors during predictions is regularization. Regularization is a technique used in machine learning and statistical modeling to reduce the risk of overfitting, which occurs when a model learns to perform very well on training data but fails to generalize effectively to unseen data.

By introducing a penalty for more complex models, regularization encourages the model to remain simpler and more general. This helps in maintaining a balance between accuracy on the training set and performance on validation or test sets. The use of regularization techniques, such as L1 (Lasso) or L2 (Ridge) regularization, makes adjustments to the cost function of the model by including these penalties, thereby discouraging excessive complexity that could lead to poor prediction results in real-world scenarios.

In contrast, the other concepts, while related to data preparation and model evaluation, do not directly pertain to the balance meant to prevent errors during predictions. Overfitting describes a phenomenon rather than a method to mitigate it, normalization and standardization relate more to data scaling techniques rather than model performance balancing.