True or False: Defining test and validation samples is mandatory when creating a predictive model.

Defining test and validation samples when creating a predictive model is essential to ensure the model's effectiveness and reliability. Test and validation samples allow practitioners to evaluate the model's performance on unseen data, helping to ensure that the model generalizes well beyond the training data. This process helps to identify overfitting, where the model performs well on training data but poorly on new, unseen data.

Validation samples specifically allow for a check on the accuracy and applicability of the model before deploying it for use in decision-making. By using distinct subsets of data for training, testing, and validation, practitioners can achieve a clearer understanding of the predictive capabilities and adjustments needed for the model.

In summary, the requirement to define test and validation samples is a critical step in creating a robust predictive model, making it accurate to say that this step is indeed mandatory.