Validation of the predictive model is critical in that it makes it reliable and trustworthy, allowing accurate decisions. Otherwise, it might generate flawed or biased results and mislead the decisions associated with serious consequences. For example, in finance, an unvalidated model would result in wrong risk assessments, while in healthcare, it might lead to misdiagnosis or treatment plans.
Validation strengthens the model by indicating where the model may be overfitting or underfitting. Overfitting means that the model performs well on the training data but poorly on new data, while underfitting implies that it does not capture the important trends in the data.
With thorough validation of a model, you can be certain that your performance will improve new, unseen data, making validation an integral part of the model development process.
