Using the first 20% of rows as the holdout sample can introduce bias if the data is sorted by a characteristic that changes over time. For instance, in a dataset of customer transactions spanning multiple years, this method may lead to the holdout sample primarily consisting of early adopters or those with specific spending patterns from the initial period, potentially skewing model evaluation and generalization. A random split ensures more representative training and holdout samples, aiding in robust model performance evaluation across various data distributions