What is a potential negative effect of using excessively large datasets?

The potential negative effect of using excessively large datasets is that they can increase training time and computing power requirements. Large datasets often require significant computational resources to process, analyze, and train machine learning models. The increase in the amount of data being processed can lead to longer training times, requiring more powerful hardware or more time to complete the model training.

Additionally, as the dataset size grows, the complexity of the computations usually escalates, which can lead to inefficiencies and potentially diminish returns on performance improvements. Allocating more resources to accommodate the demands of extensive datasets can also incur higher costs, making it less efficient for organizations with limited infrastructure.

This nuance highlights the importance of balancing dataset size with the available computational resources and the model's complexity to achieve the desired outcomes effectively.