What are common mistakes in validation data selection?

What are common mistakes in validation data selection?

Some common mistakes include using biased data, not having enough data, and failing to represent the target population.

How can I ensure my validation data is representative?

You can ensure representativeness by stratifying your data, using random sampling, and including diverse data sources.

What impact does biased validation data have on model performance?

Biased validation data can lead to overfitting, poor generalization, and ultimately, a model that performs poorly in real-world scenarios.

Can you give examples of biased data?

Sure! Examples include data that over-represents certain demographics or scenarios, like only using data from one geographic area.