How do I prepare validation data for my model?

How do I prepare validation data for my model?

To prepare validation data, you should first split your dataset into training, validation, and test sets. Typically, you can use 70% for training, 15% for validation, and 15% for testing.

What criteria should I use to select the validation data?

The validation data should be representative of the overall dataset. Ensure it includes a variety of examples that reflect the different classes or outcomes you expect your model to predict.

How can I ensure my validation data is not biased?

To avoid bias, use stratified sampling to maintain the same distribution of classes in your validation set as in the training set. Also, review the data for any potential sources of bias.

Is there a specific size I should aim for with my validation set?

The size of your validation set can depend on the overall dataset size, but a common practice is to have at least a few hundred examples to ensure reliable performance evaluation.