Cross-validation (CV) splits the data into two portions, one for building the model and one for testing it.

A common practice is to repeat CV to get more precise estimates of the model's performance.

Besides increased computation time, what is the disadvantage of this approach?

Does it increase model bias or variance?

Cross-validation (CV) splits the data into two portions, one for building the model and one for testing it.

A common practice is to repeat CV to get more precise estimates of the model's performance. For example, instead of doing CV only once, it is repeated 100 times with random splits and the mean performance is reported.

Besides increased computation time, what is the disadvantage of this approach?

Does it increase model bias or variance?

Cross-validation (CV) splits the data into two portions, one for building a model and one for testing the model.

A common practice is to repeat cv to get more precise estimates of model performance.

Besides increased computation time, what is the disadvantage of this approach?

Does it increase model bias or variance?

Cross-validation (CV) splits the data into two portions, one for building the model and one for testing it.

A common practice is to repeat CV to get more precise estimates of the model's performance.

Besides increased computation time, what is the disadvantage of this approach?

Does it increase model bias or variance?