Covariate shift refers to a change in the distribution of input data between the training phase and the testing phase of a machine learning model. This can lead to performance issues, as the model may not generalize well if the conditions under which it was trained differ significantly from those during inference. Understanding this shift is crucial when using methods like learning from demonstration, as it can impact how well the learned behaviors are applied in new scenarios.
congrats on reading the definition of Covariate Shift. now let's actually learn it.