Building Robust Machine-Learned Models via Cross-Validation
Channel: Machine Learning Practice
Date: 2022-10-11

Statistically sound techniques for sampling data to make choices about hyper-parameters and to compare different machine learning model approaches.  

In order to make robust choices for our machine learned models, we must train and evaluate multiple copies of the same model with different data.  Ideally, the data used to train and evaluate the models are non-overlapping.  However, when available data sets are small, we are forced to re-use data across the different models.  In this video, I discuss a variety of approaches, including what is today called 'Cross-Validation', as well as old-style Cross-Validation (I refer to as 'Holistic Cross-Validation') and an orthogonalized version of Cross-Validation.