Why is it Important to Test Data Science Models on a Holdout Dataset before Deploying Them?

It’s important for analytics and data science teams to test their models on a holdout dataset before deploying them. There is a concept in data science called ‘overfitting’ a model, which is something that should be avoided; this happens when a model is trained too closely to a training dataset to the point where all the predictive power essentially goes away when it’s applied to a new dataset of observations the model has never seen before. Data scientists should test their model on a “holdout” dataset, a dataset that the model has never seen before, before deploying to ensure that its predictive power stands even when it’s applied to data it has never seen before. This essentially ensures that the model is not “overfit”.

Beyond simply setting aside a randomly selected % of the modeling dataset as a holdout to test a trained model, analysts and data scientists may be able to get an even better, less variable read on overfitting and model performance by using k-fold cross-validation. This essentially creates k (often set to 5 or 10) different holdout datasets to validate the model against, which reduces the variance of your holdout dataset model performance compared to just taking a single randomly selected cut. It’s also a good idea to train your model on several different time snapshots and test it on other time snapshots. This can ensure that your model is built and validated on trends and relationships that stand the test of time and don’t stand in one time period and not in another. This is particularly important when variable relationships and trends may be changing over time, such as in the stock market.

If your analytics and data science team would like to learn more advanced data science techniques like these to make their models more rigorous, predictive, and useful, Value Driven Analytics can provide engaging training on all kinds of advanced data science techniques. This helps ensure that analysts and data scientists at your organization build data science models in a robust manner, tailored to the business problem in a way that will drive value for your organization.