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Why do you need to have a hold out validation set?
This process is called hyperparameter tuning. Because you have adjusted your model using the validation dataset, it can no longer be used to create an unbiased evaluation of performance. This is why you also need to holdout a test dataset.
Do you still need a test set with cross validation?
Yes. As a rule, the test set should never be used to change your model (e.g., its hyperparameters). However, cross-validation can sometimes be used for purposes other than hyperparameter tuning, e.g. determining to what extent the train/test split impacts the results. Generally, yes.
What is cross validation and why would you prefer it to a hold out validation set?
Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data. That makes the hold-out method score dependent on how the data is split into train and test sets.
Is the 12 month holdout set good or bad?
On the other hand, if the model did poorly in forecasting the 12 month holdout set, this is a good indication that there is no ongoing systematic pattern in the data, or else that you completely misinterpreted the pattern with your model.
What happens if you do not use holdout sets?
If we did not use holdout sets, but instead only built a model based on the full history, we can be fooled into thinking our model is correct. This leads to unwarranted confidence in the accuracy of future forecasts – which can result in bad business decisions.
When to use hold out groups in a / B testing?
You can run holdouts for A/B tests and other marketing efforts, like drip email campaigns in which a percentage of users receives no email at all. After completion of a test and implementation of the winning version, the hold-out group remains for weeks, months, or, in rare cases, years.
Why are training, validation, and holdout sets important?
Why are Training, Validation, and Holdout Sets Important? Partitioning data into training, validation, and holdout sets allows you to develop highly accurate models that are relevant to data that you collect in the future, not just the data the model was trained on.