What is holdout data?
A portion of data set aside for testing a machine learning model's performance
holdout data explained in plain English
Holdout data is a subset of data that is not used to train a machine learning model, but is instead used to evaluate its performance after training is complete. This helps to ensure the model is not overfitting to the training data and can generalize well to new, unseen data.
Analogy
Think of holdout data like a final exam in school. Just as a student's performance on a final exam can show how well they've learned the material, holdout data shows how well a machine learning model has learned from its training data.
Example
A company building a model to predict customer churn might use 80% of their customer data to train the model and the remaining 20% as holdout data to test its performance.
How is holdout data used?
Holdout data is typically used after a machine learning model has been trained, to evaluate its performance and make any necessary adjustments before deploying the model in a real-world setting.
Common misconceptions about holdout data
One common misconception is that holdout data is the same as validation data, but while both are used for model evaluation, validation data is typically used during the training process to fine-tune the model, whereas holdout data is used after training is complete.
History
The concept of holdout data has been around since the early days of machine learning, as a way to evaluate the performance of models and prevent overfitting.
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