Models need to be validated:

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BlackTom AI · Accepted Answer

When evaluating models, one needs to consider how the model performs both on data it was trained on and on new data to gauge generalization.
Option 1: 'Both in-sample and out-of-sample' acknowledges that validation should cover the data the model learned from (in-sample) to check internal fit, as well as unseen data (out-of-sample) to assess predictive performance on new cases. This approach helps detect overfitting and ensures robustness across different datasets.
Option 2: 'In-Sample' focuses only on the data used to fit the model. While this can reveal how well the model captured the training data, it can dangerously overstate performance because it ignores how the model behaves on new data.
Option 3: 'Out-of-Sample' concentrates on data not used during training. This is essential for testing generalization, but relying solely on out-of-sample validation without considering in-sample fit can miss how well the model captures the training structure or may overlook overfitting indicators detectable in-sample.
In summary, validating a model typically benefits from a combined assessment across both in-sample and out-of-sample datasets, as each perspective highlights different aspects of model performance and generalization.

2025WI_MSDS_410-DL_SEC56 Quiz #3: Advanced Modeling Topics

Models need to be validated:

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