Predictive model validation is the process of confirming that a predictive model functions well with new, independent data. It verifies that the model is generalizable, accurate, reliable, and not overfitted to the training dataset.
The Process of Predictive Model Validation
Model validation generally happens in three main phases: in-sample validation, out-of-sample or holdout validation, and cross-validation. These techniques help evaluate if the model can generalize on unseen data, prevent overfitting and underfitting, and assist in hyperparameter tuning to achieve optimal performance.
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