Cross-Validation
Cross-validation is a statistical technique for evaluating predictive models by repeatedly partitioning datasets into training and test folds; it reduces overfitting and provides more reliable performance estimates. Different strategies (k‑fold, stratified, time‑series split) address data characteristics and bias. Applying it requires choosing a validation strategy that matches data structure and business questions.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
Relations
Connected blocks
Directly linked content elements.