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Cross-Validation
A statistical method used to estimate the performance of a model by dividing data into subsets, training the model on some subsets, and validating it on the remaining subsets.
Implications
A statistical technique used to assess the performance of a predictive model by partitioning data into subsets, training the model on one subset, and validating it on another, often used to ensure model accuracy and prevent overfitting.
Example
Example: A data scientist uses cross-validation to evaluate the accuracy of a machine learning model predicting customer churn, ensuring it performs well on unseen data.
Related Terms
Different from simple train-test splits, cross-validation typically involves multiple iterations, providing a more robust assessment of model performance.
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