Description: Weighted Cross-Validation is a cross-validation method that takes into account the weights of instances in a dataset. Unlike standard cross-validation, which divides the dataset into folds uniformly, weighted cross-validation assigns different importances to instances, allowing for a more accurate assessment of model performance, especially in situations where data is imbalanced. This approach is particularly useful in supervised learning, where classes may not be equally represented. By weighting instances, the aim is to mitigate the bias that can arise from the over-representation of certain classes, ensuring that the model learns effectively from all instances. Weighted cross-validation is commonly implemented in various machine learning algorithms, where model accuracy is crucial. This method not only enhances model robustness but also provides a better estimate of its performance on unseen data, which is essential for generalization in real-world applications.
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