K-fold Cross-validation

K-fold cross-validation is a technique used in machine learning to evaluate the performance of a model. It involves dividing the dataset into k equal parts or folds. The model is trained on k-1 folds and tested on the remaining fold. This process is repeated k times, with each fold being used as the test set exactly once. The results are then averaged to give an overall performance metric for the model. K-fold cross-validation is useful for assessing the performance of a model on a limited dataset, as it allows for a more accurate estimate of the model's performance than a simple train-test split. It also helps to reduce the risk of overfitting, as the model is tested on multiple independent datasets.
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