<ul><li>K-fold cross-validation is a widely used technique in the machine learning industry to evaluate model performance.</li><li>The technique involves dividing the data into k non-overlapping buckets, with each iteration using one bucket as the validation set and the remaining buckets as the training set.</li><li>Performance metrics are calculated for each iteration, and various approaches such as mean accuracy and cross-entropy can be used to select the best model.</li><li>K-fold cross-validation works for both classification and regression problems.</li></ul>

k-fold cross-validation in Machine Learning

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