Cross-validation is a technique for evaluating the performance of a machine-learning model. It is an essential step in the model development process. When we cross-validate a model, this involves splitting the training dataset into multiple subsets, called folds. We then train the model on one subset and evaluate it on the remaining subsets. This is done for each possible combination of training and evaluation subsets. The performance of the model is averaged across all of the splits. This allows us to train a model and evaluate it on different data. The result is a more accurate estimate of the model performance. Evaluating a model on multiple folds can provide a more accurate estimate of the model’s performance than using a single split of the data. This can be useful for choosing the best model or for fine-tuning model hyperparameters. It also helps to avoid overfitting.
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