Validation
Validation is the process of evaluating a machine learning model's performance on an independent dataset. It is a crucial step in the model development process as it helps to ensure that the model is not overfitting to the training data and can generalize well to new, unseen data. Validation can be done using various techniques such as holdout validation, k-fold cross-validation, and leave-one-out cross-validation. In holdout validation, the dataset is split into training and validation sets, and the model is trained on the training set and evaluated on the validation set. In k-fold cross-validation, the dataset is divided into k subsets, and the model is trained and evaluated k times, with each subset used once for validation and the remaining subsets used for training. In leave-one-out cross-validation, the model is trained and evaluated on each sample in the dataset, one at a time, with the remaining samples used for training.
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