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Cross Validation

Definition of Cross Validation

Cross Validation: Cross validation is a technique used in data science to improve the accuracy of predictions. It works by splitting the data into two sets, a training set and a testing set. The training set is used to train the model, while the testing set is used to evaluate the accuracy of the predictions.

What is Cross Validation used for?

Cross Validation is a technique that is used to assess the performance of a machine learning model on unseen data. It is a type of resampling method, meaning it uses different subsets of the available training data to train and test the model. Specifically, it involves partitioning the training set into k parts or folds, such that each fold is used only once for testing while the remaining k-1 folds are used for training. This helps to prevent overfitting and other issues that can arise when using traditional methods of validation such as using just one test set.

Cross Validation techniques vary from simple k-fold cross validation, which evenly splits the data into k sets, to leave one out cross validation (LOOCV) which uses only one sample from the training set at a time and repeats this process until all samples have been tested. Other variations include nested cross validation and Monte Carlo cross validation. The main advantage of Cross Validation is its ability to provide an accurate estimate of model performance on unseen data. By taking multiple samples from the available training set and testing them independently it allows us to get an unbiased estimate of how well our model will perform on unseen input data sets. Additionally, it can be used in combination with grid search or other hyperparameter optimization algorithms in order to select the optimal parameters for a given dataset.

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