Validated validating c

The fitting process optimizes the model parameters to make the model fit the training data as well as possible.

If we then take an independent sample of validation data from the same population as the training data, it will generally turn out that the model does not fit the validation data as well as it fits the training data.

The process looks similar to jackknife; however, with cross-validation you compute a statistic on the left-out sample(s), while with jackknifing you compute a statistic from the kept samples only.

LOO cross-validation does not have the same problem of excessive computation time as general Lp O cross-validation because .

is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.

partitioning the data set into two sets of 70% for training and 30% for test) is that there is not enough data available to partition it into separate training and test sets without losing significant modelling or testing capability.

This is called overfitting, and is particularly likely to happen when the size of the training data set is small, or when the number of parameters in the model is large.

Cross-validation is a way to predict the fit of a model to a hypothetical validation set when an explicit validation set is not available.

Linear regression provides a simple illustration of overfitting (the expected value is taken over the distribution of training sets).

Thus if we fit the model and compute the MSE on the training set, we will get an optimistically biased assessment of how well the model will fit an independent data set.

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Non-exhaustive cross validation methods do not compute all ways of splitting the original sample.

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