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Can anyone tell me what exactly a cross-validation analysis gives as result? Is it just the average accuracy or does it give any model with parameters tuned?

Because, I heard somewhere that cross-validation is used for parameter tuning.

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Cross-validation gives a measure of out-of-sample accuracy by averaging over several random partitions of the data into training and test samples. It is often used for parameter tuning by doing cross-validation for several (or many) possible values of a parameter and choosing the parameter value that gives the lowest cross-validation average error.

So the process itself doesn't give you a model or parameter estimates, but you can use it to help choose between alternatives.

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To add to Jonathan's answer.

However, if you use cross validation for parameter tuning, the out-samples in fact become part of your model. So you need another independent sample to correctly measure the final model's performance.

Employed for measuring model performance, cross validation can measure more than just the average accuracy:
A second thing you can measure with cross validation is the model stability with respect to changing training data: cross validation builds lots of "surrogate" models that are trained with slightly differing training sets. If the models are stable, all these surrogate models are equivalent, if training is unstable, the surrogate models vary a lot. You can quantify this "varies a lot" e.g. as variance of predictions of different surrogate models for the same sample (in iterated/repeated cross validation) or e.g. as variance of the surrogate models' parameters.

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