Cross validation is a method to estimate the true error of a model by building models from subsets of training data and testing them on the remaining subsets. It provides a better estimate of how the model will generalize to new, unseen data compared to simply measuring error on the training data. Cross validation can also help evaluate which learning algorithm or parameter settings are best by comparing error rates from different options. Nested sub-processes in RapidMiner allow operators to contain additional processes that can be viewed by double clicking the icon.

1. RapidMinerNested Sub-processes

2. Cross ValidationCross validation is a method for estimating the true error of a model. When a model is built from training data, the error on the training data is a rather optimistic estimate of the error rates the model will achieve on unseen data.

3. Cross ValidationThe aim of building a model is usually to apply the model to new, unseen data--we expect the model to generalize to data other than the training data on which it was built. Thus, we would like to have some method for better approximating the error that might occur in general. Cross validation provides such a method.

4. Cross ValidationCross validation is also used to evaluate a model in deciding which algorithm to deploy for learning, when choosing from amongst a number of learning algorithms. It can also provide a guide as to the effect of parameter tuning in building a model from a specific algorithm.

5. Applying Naïve Bayes model to data sample

6. Validation

7. Nested Sub-processThe small icon on the lower right corner implies that the operator can be double clicked to view the nested sub-process.

8. Nested Sub-processDouble click on the operator to see the two sub-processes

9. Modeling the sub-processes

10. Results

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