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Cross-Validation

Cross-Validation

Cross-Validation:

Cross-validation is a general computer-intensive approach used in estimating the accuracy of statistical models. The idea of cross-validation is to split the data into N subsets, to put one subset aside, to estimate parameters of the model from the remaining N-1 subsets, and to use the retained subset to estimate the error of the model. Such a process is repeated N times - with each of the N subsets being used as the validation set . Then the values of the errors obtained in such N steps are combined to provide the final estimate of the model error.

The cross-validation is used in various classification and prediction procedures, such as regression analysis , discriminant analysis , neural networks and classification and regression trees (CART) .

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Predictive Analytics 1 – Machine Learning Tools
This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.
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