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Validating data mining models

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Cross-validation is a standard dojigger in analytics and is an important feature in favour of helping you develop and fine-tune data mining models. You use cross-validation after you have created a mining structure and coordinated mining models to ascertain the validity of the model. Cross-validation has the following applications:. This portion describes how to consume the cross-validation features provided for data mining, and how to interpret the results of cross-validation in return either a single sitter or for multiple models based on a secluded data set.

Cross-validation consists of two phases, training and result generation. These phases include the following steps:. You specify the models you want to test.

This step is optional; you can assessment just the mining organize as well. Analysis Services returns a set of accuracy metrics for each fold in each shape, or for the input set as a as a rule. You can customize the way that cross-validation works to control the digit of cross-sections, the models that are tested, and the accuracy bar an eye to predictions.

If you serviceability the cross-validation stored procedures, you can also establish the data set that is used for validating the models.

Definition of Criteria for Validating Data Mining Models

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Do guys like a natural look? Model Validation and Verification of Data Mining from the Knowledge Workers. Productivity Approach. Asadallah Najafi. Islamic Azad University, Zanjan Branch, . However, the model checking of complex software systems suffers from the state space explosion problem. To handle this problem, data mining techniques may..

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Validating data mining models

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Validating data mining models

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