Keyword Analysis & Research: data mining model selection and validation

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Frequently Asked Questions

How do you validate data mining solutions?

Analysis Services supports multiple approaches to validation of data mining solutions, supporting all phases of the data mining test methodology. Partitioning data into testing and training sets. Filtering models to train and test different combinations of the same source data.

How do you test a data mining model?

Methods for Testing and Validation of Data Mining Models Use various measures of statistical validity to determine whether there are problems in the data or in the model. Separate the data into training and testing sets to test the accuracy of predictions. Ask business experts to review the results ...

Why is it important to validate mining models?

It is important that you validate your mining models by understanding their quality and characteristics before you deploy them into a production environment. This section introduces some basic concepts related to model quality, and describes the strategies for model validation that are provided in Microsoft Analysis Services.

What is the difference between theory and data mining?

Instead of data mining, use theory to guide you while fitting models and evaluating results. This approach reduces the number of models that you need to fit. Additionally, you can evaluate the model’s properties using subject-area considerations.

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