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Model Performance Evaluation

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Data Mining with Rattle and R

Part of the book series: Use R ((USE R))

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Abstract

The preceding chapters presented a number of algorithms for building descriptive and predictive models. Before we can identify the best from amongst the different models, we must evaluate the performance of the model. This will allow us to understand what to expect when we use the model to score new observations. It can also help identify whether we have made any mistakes in our choice of input variables. A common error is to include as an input variable a variable that directly relates to the outcome (like the amount of rain tomorrow when we are predicting whether it will rain tomorrow). Consequently, this input variable is exceptionally good at predicting the target.

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Correspondence to Graham Williams .

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© 2011 Springer Science+Business Media, LLC

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Williams, G. (2011). Model Performance Evaluation. In: Data Mining with Rattle and R. Use R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9890-3_15

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