- Geoffrey I. Webb
Model evaluation is the process of assessing a property or properties of a model.
Motivation and Background
It is often valuable to assess the efficacy of a model that has been learned. Such assessment is frequently relative—an evaluation of which of several alternative models is best suited to a specific application.
Processes and Techniques
There are many metrics by which a model may be assessed. The relative importance of each metric varies from application to application.
The primary considerations often relate to predictive efficacy—how useful will the predictions be in the particular context it is to be deployed. Measures relating to predictive efficacy include Accuracy, Lift, Mean Absolute Error, Mean Squared Error, Negative Predictive Value, Positive Predictive Value, Precision, Recall, Sensitivity, Specificity, and various metrics based on ROC analysis.
Computational issues may also be important, such as a model’s size or its executio ...
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- Model Evaluation
- Reference Work Title
- Encyclopedia of Machine Learning
- p 683
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Springer Science+Business Media, LLC
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