Difference between revisions of "Multi-Objective Modeling"
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| + | == Motivation == | ||
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| + | == Using Multiple Measures == | ||
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| + | === Weighted Single Objective === | ||
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| + | === Multi-Objective === | ||
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| + | == Multi-output modeling == | ||
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the best alternative is ValidationSet, which by default behaves as cross validation in which only one fold is considered, reducing the cost of the measure by a factor 5. A second measure, called MinMax, is also activated by default, enabling the user to force the model to remain within certain bounds, to speed up the convergence. See below for more details in how to set these bounds. | the best alternative is ValidationSet, which by default behaves as cross validation in which only one fold is considered, reducing the cost of the measure by a factor 5. A second measure, called MinMax, is also activated by default, enabling the user to force the model to remain within certain bounds, to speed up the convergence. See below for more details in how to set these bounds. | ||
However, in certain situations it might be very effective to use different measures, or use multiple measures together. When multiple measures are used, an intelligent pareto-based method is used to decide which model is the best choice. Models that score high on a particular measure but low on another are not discarded immediately, but are given a chance to set things right in further iterations of the toolbox. This encourages variety in the models, while still ensuring convergence to the optimal accuracy for each measure. An often used combination is CrossValidation with the MinMax measure, to ensure that no poles are present in the model domain. | However, in certain situations it might be very effective to use different measures, or use multiple measures together. When multiple measures are used, an intelligent pareto-based method is used to decide which model is the best choice. Models that score high on a particular measure but low on another are not discarded immediately, but are given a chance to set things right in further iterations of the toolbox. This encourages variety in the models, while still ensuring convergence to the optimal accuracy for each measure. An often used combination is CrossValidation with the MinMax measure, to ensure that no poles are present in the model domain. | ||
Revision as of 00:49, 8 February 2009
THIS PAGE IS UNDER CONSTRUCTION
Motivation
Using Multiple Measures
Weighted Single Objective
Multi-Objective
Multi-output modeling
-- the best alternative is ValidationSet, which by default behaves as cross validation in which only one fold is considered, reducing the cost of the measure by a factor 5. A second measure, called MinMax, is also activated by default, enabling the user to force the model to remain within certain bounds, to speed up the convergence. See below for more details in how to set these bounds.
However, in certain situations it might be very effective to use different measures, or use multiple measures together. When multiple measures are used, an intelligent pareto-based method is used to decide which model is the best choice. Models that score high on a particular measure but low on another are not discarded immediately, but are given a chance to set things right in further iterations of the toolbox. This encourages variety in the models, while still ensuring convergence to the optimal accuracy for each measure. An often used combination is CrossValidation with the MinMax measure, to ensure that no poles are present in the model domain.
