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Thanks to the advantages of the empirical over the laboratory models, a decision was made to introduce a model for prediction of the drilling rate in the Ahvaz oil field area based on the empirical data.
The C5.0 node can produce two kinds of models: a decision tree which is a straightforward description of the splits found by the algorithm; and a rule set which tries to make predictions for individual records.
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By a linear discriminative is one way to model a decision.
A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e., operators, regulators and service engineers) are involved.
When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker's uncertainty about the domain and utilities for representing preferences.
The product of this phase was the final model: a decision tree loaded with the probability values and the costs associated with each path.
Based on this model, a decision algorithm was developed for the management of such a population.
In this model, a decision variable accumulates information over time about which action to select.
Starting from the TLV model, a decision analytic model was developed to simulate the management of Italian patients with MDD over a time horizon of 12 months.
Additionally, so as to estimate a clinically meaningful model, a decision was made to force all five established prognostic factors into the model.
When selecting the best-fitting model, a decision has to be made both on the number of lower-level and of higher-level LCs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com