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This paper constructs AdaBoost ensemble respectively with single attribute test (SAT) and decision tree (DT) for FDP, and empirically compares them with single DT and support vector machine (SVM).
Each internal node in a decision tree specifies an attribute test, and each leaf represents the predicted target value.
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Application examples are given to identify isotopes in various samples, typical for qualitative safeguards verification tasks – attribute tests.
This chapter describes the elements and tools used to establish acceptance criteria and an attribute testing strategy (ATS) for product variants and process related impurities.
A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy), each representing values for the attribute tested.
Since the approved shelf-life of this product is 3 years, the likelihood of any batch failing assay on stability is remote and Pfizer will use these data to help justify removal of assay as an attribute tested on stability.
The most attractive attribute tested was "providing results at no cost to research participants".
In both groups, providing results at no cost was the most attractive attribute (OR of finding the policy acceptable=1.82 in those willing to participate; OR=1.89 in those unwilling -p=0.25. Retunwilling -p=0.25r treatable diseases (OR=1.69 and 1.59, Returningely, p=0.09) was the second most imporesultsttribute tested).
Results obtained matched the predicted value where the optimum formulation received the best sensory scores for most attributes tested.
Experimental values were close to the predicted ones, with prediction errors below 10% for all attributes tested.
To classify an unknown instance, the tree is routed down according to the values of the attributes tested in successive nodes, and when a leaf is reached, the instance is classified according to the class assigned to the leaf.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com