2023.1:Waterfall Classification (Concept): Difference between revisions
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== ABOUT == | == ABOUT == | ||
Normally with classification, one can train a Document, set up a Positive Extractor for maximum accuracy, classify, get good results, and call it done. | Normally with classification, one can train a Document, set up a Positive Extractor for maximum accuracy, classify, get good results, and call it done. But what happens when high accuracy and specificity do more harm than good? | ||
== STARTING THE WATERFALL == | == STARTING THE WATERFALL == | ||
Revision as of 14:49, 9 April 2024
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WIP |
This article is a work-in-progress or created as a placeholder for testing purposes. This article is subject to change and/or expansion. It may be incomplete, inaccurate, or stop abruptly. This tag will be removed upon draft completion. |
Waterfall Classification is a classification concept in Grooper that manipulates the Positive Extractor property to prioritize training similarity in order to achieve a middle ground between high specificity and accuracy, and generality with minimal accuracy. This is helpful whenever Documents get misclassified, and simply retraining won't help.
ABOUT
Normally with classification, one can train a Document, set up a Positive Extractor for maximum accuracy, classify, get good results, and call it done. But what happens when high accuracy and specificity do more harm than good?