Enhancing decision tree accuracy and compactness with improved categorical split and sampling techniques
Decision tree is one of the most popular algorithms in the domain of explainable AI. From its structure, it is simple to induce a set of decision rules which are totally understandable for a human. That is why there is currently research on improving decision or mapping other models into a tree. Dec...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279454 |