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2026-10-20, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20260379 Question.1
Questioner: abilaaniloshan@gmail.com 2026-02-09 오후 10:29:05 |
ICACT20260379 Answer.1
Answer by Auhor ida@niad.ac.jp 2026-02-09 오후 10:29:05
Chrome  Click!! |
| Why did you use normalized spectral entropy in addition to the f1-score for evaluating classification performance? |
Generally, when entropy is low, the data is biased in a small number of directions, and if the data items related to that direction are selected, classification becomes easier. |
ICACT20260379 Question.2
Questioner: abilaaniloshan@gmail.com 2026-02-09 오후 10:27:40 |
ICACT20260379 Answer.2
Answer by Auhor ida@niad.ac.jp 2026-02-09 오후 10:27:40
Chrome Click!! |
| Your results show that adding more indicators improves accuracy, but entropy does not change significantly. How should practitioners interpret this? |
As for entropy, low entropy does not necessarily mean high accuracy. it is possible for entropy to be relatively high for classification to be based on multiple criteria. |
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