[Machine Learning] Decision Tree - ID3 algorithm (entropy + Information Gain)

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Describe ID3 algorithm with mathematical calculation.
The tutorial will cover Shannon Entropy and Information Gain.
Subtitle (English) is also available, please click 'CC' button for subtitle.

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Decision tree can't be explained more easily than this video series.

IcebergProject
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좋은 비디오 감사합니다 :) 질문이 있는데


마지막 슬라이드에서 E(winter family photo) - E(winter family photo, cartoon) / E(winter family photo) - E(winter family photo, winter) / E(winter family photo) - E(winter family photo, >1) 이렇게 3개를 비교하는게 맞나요? 전부 E(winter family photo) - E(winter family photo, cartoon) 로 되어있는것 같아서요...


3개를 비교해서 가장 Information Gain이 큰 'cartoon'이라는 attribute를 첫 번째 split attribute로 사용하는 건가요?

jonghyeonmin
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I was wondering about the values for the Entropy of cartoon pictures and such, the E([0+, 4-]) part. Is this just a value from a table, or is this a value that you have to calculate with the log function? And if it is calculated with the log function, what do you take for p(+) and p(-) as values?

JJDeath
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Why log base 2 is used in entropy? why not natural log or log base 10

shaz-z
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May i have your full video series on this topic please, thank you so much !

ttctoh
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Subtitle (English) is also available, please click 'CC' button for subtitle.

TheEasyoung