inputs = [input for input in inputs if input.level == 'Senior']
assert 0.4 == partition
_
entropy
_
by(senior
_
inputs, 'lang', 'did
_
well')
assert 0.0 == partition
_
entropy
_
by(senior
_
inputs, 'tweets', 'did
_
well')
assert 0.95 < partition
_
entropy
_
by(senior
_
inputs, 'phd', 'did
_
well') < 0.96
앞의 결과에 따르면
tweets
의 엔트로피 값은
0
이므로 이에 따라 파티션을 나누
자. 이 경우에는
Senior
직급인 후보들이 트위터를 하면 항상
True
이고, 트위터
를 하지 않으면 항상
False
가 된다.
마지막으로
Junior
직급 후보자들에 대해서도 같은 작업을 반복해 보면
phd
를
기준으로 파티션을 나누게 되는데,
phd
의 경우 항상
False
를 반환한다는 사실을
발견할 수 있다.
그림
17
-
3
에서 최종 의사결정나무를 확인할 수 있다.
그림 17-3
17.5 ...
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