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파이썬 라이브러리를 활용한 머신러닝(번역개정판)
(
https
://
goo
.
gl
/
C7kSgU
)
metrics
.
scorer
모듈의
SCORERS
딕셔너리를 봐도 됩니다.
In [77]:
from sklearn.metrics.scorer import SCORERS
print("가능한 평가 방식:\n", sorted(SCORERS.keys()))
Out [77]:
가능한 평가 방식:
['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision',
'balanced_accuracy', 'brier_score_loss', 'completeness_score', 'explained_variance',
'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score',
'homogeneity_score', 'mutual_info_score', 'neg_log_loss', 'neg_mean_absolute_error',
'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error',
'normalized_mutual_info_score', ...