================================================================================
ASPEKT               | ACC      | F1-MACRO   | WYNIK
================================================================================
overall_experience   | 0.8406   | 0.7705     | (Neg/None/Pos)
location             | 0.9468   | 0.8534     | (Neg/None/Pos)
cleanliness          | 0.9548   | 0.9243     | (Neg/None/Pos)
bed_comfort          | 0.8925   | 0.8122     | (Neg/None/Pos)
size_space           | 0.8034   | 0.7348     | (Neg/None/Pos)
kitchen              | 0.8914   | 0.8038     | (Neg/None/Pos)
wifi                 | 0.9888   | 0.8632     | (Neg/None/Pos)
noise                | 0.9588   | 0.9216     | (Neg/None/Pos)
safety               | 0.9018   | 0.6855     | (Neg/None/Pos)
host_contact         | 0.9553   | 0.9265     | (Neg/None/Pos)
value_for_money      | 0.9350   | 0.7852     | (Neg/None/Pos)
food                 | 0.8778   | 0.6203     | (Neg/None/Pos)
--------------------------------------------------------------------------------

>>> SZCZEGÓŁY DLA overall_experience
              precision    recall  f1-score   support

   Negatywny       0.89      0.86      0.87       423
     Brak/OK       0.39      0.83      0.53       403
   Pozytywny       0.99      0.84      0.91      2913

    accuracy                           0.84      3739
   macro avg       0.75      0.84      0.77      3739
weighted avg       0.91      0.84      0.86      3739


>>> SZCZEGÓŁY DLA location
              precision    recall  f1-score   support

   Negatywny       0.49      0.96      0.65        92
     Brak/OK       0.96      0.92      0.94      1301
   Pozytywny       0.97      0.96      0.97      2346

    accuracy                           0.95      3739
   macro avg       0.81      0.95      0.85      3739
weighted avg       0.96      0.95      0.95      3739


>>> SZCZEGÓŁY DLA cleanliness
              precision    recall  f1-score   support

   Negatywny       0.79      0.92      0.85       302
     Brak/OK       0.98      0.96      0.97      2392
   Pozytywny       0.96      0.95      0.96      1045

    accuracy                           0.95      3739
   macro avg       0.91      0.94      0.92      3739
weighted avg       0.96      0.95      0.96      3739


>>> SZCZEGÓŁY DLA bed_comfort
              precision    recall  f1-score   support

   Negatywny       0.63      0.95      0.76       171
     Brak/OK       0.99      0.88      0.93      3102
   Pozytywny       0.61      0.95      0.74       466

    accuracy                           0.89      3739
   macro avg       0.74      0.93      0.81      3739
weighted avg       0.93      0.89      0.90      3739


>>> SZCZEGÓŁY DLA size_space
              precision    recall  f1-score   support

   Negatywny       0.54      0.94      0.68       258
     Brak/OK       0.94      0.80      0.86      2801
   Pozytywny       0.58      0.76      0.66       680

    accuracy                           0.80      3739
   macro avg       0.68      0.83      0.73      3739
weighted avg       0.85      0.80      0.81      3739


>>> SZCZEGÓŁY DLA kitchen
              precision    recall  f1-score   support

   Negatywny       0.61      0.96      0.74       164
     Brak/OK       0.99      0.88      0.93      3109
   Pozytywny       0.61      0.92      0.73       466

    accuracy                           0.89      3739
   macro avg       0.74      0.92      0.80      3739
weighted avg       0.93      0.89      0.90      3739


>>> SZCZEGÓŁY DLA wifi
              precision    recall  f1-score   support

   Negatywny       0.86      0.98      0.92        55
     Brak/OK       1.00      0.99      0.99      3648
   Pozytywny       0.53      0.94      0.68        36

    accuracy                           0.99      3739
   macro avg       0.80      0.97      0.86      3739
weighted avg       0.99      0.99      0.99      3739


>>> SZCZEGÓŁY DLA noise
              precision    recall  f1-score   support

   Negatywny       0.81      0.92      0.86       305
     Brak/OK       0.99      0.97      0.98      2881
   Pozytywny       0.92      0.94      0.93       553

    accuracy                           0.96      3739
   macro avg       0.90      0.94      0.92      3739
weighted avg       0.96      0.96      0.96      3739


>>> SZCZEGÓŁY DLA safety
              precision    recall  f1-score   support

   Negatywny       0.45      0.92      0.60       111
     Brak/OK       0.99      0.90      0.95      3490
   Pozytywny       0.36      0.88      0.51       138

    accuracy                           0.90      3739
   macro avg       0.60      0.90      0.69      3739
weighted avg       0.95      0.90      0.92      3739


>>> SZCZEGÓŁY DLA host_contact
              precision    recall  f1-score   support

   Negatywny       0.77      0.97      0.86       203
     Brak/OK       0.97      0.93      0.95      1568
   Pozytywny       0.97      0.97      0.97      1968

    accuracy                           0.96      3739
   macro avg       0.90      0.96      0.93      3739
weighted avg       0.96      0.96      0.96      3739


>>> SZCZEGÓŁY DLA value_for_money
              precision    recall  f1-score   support

   Negatywny       0.43      0.92      0.59       106
     Brak/OK       0.99      0.94      0.96      3357
   Pozytywny       0.76      0.86      0.81       276

    accuracy                           0.94      3739
   macro avg       0.73      0.90      0.79      3739
weighted avg       0.95      0.94      0.94      3739


>>> SZCZEGÓŁY DLA food
              precision    recall  f1-score   support

   Negatywny       0.21      0.95      0.35        19
     Brak/OK       0.99      0.87      0.93      3424
   Pozytywny       0.42      0.93      0.58       296

    accuracy                           0.88      3739
   macro avg       0.54      0.92      0.62      3739
weighted avg       0.94      0.88      0.90      3739

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Paper for bziemba/review-aspects-data-balanced-medium

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 25