How to read multilabel classification report

username: Makos63
cooked: <p>Hello, I have trained German BERT model and ran it against my test set. I know that my data is imbalanced, but I will leave it be for now.<br>
The point of my experiment was to see what can I get out of my model with data at hand. However, I am still quite new in this topic and would very appreciate if someone could answer some of my questions. The table below show some of my classification report.</p>
<p>Question 1<br>
If I understand correctly, precision means, how many of recognized classes are actually correct and recall says how many samples of this class<br>
has been found. So when we see class fonds you could say, model find quite a lot of actual fonds samples, yet at the same time many other samples are falsely classified as fonds. Because precision is not that high. So others classes are suffering because<br>
Fonds ‘steal’ samples from other classes, is it correct?</p>
<p>Question 2<br>
I looked around but could not find the answer, is there a threshold when one would say that model is good enough? let’s say everything above 70% is pleasing, or is it necessary to check in inference if model is behaving correctly?</p>
<p>Question 3<br>
In one case, my test data in auslandskrankenversicherung class has 0 samples. Thus, precision as well as recall are zero, so these have way more impact on macro avg.<br>
When I got it right, macro focus on the avg performance of each class and micro takes total samples in the consideration. Thus, micro would be the optimistic assumption on overall performance and macro the pessimistic one.</p>
<pre><code class="lang-auto">									 precision    recall  f1-score   support

                           none_cat       0.94      0.85      0.89       722
                              fonds       0.65      0.87      0.75        39
                       kontaktdaten       0.60      0.60      0.60         5
                 lebensversicherung       0.96      0.94      0.95       236
                      kundenberater       0.92      0.98      0.95       146
                     altersvorsorge       0.94      0.90      0.92       129
                          anschrift       0.73      1.00      0.84         8
                          allgemein       0.52      0.87      0.65       118
                          ...			  ...       ...       ...        ...
        auslandskrankenversicherung       0.00      0.00      0.00         0
                           none_int       0.88      0.88      0.88       901
                           widerruf       1.00      0.60      0.75         5
                          steuer-id       1.00      1.00      1.00         2
                            dynamik       1.00      1.00      1.00        10
   		          vertragsauskunfte       0.71      0.73      0.72       245
     		              kundigung       0.92      0.96      0.94       102


                          micro avg       0.86      0.87      0.86      3416
                          macro avg       0.79      0.79      0.78      3416
                       weighted avg       0.87      0.87      0.87      3416
                        samples avg       0.86      0.87      0.87      3416
</code></pre>
<p>By looking at the micro and macro avg that model is not that bad?</p>