scikit-learn classification report's f1 accuracy?
I give you that this is a weird way of displaying the data, but the accuracy is the only field that don't fit the schema. For example:
              precision    recall  f1-score   support

           0       0.84      0.97      0.90    160319
           1       0.67      0.27      0.38     41010

As explained in How to interpret classification report of scikit-learn?, the precision, recall, f1-score and support are simply those metrics for both classes of your binary classification problem.
The second part of the table:
    accuracy                           0.82    201329   &lt;--- WHAT?
   macro avg       0.75      0.62      0.64    201329
weighted avg       0.80      0.82      0.79    201329

The accuracy is the overall accuracy of the model (note that accuracy is not a measure that is relative to a certain class, but a performance across all classes). The macro average for the precision and recall score is just the harmonic mean of the two classes. ie:
recall macro avg = (recall_class_1 + recall_class_0) / 2

The weighted average is just the average metric of the two classes weighted by the support/size-of-sample. ie:
recall weighted avg = (support_class_0 * recall_class_0 + support_class_1 * recall_class_1) / (support_class_0 + support_class_1)

This is a pretty long-winded way of saying the accuracy is just the overall accuracy. It has nothing to do with the column f1-score that it happens to be under.