First, please forgive my ignorance; I am a newbie but dedicated to learning more.
Example: I have a using a random forest classifier to predict a binary outcome. The binary outcome equals 1 if people in the dataset ever experience a specific health condition and equals 0 if they don't experience the health condition.
I have tuned and run the model using the scikit-learn and related packages in a Python coding environment. I then produce the following classification report pasted below. I understand how to interpret accuracy, precision, recall (sensitivity), etc. But I am confused about the following...
Question: I want to report the most appropriate performance metrics from the classification_report -- whether just the metrics for the "= 1" class or the macro averages for both classes (ie, 0 and 1). So, if I have a binary outcome, and = 1 (ie, Yes) is the outcome of interest for prediction purposes, would it be more appropriate to report and discuss the precision, recall, F1 for the "= 1" class? Or would it instead be more appropriate and useful to report macro (or weighted) average metrics for precision, recall, F1, taking into account the metrics for both classes? For instance, I understand that the "= 0" class precision and recall metrics are better in this example, and those would affect the macro/weighted average metrics in the classification report. But I am not quite sure if it would be appropriate (and useful) to report the averages across both classes (0 and 1) in a table, rather than just the "= 1" class performance metrics. For example, macro average precision = 0.715 instead of 0.494 for just the = 1 class.
Thank you very much in advance for your time, understanding, and help!