Understanding the results of this ML project

@pudkeaayush wrote:

I ran my code and got the following result in python:
1. For Naive Bayes

                  precision    recall  f1-score   support

Less than 50k       0.98      0.85      0.91     93576
More than 50k       0.24      0.72      0.36      6186

avg / total         0.93      0.84      0.88     99762

1. For XGBoost

                   precision    recall  f1-score   support
   
   Less than 50k       0.96      0.99      0.98     93576
   More than 50k       0.77      0.37      0.50      6186
   
   avg / total         0.95      0.95      0.95     99762

As can be seen the precision value of miniority class has increased to 0.77 which is what we wanted in this project I guess. What does the recall and F1-score indicate in this case in terms of predicting less than or more than 50k. I read the theoritical definition of the same from wiki but could not relate it specifically to this project. Can you please explain what each entry in this table means?
I got this result by using metrics.classification_report in Python.

Regards
Raju

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