I ran my code and got the following result in python:
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
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.