This is a documentation post-release of 0.6.0. The package is identical to 0.6.0, with no code change.
The "What to expect on imbalanced data" section of the README now reports a minority-class F1 column next to recall and precision, on the same three public datasets, so the recall gain and the precision cost can be read against the balance of the two. A new explanation states plainly why recall rises and precision falls under imbalanced handling, and how the F1 reading shows the trade came out ahead on all three datasets.
The numbers are unchanged from 0.6.0. The full change history is at https://github.com/MuditNautiyal-21/mudra-ml/blob/main/CHANGELOG.md