Release highlights for 1.3

[jeremiedbb]

As usual, let's start with a few highlights and add more if needed in subsequent PRs. For now I put the TargetEncoder, HDBSCAN, and missing values support in trees.

Do not hesitate to edit.

Something we should add is some news about the metadata routing but I wasn't sure how to write that. @adrinjalali would you mind adding a section (here or in a separate PR) ?

[jeremiedbb]

Other features that I'm thinking about are

• Grouping infrequent categories in OrdinalEncoder
• Gamma deviance in HGBRegressor

Do you think they should end up in the highlights ?

[adrinjalali]

@jeremiedbb added metadata routing.

[lorentzenchr]

Other features that I'm thinking about are

• Grouping infrequent categories in OrdinalEncoder
• Gamma deviance in HGBRegressor

From a practitioner‘s perspective, pricing actuaries in particular, gamma deviance HGBT are a big deal. The PR itself was pretty small, but based on a pile of work with the common loss functions.

[jeremiedbb]

@lorentzenchr would you mind adding a small section for the Gamma deviance ?

[github-actions[bot]]

✔️ Linting Passed

All linting checks passed. Your pull request is in excellent shape! ☀️

_{Generated for commit: a7b9264. Link to the linter CI: here}

[lorentzenchr]

@lorentzenchr would you mind adding a small section for the Gamma deviance ?

Until when? Something like https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_23_0.html#generalized-linear-models-and-poisson-loss-for-gradient-boosting will do with the rng.poisson replaced by rng.gamma.

[lorentzenchr]

@jeremiedbb Thank you! 🚀