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_posts/2024-12-05-dev-api.md

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In the past few releases, we've slowly introduced more functionalities under this
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umbrella. `__sklearn_clone__` and `__sklearn_is_fitted__` are two examples.
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In the latest release, at the time of writing this post, we focused on the testing
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infrasutructure and estimator tag system. Estimator tags used to be private, and we
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were not sure about their design. In the 1.6 release, new tags are introduced and
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using them looks like the following:
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In the 1.6 release, we focused on the testing infrastructure and estimator tag system.
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Estimator tags used to be private, and we were not sure about their design. In the 1.6
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release, new tags are introduced and using them looks like the following:
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```python
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from sklearn.base import BaseEstimator, ClassifierMixin
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```
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The new tags mostly follow the same structure as the old tags, but there are certain
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changes to them. The main change is that the old `_xfail_checks` is no more present
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changes to them. The main change is that the old `_xfail_checks` is no longer present
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in the new tags. That tag was used to tell the common testing tools about the tests
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which are known to fail and are to be skipped. That information is now directly passed
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to the test functionalities. The old way of skipping a test was the following:
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```
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While working on the testing infrastructure, we have also been working on improving our
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tests and that means in this release we had a particularly higher number of changes in
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their names and what they do. The changes should have made it easier for developers to
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fix issues with their estimators. Note that you can now pass `legacy=False` to both
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tests and that means in this release we had a particularly high number of changes in
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their names and what they do. The changes will make it easier for developers to fix
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issues with their estimators. Note that you can now pass `legacy=False` to both
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`check_estimator` and `parametrize_with_checks` to include only strictly API related
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tests.
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The above changes means developers need to updated their estimators and depending on
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The above changes mean developers need to update their estimators and depending on
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what they use, write scikit-learn version specific code to handle supporting multiple
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scikit-learn versions. To make that process easier, we've worked on a package called
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[`sklearn_compat`](https://github.com/sklearn-compat/sklearn-compat/). You can either

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