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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/events | https://github.com/scikit-learn/scikit-learn/pull/32953 | 3,763,530,990 | PR_kwDOAAzd1s66o0pN | 32,953 | feat: add preserve_output_dtypes config | {
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- add preserve_output_dtypes to global config and wrappers
- ensure pandas outputs can retain input dtypes when untouched
- cover dtype preservation, skip scenarios, and duplicate column handling
## Testing
- pytest sklearn/utils/tests/test_set_output.py -q
## Issues
- #52 | {
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Net benefit is defined as a decision-analytic measure that quantifies the clinical value of a prediction model, test, or marker by putting its benefits and harms on the same scale. This is done by specifying an exchange rate—a clinical judgment about how much a harm (e.g., ... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/events | https://github.com/scikit-learn/scikit-learn/pull/32951 | 3,763,253,579 | PR_kwDOAAzd1s66n6BQ | 32,951 | DOC: clarify GridSearchCV verbose behaviour for higher values | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2025-12-26T10:48:24 | 2025-12-26T15:13:57 | 2025-12-26T14:55:58 | null | CONTRIBUTOR | null | null | null | null | This PR clarifies the documentation of the `verbose` parameter in `GridSearchCV`.
The current documentation does not reflect the actual behavior for larger
values (e.g. >3 or >10). This update explains that higher values increase
verbosity and that the exact output depends on the execution backend.
Closes #3294... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/events | https://github.com/scikit-learn/scikit-learn/issues/32950 | 3,762,599,696 | I_kwDOAAzd1s7gRLcQ | 32,950 | AttributeError: cross_val_predict(method='predict_proba') with MultiOutputClassifier (swev-id: scikit-learn__scikit-learn-14629) | {
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} | ## User Request
AttributeError with `cross_val_predict(method='predict_proba')` when using `MultiOutputClassifier`.
Description:
Using `cross_val_predict(method='predict_proba')` with `MultiOutputClassifier` triggers `AttributeError` because `estimator.classes_` is accessed; `MultiOutputClassifier` exposes `classes_`... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/events | https://github.com/scikit-learn/scikit-learn/issues/32949 | 3,762,549,153 | I_kwDOAAzd1s7gQ_Gh | 32,949 | BUG: HuberRegressor fails with boolean feature matrix | {
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`HuberRegressor.fit` raises a `TypeError` when the feature matrix is boolean because the internal gradient helper negates a boolean mask with the `-` operator. In NumPy >=1.13 this triggers `TypeError: The numpy boolean negative, the '-' operator, is not supported, use the '~' operator or the logical_not fu... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/events | https://github.com/scikit-learn/scikit-learn/pull/32948 | 3,762,419,891 | PR_kwDOAAzd1s66lRpT | 32,948 | Fix multinomial CV scoring uses softmax | {
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- ensure _log_reg_scoring_path propagates solver and multi_class to the temporary LogisticRegression scorer
- add regression tests covering multinomial neg_log_loss scoring for scoring path and LogisticRegressionCV
## Testing
- blocked: python setup.py develop (fails linking legacy ATLAS cblas headers with ... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/events | https://github.com/scikit-learn/scikit-learn/issues/32947 | 3,762,410,863 | I_kwDOAAzd1s7gQdVv | 32,947 | clone: treat class-valued parameters as non-estimators (avoid TypeError) [swev-id: scikit-learn__scikit-learn-12585] | {
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Clone fails for parameters that are estimator types.
Key: clone should not error when a parameter is a class; adjust clone to treat classes as non-estimators (e.g., check isinstance(estimator, type) or ismethod(get_params)).
## Research specification
- Location: sklearn/base.py, function `clone`.
- Pr... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/events | https://github.com/scikit-learn/scikit-learn/issues/32946 | 3,762,349,288 | I_kwDOAAzd1s7gQOTo | 32,946 | GridSearchCV.verbose documentation does not match actual behavior (values >3, >10) | {
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} | ### Describe the issue linked to the documentation
[`GridSearchCV` docs ](https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html) state
> **verbose** int
> Controls the verbosity: the higher, the more messages.
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Currently, in Ridge regression, the current_alpha used in the SAG and SAGA solvers does not correctly account for the sum of sample weights when provided. This can lead to incorrect regularization strength compared to other solvers like cholesky or lsqr.
Changes
This PR updates the internal _ridge_regressi... | {
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
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#### AI usage disclosure
I found the problem when prototyping a test refactoring with copilot but the final content of this PR was manually edited in the end. | {
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When sample_weight zeros out an entire class, weighted_class_counts becomes zero for that class, causing division by zero and producing inf values that silently propagate through the training pipeline.
This fix adds explicit validation to detect classes with zero total sample_weight and raises a clear ValueError i... | null | {
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This PR removes the `<1.9.0` version constraint for `numpydoc` in the CI environment configuration.
### Rationale
The constraint was originally added due to an issue in `numpydoc` (issue #638). Since that issue has been resolved and a new version has been released, we can now allow the CI to use the l... | {
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Hi @lesteve ,
This PR fixes the flaky failure in the min-dependencies OpenBLAS CI job for test_predict_joint_proba (#32906 and #29909). With the default rtol and atol, I hit intermittent assert_allclose failures.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32932 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32932/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32932/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32932/events | https://github.com/scikit-learn/scikit-learn/pull/32932 | 3,756,648,475 | PR_kwDOAAzd1s66SnuZ | 32,932 | FIX Avoid LogisticRegression spurious warning with `C=np.inf` | {
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Mentioned here: https://github.com/scikit-learn/scikit-learn/pull/32733#discussion_r2540045497
#### What does this implement/fix? Explain your changes.
Adds a glossary entry for one-vs-one and one-vs-rest.
Happy to amend wording!
I have not added links from our docs to this entry,... | {
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} | Several of our supervised clustering metrics specify/suggest (??) that the array input should be of type `int` or `integral`, e.g.:
`pair_confusion_matrix`:
https://github.com/scikit-learn/scikit-learn/blob/b0ba8b029c298e0cc545206d2df4757be0ec2ac2/sklearn/metrics/cluster/_supervised.py#L212-L216
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/events | https://github.com/scikit-learn/scikit-learn/issues/32929 | 3,755,413,093 | I_kwDOAAzd1s7f1w5l | 32,929 | [Solved] Picard library modifies sklearn FastICA causing it to reject a default value for the `fun` parameter | {
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I wrote some code that did not specify a `fun` parameter to FastICA and somehow this default value was rejected
```
File "/home/louis/dev/arxiv_explorer/benchmark_ica_impls.py", line 84, in <module> ... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/events | https://github.com/scikit-learn/scikit-learn/pull/32928 | 3,754,464,777 | PR_kwDOAAzd1s66LXuc | 32,928 | FIX html_repr: remove redundant text dropdown (Fixes #32146) | {
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Replace estimator_label_details with an empty string in the HTML output.
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I am encountering a catch-22 situation with `LogisticRegression` warnings in scikit-learn 1.8.0.
When I use `LogisticRegression(penalty=None)`, I get a `FutureWarning` advising me to use `C=np.inf` instead. However, when I follow this advice and use `LogisticRegression(C=np.inf)` (leaving `penalt... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/events | https://github.com/scikit-learn/scikit-learn/issues/32924 | 3,746,197,390 | I_kwDOAAzd1s7fSm-O | 32,924 | Array API spec for `unique_values` does not specify order of unique elements | {
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I don't think we need to do anything about this, (and indeed all our tests pass), I just wanted to document this for posterity, in case we encounter any problems later.
Array API spec for `unique_values` does not specify what the order of unique elements should be.
From https://data-api... | null | {
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#### Reference Issues/PRs
#### What does this implement/fix? Explain your changes.
As discussed in https://github.com/scikit-learn/scikit-learn/pull/32422/changes#r2575666004 , add array API handling to the case when `pos_label=None` and `y_true` is not string.
Also adds test.
#### AI usage disclosure
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Updated LinearRegression documentation to clarify that the model includes an intercept term, addressing the confusion noted in the issue.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/events | https://github.com/scikit-learn/scikit-learn/issues/32919 | 3,739,834,889 | I_kwDOAAzd1s7e6VoJ | 32,919 | Kernel crash on BayesianGaussianMixture | {
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I learn BayesianGaussianMixture right now and get kernel crash on sklearn documentation example.
The error is 100% reproducible and wont go away after OS restrat, kernel restrat. I even try to read Goodfellow, no effect.
Initially i got the error on version chosen by `conda install scikit-learn` ... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32918 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32918/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32918/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32918/events | https://github.com/scikit-learn/scikit-learn/pull/32918 | 3,739,259,930 | PR_kwDOAAzd1s65Y902 | 32,918 | DOC Fix response values shape | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32917 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32917/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32917/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32917/events | https://github.com/scikit-learn/scikit-learn/pull/32917 | 3,739,140,668 | PR_kwDOAAzd1s65YkBT | 32,917 | MNT cleanup old numpy workaround in metrics functions | {
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discovered while working on #32909
#### What does this implement/fix? Explain your changes.
This cleans up an old workaround, that had been fixed in numpy three years ago, see https://github.com/numpy/numpy/issues/6784#issuecomment-1341719384. I think this fix went into numpy 1.24.1 or ... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/events | https://github.com/scikit-learn/scikit-learn/pull/32916 | 3,739,099,636 | PR_kwDOAAzd1s65YbGQ | 32,916 | FIX: Ensure deterministic results in FastICA for float32 by honoring random_state (#25038) | {
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This PR fixes non-deterministic behavior in `FastICA` when input data is `float32`.
previously `FastICA` ignored the provided `random_state` in some execution paths for `float32` causing variability across runs
With this patch `random_stat... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/events | https://github.com/scikit-learn/scikit-learn/issues/32915 | 3,739,008,396 | I_kwDOAAzd1s7e3L2M | 32,915 | [for discussion]: adding link to Contributing Guide to top menu | {
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This issue is a **discussion** item.
## Questions:
1. Can we add a link to "Contributing" to the top level menu? It seems a bit hidden, and the questions on contributing come up quite often.
- Contributing: https://scikit-learn.org/stable/developers/contributin... | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/events | https://github.com/scikit-learn/scikit-learn/issues/32914 | 3,738,123,695 | I_kwDOAAzd1s7ezz2v | 32,914 | Addressed: FastICA logcosh bottleneck | {
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For: sklearn/decomposition/_fastica.py
Hello,
Firstly thank you for the amazing library :D
I see in the FastICA there was a bottleneck that was messing a lot with my run time, so i optimized it and compared my implementation with the original and the results are exactly ... | null | {
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discovered while working in #32909
#### What does this implement/fix? Explain your changes.
This adds a proper error handling if a multiclass `y_true` is passed into `average_precision_score`, `roc_auc_score` and `top_k_accuracy_score` while `y_score` has only shape `(n_classes, )` (aka... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/events | https://github.com/scikit-learn/scikit-learn/pull/32911 | 3,734,978,850 | PR_kwDOAAzd1s65Kbvp | 32,911 | PERF: `GradientBoosting*` optimize line-search from O(n * n_leaves) to O(n) for classification losses | {
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Currently the line-search is implemented this way:
```Python
for leaf in leaves:
(indices,) = np.nonzero(masked_terminal_regions == leaf)
```
`np.nonzero(masked_terminal_regions == leaf)` is O(`n_sample`) and we do it `n_leaves` times. This can quickly becom... | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/events | https://github.com/scikit-learn/scikit-learn/issues/32910 | 3,734,591,349 | I_kwDOAAzd1s7emVd1 | 32,910 | bug: Nested Pipeline is not fitted (sklearn 1.8) | {
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However, this poses a more general question on how easy it is to abide by sklearn's conventions: skip directly to [this comment](https://github.com/scikit-learn/scikit-learn/issues/32910#iss... | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/events | https://github.com/scikit-learn/scikit-learn/pull/32909 | 3,734,504,762 | PR_kwDOAAzd1s65Izz8 | 32,909 | FEA Add array API support for `average_precision_score` | {
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towards #26024
#### What does this implement/fix? Explain your changes.
Adds array API support to `average_precision_score`
#### AI usage disclosure
<!--
If AI tools were involved in creating this PR, please check all boxes that apply
below and make sure that you adhere to our Autom... | {
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By sampling radomly from a distribution of x,y values, the RANSAC algorithm tries to find the line which describes best most of the data (while ignoring outliers). For deciding, how well a certain line describes the data, estimator.score() is called in line 526 of _ransac.py.
```pytb
525 # sc... | null | {
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I therefore make a PR in the blog website since it seems that it is the location where we have this information: https://github.com/scikit-learn/blog/pull/220
I just... | {
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create the env from lock-file (only works on Linux)
```
conda-lock install -n scikit-learn-min-dependencies build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock
conda activate scikit-learn-min-dependencies
```
```
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
of having maintainers review your PR:
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Follow up to PR https://github.com/scikit-learn/scikit-learn/pull/32100 and esp. discussion here: https://github.com/scikit-learn/scikit-learn/pull/32100#discussion_r2331679833
#### Parameter naming
For now, I went for `criterion="quantile"` and `quantile=0.9` (to match HGB).
Other p... | null | {
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ElasticNetCV and LassoCV generate warnings when y is a (n,1) vector but doesn't when y is a (n,) vector. This behavior is not in LinearRegression or RidgeCV. Can they be made consistent to avoid confusion in applications?
### Steps/Code to Reproduce
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The `FriedmanMSE.impurity_improvement()` method was incorrectly computing the impurity decrease by not normalizing by the total weighted samples (`weighted_n_samples`).
## Root Cause
The method was computing:
```python
diff * diff / (weighted_n_left * weighted_n_right * weighted_n_node_samples)
```
But it ... | {
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✅ In particular following the pull request checklist will increase the likelihood
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#### Reference Issues/PRs
Noticed when working on #32893
#### What does this implement/fix? Explain your changes.
Add reference to user guide for `MultiLabelBinarizer`
#### AI usage disclosure
<!--
If AI tools were involved in creating this PR, please check all boxes that apply
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Noticed while working on #32755
#### What does this implement/fix? Explain your changes.
Adds reference to the glossary term https://scikit-learn.org/dev/glossary.html#term-label-indicator-matrix for classification metrics
Not sure about the best term to use.
* Avoided use of 'bina... | {
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## Problem
When using ColumnTransformer.set_output(transform='pandas'), scikit-learn raises a ValueError if OneHotEncoder is configured with sparse_output=True, even when downstream transformers (like TruncatedSVD) convert the sparse output to dense.
> ValueError: Pandas output does not support sparse data.
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🙌 Thanks for contributing a pull request!
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- fixes https://github.com/scikit-learn/scikit-learn/pull/32743#issuecomment-3641069962
- fixes https://github.com/scikit-learn/scikit-learn/pull/32660#issuecomment-3546620178
- implements ideas from discussion with @lucyleeow
#### What does this implement/fix? Explain your changes.
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One idea could be to have some commented ... | null | {
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#### What does this implement/fix? Explain your changes.
`confusion_matrix_at_thresholds` was amended to a public function added in #30134. I think it would be worth adding to the common metric tests in `test_common.py`.
This would also fix one of the test failures in #32755
Also f... | {
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This should show the 1.8 in the news section of the [stable website](https://scikit-learn.org/stable) | {
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1. **Cython Backend (`sklearn/metrics/_pairwise_fast.pyx`):**
- Refactored `_chi2_kernel_fast` and `_sparse_manhattan` to use `floating` fused types for internal accumulators.
- Updated `_sparse_manhattan` to accept a `floating[:, ::1]` output buffer, preventing implicit casting to double.
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## Summary
Ensures `nan_euclidean_distances` returns perfectly symmetric matrices when computing self-distances with NaN values, eliminating floating-point rounding asymmetries.
## Problem
When computing distances with NaN values, `nan_euclidean_distances` produced slightly asymmetric matrices (~4e... | null | {
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Note that it still fails because `Ridge` and `LinearDiscriminantAnalysis` still do not accept mixed-array inputs (see #28668).
Not sure if we should fix that here or in a PR that introduces a comm... | null | {
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This issue was discovered while reviewing #32867, but since it's not directly related, let's open a dedicated issue to avoid derailing the original discussion.
As can be seen in the plots below, passing `multiclass_colors="gist_rainbow"` has no impact when `response_method="predict"` but I don't ... | {
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* [x] Generate the changelog in the release branch
* [x] Check that the wheels for the release can be built successfully
* [x] Merge the PR with `[cd build]` commit message to upload wheels to the staging repo
* [x] Upload the wheels and source tarball to https://t... | {
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When missing values are present in `X`, `DecisionTreeRegressor(criterion="poisson", ...)` sometimes computes invalid impurities.
Impurity should match with half-poisson deviance according to the documentation. And it is indeed the case when they are no missing values. But when missing values are ... | {
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For `LogisticRegression` with `n_classes >= 3`, I would like to specify a class-wise (grouped) L21 penalty $\sum_{j=1}^{n_{features}} \Vert\beta_{j,\cdot}\Vert_2 = \sum_{j=1}^{n_{features}} (\sum_{k=1}^{n_{classes}}\beta_{j,k}^2)^{1/2}$ instead of the implemented L1 penalty... | null | {
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the hyperparameters `gamma`, `degree`, and `coef0` are only used
with kernels that support them.
Currently, these parameters are silently ignored for some kernels
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Fixes https://github.com/scikit-learn/scikit-learn/issues/32866 | {
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I was trying to use `DecisionBoundaryDisplay.from_estimator` to display different regions classified by `NuSVC`:
<img width="455" height="355" alt="Image" src="https://github.com/user-attachments/assets/ffd7d137-b7f1-46a9-bd63-b967903617ce" />
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
of having maintainers review your PR:
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## Issue
Currently, `svm.SVC` silently ignores hyperparameters that do not apply to the chosen kernel (e.g., `gamma` with a `'linear'` kernel), which can confuse users.
## Proposed Change
Adds `_warn_for_unu... | {
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
of having maintainers review your PR:
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Allow users to modify the model or fit_params in each step of the chain dynamically.
For example use the model to dynamically configure fit_params, for example add in Eval_set in light GBM latest prediction to prepare it for the next chain model fit.
### Describe your pro... | null | {
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"number": 7... | 7 | 2025-12-08T01:59:07 | 2026-02-18T20:31:22 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
A follow-up to #31068 and https://github.com/scikit-learn/scikit-learn/pull/32809#discussion_r2594800078
#### What does this implement/fix? Explain your changes.
This PR adds an example demonstrating the use of temperature scaling for multi-class probability calibration.
#### Any... | null | {
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Fixes https://github.com/s... | {
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When using `FeatureUnion` with `set_config(transform_output="polars")`, the operation fails with `polars.exceptions.DuplicateError` because the `ContainerAdapterProtocol.hstack()` interface is incomplete - it doesn't accept `feature_names` parameter to apply column renaming before concatenation.
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The Fix:
When Y is None or Y is X, the code now explicitly symmetrizes the result matrix (D + D... | null | {
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