html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/32872 | [
"Bug",
"module:inspection"
] | `DecisionBoundaryDisplay` with `response_method="predict"` has inconsistent handling for the colormap in the multiclass case
### Describe the bug
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 ... | 32,872 |
https://github.com/scikit-learn/scikit-learn/issues/32872 | [
"Bug",
"module:inspection"
] | `DecisionBoundaryDisplay` with `response_method="predict"` has inconsistent handling for the colormap in the multiclass case
### Describe the bug
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 ... | 32,872 |
https://github.com/scikit-learn/scikit-learn/issues/32870 | [
"Bug"
] | BUG: `DecisionTreeRegressor`: invalid impurity for `criterion="poisson"` with missing values
### Describe the bug
When missing values are present in `X`, `DecisionTreeRegressor(criterion="poisson", ...)` sometimes computes invalid impurities.
Impurity should match with half-poisson deviance according to the document... | 32,870 |
https://github.com/scikit-learn/scikit-learn/issues/32869 | [
"New Feature"
] | Classwise (group) L21 penalty for multinomial LogisticRegression
### Describe the workflow you want to enable
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_{cl... | 32,869 |
https://github.com/scikit-learn/scikit-learn/issues/32869 | [
"New Feature"
] | Classwise (group) L21 penalty for multinomial LogisticRegression
### Describe the workflow you want to enable
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_{cl... | 32,869 |
https://github.com/scikit-learn/scikit-learn/issues/32869 | [
"New Feature"
] | Classwise (group) L21 penalty for multinomial LogisticRegression
### Describe the workflow you want to enable
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_{cl... | 32,869 |
https://github.com/scikit-learn/scikit-learn/issues/32866 | [
"Bug"
] | `DecisionBoundaryDisplay.from_estimator` only displays up to 7 distinct colours
### Describe the bug
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... | 32,866 |
https://github.com/scikit-learn/scikit-learn/issues/32866 | [
"Bug"
] | `DecisionBoundaryDisplay.from_estimator` only displays up to 7 distinct colours
### Describe the bug
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... | 32,866 |
https://github.com/scikit-learn/scikit-learn/issues/32866 | [
"Bug"
] | `DecisionBoundaryDisplay.from_estimator` only displays up to 7 distinct colours
### Describe the bug
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... | 32,866 |
https://github.com/scikit-learn/scikit-learn/issues/32861 | [
"Needs Triage"
] | Pytest -Werror fails with versions <9.0 due to faulthandler
When trying to turn warnings into errors with `pytest -Werror`, there is an error with `faulthandler` (`faulthandler` was introduced in #32776) and the tests don't run.
Updating pytest to >=9.0 fixes the problem.
Here's the traceback for when I used pytest=... | 32,861 |
https://github.com/scikit-learn/scikit-learn/issues/32861 | [
"Needs Triage"
] | Pytest -Werror fails with versions <9.0 due to faulthandler
When trying to turn warnings into errors with `pytest -Werror`, there is an error with `faulthandler` (`faulthandler` was introduced in #32776) and the tests don't run.
Updating pytest to >=9.0 fixes the problem.
Here's the traceback for when I used pytest=... | 32,861 |
https://github.com/scikit-learn/scikit-learn/issues/32861 | [
"Needs Triage"
] | Pytest -Werror fails with versions <9.0 due to faulthandler
When trying to turn warnings into errors with `pytest -Werror`, there is an error with `faulthandler` (`faulthandler` was introduced in #32776) and the tests don't run.
Updating pytest to >=9.0 fixes the problem.
Here's the traceback for when I used pytest=... | 32,861 |
https://github.com/scikit-learn/scikit-learn/issues/32852 | [
"Bug"
] | FeatureUnion with polars output fails due to missing column renaming in adapter interface
### Describe the bug
When using `FeatureUnion` with `set_config(transform_output="polars")`, the operation fails with `polars.exceptions.DuplicateError` because the `ContainerAdapterProtocol.hstack()` interface is incomplete - i... | 32,852 |
https://github.com/scikit-learn/scikit-learn/issues/32852 | [
"Bug"
] | FeatureUnion with polars output fails due to missing column renaming in adapter interface
### Describe the bug
When using `FeatureUnion` with `set_config(transform_output="polars")`, the operation fails with `polars.exceptions.DuplicateError` because the `ContainerAdapterProtocol.hstack()` interface is incomplete - i... | 32,852 |
https://github.com/scikit-learn/scikit-learn/issues/32848 | [
"Bug"
] | nan_euclidean_distances producing distance matrix not symmetrical due to floating point precision
### Describe the bug
nan_euclidean_distances is producing asymmetrical matrix when input matrix contains nan values. This, in turn, causes errors when checked for symmetry, for example by scipy.spatial.distance.squarefor... | 32,848 |
https://github.com/scikit-learn/scikit-learn/issues/32837 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes some estimators without array API support to reject valid NumPy inputs
### Describe the bug
This might be related to #32836 (or not).
I think we need a common test to check that enabling array API support does not affect estimators (without array API support) when called on regular... | 32,837 |
https://github.com/scikit-learn/scikit-learn/issues/32837 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes some estimators without array API support to reject valid NumPy inputs
### Describe the bug
This might be related to #32836 (or not).
I think we need a common test to check that enabling array API support does not affect estimators (without array API support) when called on regular... | 32,837 |
https://github.com/scikit-learn/scikit-learn/issues/32837 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes some estimators without array API support to reject valid NumPy inputs
### Describe the bug
This might be related to #32836 (or not).
I think we need a common test to check that enabling array API support does not affect estimators (without array API support) when called on regular... | 32,837 |
https://github.com/scikit-learn/scikit-learn/issues/32837 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes some estimators without array API support to reject valid NumPy inputs
### Describe the bug
This might be related to #32836 (or not).
I think we need a common test to check that enabling array API support does not affect estimators (without array API support) when called on regular... | 32,837 |
https://github.com/scikit-learn/scikit-learn/issues/32836 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes pipelines to reject dataframe inputs
### Describe the bug
Enabling the array API dispatch has a negative side effect on code that usually accepts non-array inputs such as pandas dataframes:
### Steps/Code to Reproduce
```python
# %%
import os
os.environ["SCIPY_ARRAY_API"] = "1"
#... | 32,836 |
https://github.com/scikit-learn/scikit-learn/issues/32836 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes pipelines to reject dataframe inputs
### Describe the bug
Enabling the array API dispatch has a negative side effect on code that usually accepts non-array inputs such as pandas dataframes:
### Steps/Code to Reproduce
```python
# %%
import os
os.environ["SCIPY_ARRAY_API"] = "1"
#... | 32,836 |
https://github.com/scikit-learn/scikit-learn/issues/32836 | [
"Bug",
"Array API"
] | Enabling array API dispatch causes pipelines to reject dataframe inputs
### Describe the bug
Enabling the array API dispatch has a negative side effect on code that usually accepts non-array inputs such as pandas dataframes:
### Steps/Code to Reproduce
```python
# %%
import os
os.environ["SCIPY_ARRAY_API"] = "1"
#... | 32,836 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32834 | [
"RFC"
] | RFC: Potential improvement of HTML Display's css logic
I propose to remove repetition of CSS styling in `sklearn/utils/_repr_html/estimator.py`.
Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://... | 32,834 |
https://github.com/scikit-learn/scikit-learn/issues/32833 | [
"Bug",
"Documentation",
"Build / CI",
"wasm"
] | Broken dev website JupyterLite since December 2
can reproduce opening a new notebook in https://scikit-learn.org/dev/lite/lab/index.html
Maybe related to merging https://github.com/scikit-learn/scikit-learn/pull/32824, maybe something else in the world that changed.
Noticed thanks to https://github.com/lesteve/test-... | 32,833 |
https://github.com/scikit-learn/scikit-learn/issues/32829 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 10, 2025) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83308&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 10, 2025)
- test_pandas_copy_on_writ... | 32,829 |
https://github.com/scikit-learn/scikit-learn/issues/32829 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 10, 2025) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83308&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 10, 2025)
- test_pandas_copy_on_writ... | 32,829 |
https://github.com/scikit-learn/scikit-learn/issues/32817 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Nov 30, 2025) ⚠️
**CI failed on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83023&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Nov 30, 2025)
Unable to find junit file. Please se... | 32,817 |
https://github.com/scikit-learn/scikit-learn/issues/32817 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Nov 30, 2025) ⚠️
**CI failed on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83023&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Nov 30, 2025)
Unable to find junit file. Please se... | 32,817 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32807 | [
"Build / CI",
"Needs Triage"
] | release notes for 1.8.0 look suspiciously empty
Not sure this is relate to this PR specifically but the release notes for version 1.9 look very full and that for 1.8 very empty:
https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there try... | 32,807 |
https://github.com/scikit-learn/scikit-learn/issues/32805 | [
"RFC"
] | RFC: `Bagging` estimators: avoid changing `max_samples` default behavior in 1.8
As stated in the change log of PR #31414
> `max_samples` is now interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a float.
Because `max_samples` default to `1.0`, this is a change of the default be... | 32,805 |
https://github.com/scikit-learn/scikit-learn/issues/32805 | [
"RFC"
] | RFC: `Bagging` estimators: avoid changing `max_samples` default behavior in 1.8
As stated in the change log of PR #31414
> `max_samples` is now interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a float.
Because `max_samples` default to `1.0`, this is a change of the default be... | 32,805 |
https://github.com/scikit-learn/scikit-learn/issues/32805 | [
"RFC"
] | RFC: `Bagging` estimators: avoid changing `max_samples` default behavior in 1.8
As stated in the change log of PR #31414
> `max_samples` is now interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a float.
Because `max_samples` default to `1.0`, this is a change of the default be... | 32,805 |
https://github.com/scikit-learn/scikit-learn/issues/32805 | [
"RFC"
] | RFC: `Bagging` estimators: avoid changing `max_samples` default behavior in 1.8
As stated in the change log of PR #31414
> `max_samples` is now interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a float.
Because `max_samples` default to `1.0`, this is a change of the default be... | 32,805 |
https://github.com/scikit-learn/scikit-learn/issues/32788 | [
"Bug",
"Needs Triage"
] | TSNE segfaults when numbers turn to NaN
### Describe the bug
When running `TSNE.fit_transform`, if numbers produced during the initialization somehow turned to NaN, it will segfault the process instead of throwing a python exception.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold imp... | 32,788 |
https://github.com/scikit-learn/scikit-learn/issues/32788 | [
"Bug",
"Needs Triage"
] | TSNE segfaults when numbers turn to NaN
### Describe the bug
When running `TSNE.fit_transform`, if numbers produced during the initialization somehow turned to NaN, it will segfault the process instead of throwing a python exception.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold imp... | 32,788 |
https://github.com/scikit-learn/scikit-learn/issues/32788 | [
"Bug",
"Needs Triage"
] | TSNE segfaults when numbers turn to NaN
### Describe the bug
When running `TSNE.fit_transform`, if numbers produced during the initialization somehow turned to NaN, it will segfault the process instead of throwing a python exception.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold imp... | 32,788 |
https://github.com/scikit-learn/scikit-learn/issues/32788 | [
"Bug",
"Needs Triage"
] | TSNE segfaults when numbers turn to NaN
### Describe the bug
When running `TSNE.fit_transform`, if numbers produced during the initialization somehow turned to NaN, it will segfault the process instead of throwing a python exception.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold imp... | 32,788 |
https://github.com/scikit-learn/scikit-learn/issues/32784 | [
"Array API"
] | Implement native Array API support for LinearRegression (feat. custom NNLS solver)
### Describe the workflow you want to enable
LinearRegression is the "Hello World" of machine learning, yet it currently lacks native Array API support, forcing users to move data to the CPU for training. This creates a significant gap... | 32,784 |
https://github.com/scikit-learn/scikit-learn/issues/32781 | [
"Build / CI"
] | Address sanitizer
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit... | 32,781 |
https://github.com/scikit-learn/scikit-learn/issues/32781 | [
"Build / CI"
] | Address sanitizer
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit... | 32,781 |
https://github.com/scikit-learn/scikit-learn/issues/32781 | [
"Build / CI"
] | Address sanitizer
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit... | 32,781 |
https://github.com/scikit-learn/scikit-learn/issues/32781 | [
"Build / CI"
] | Address sanitizer
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit... | 32,781 |
https://github.com/scikit-learn/scikit-learn/issues/32781 | [
"Build / CI"
] | Address sanitizer
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit... | 32,781 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32767 | [
"Bug",
"module:linear_model"
] | ElasticNetCV alpha_grid does not take positive constraint into account
### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a resul... | 32,767 |
https://github.com/scikit-learn/scikit-learn/issues/32765 | [
"Needs Triage"
] | ⚠️ CI failed on Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204 (last failure: Nov 23, 2025) ⚠️
**CI failed on [Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=82693&view=logs&j=f71949a9-f9d9-549e-cf45-2e99c7b412d1)** (... | 32,765 |
https://github.com/scikit-learn/scikit-learn/issues/32765 | [
"Needs Triage"
] | ⚠️ CI failed on Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204 (last failure: Nov 23, 2025) ⚠️
**CI failed on [Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=82693&view=logs&j=f71949a9-f9d9-549e-cf45-2e99c7b412d1)** (... | 32,765 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32763 | [
"New Feature"
] | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB)
### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn cl... | 32,763 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.