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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
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] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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https://github.com/scikit-learn/scikit-learn/issues/31286 | [
"Needs Decision",
"Array API"
] | Clarification of output array type when metrics accept multiclass/multioutput
Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).
The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issu... | 31,286 | [
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0.015321820043027401,
... |
https://github.com/scikit-learn/scikit-learn/issues/31284 | [
"Bug",
"cython"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: May 05, 2025) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=76198&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (May 05, 2025)
- Test Collection Failure
... | 31,284 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31283 | [
"Build / CI",
"cython"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: May 05, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=76198&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (May 05, 2025)
- Test Collect... | 31,283 | [
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https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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0.0002320646890439093,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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0.0002320646890439093,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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0.0002320646890439093,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31274 | [
"API",
"Array API"
] | Automatically move `y_true` to the same device and namespace as `y_pred` for metrics
This is closely linked to #28668 but separate enough to warrant it's own issue (https://github.com/scikit-learn/scikit-learn/issues/28668#issuecomment-2814771519). This is mostly a summary of discussions so far. If we are happy with a... | 31,274 | [
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0.0002320646890439093,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31269 | [
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: May 05, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14828681637)** (May 05, 2025)
COMMENT:
At the time of writing, #31263 was merged 17 hours ago and this failed 3 hours ago, so the fix was not enough. There i... | 31,269 | [
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https://github.com/scikit-learn/scikit-learn/issues/31269 | [
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: May 05, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14828681637)** (May 05, 2025)
COMMENT:
Direct links to:
- last successful nightly run (for manylinux):
https://github.com/scikit-learn/scikit-learn/actions... | 31,269 | [
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https://github.com/scikit-learn/scikit-learn/issues/31269 | [
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: May 05, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14828681637)** (May 05, 2025)
COMMENT:
This seems to be related to the developer version of Cython. Draft PR to investigate has been opened in #31300.
This ... | 31,269 | [
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https://github.com/scikit-learn/scikit-learn/issues/31267 | [
"New Feature"
] | Change the default data directory
### Describe the workflow you want to enable
It's not a good practice to put files directly into the home directory.
### Describe your proposed solution
A more common way is to put them into the standard cache directories recommended by operating systems:
| OS | Path |
| -- | ----... | 31,267 | [
0.021604187786579132,
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/31267 | [
"New Feature"
] | Change the default data directory
### Describe the workflow you want to enable
It's not a good practice to put files directly into the home directory.
### Describe your proposed solution
A more common way is to put them into the standard cache directories recommended by operating systems:
| OS | Path |
| -- | ----... | 31,267 | [
0.009912367910146713,
0.06217832490801811,
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0.010... |
https://github.com/scikit-learn/scikit-learn/issues/31267 | [
"New Feature"
] | Change the default data directory
### Describe the workflow you want to enable
It's not a good practice to put files directly into the home directory.
### Describe your proposed solution
A more common way is to put them into the standard cache directories recommended by operating systems:
| OS | Path |
| -- | ----... | 31,267 | [
0.009372982196509838,
0.05712896212935448,
-0.01022881455719471,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/31257 | [
"Bug",
"free-threading"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 28, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14699848568)** (Apr 28, 2025)
COMMENT:
The nightly CI has discovered a Cython-related problem on all the free-threading builds:
```python-traceback
_____... | 31,257 | [
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0.05639... |
https://github.com/scikit-learn/scikit-learn/issues/31257 | [
"Bug",
"free-threading"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 28, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14699848568)** (Apr 28, 2025)
COMMENT:
`ColMajor` comes from:
https://github.com/scikit-learn/scikit-learn/blob/7131d9488dfb8edd6ae042caca57dd76523f395b/skl... | 31,257 | [
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https://github.com/scikit-learn/scikit-learn/issues/31257 | [
"Bug",
"free-threading"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 28, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14699848568)** (Apr 28, 2025)
COMMENT:
I am not sure exactly what caused this build to start failing 2 days ago. Here is the history of the runs:
https://gi... | 31,257 | [
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0.033849846571683884,
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0.0575... |
https://github.com/scikit-learn/scikit-learn/issues/31257 | [
"Bug",
"free-threading"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 28, 2025) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/14699848568)** (Apr 28, 2025)
COMMENT:
In the last successful run and the rist failing run, the Python versions were both:
```
3.13.2 experimental free-thre... | 31,257 | [
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0.05193857476115227,
-0.01190126407891512,
... |
https://github.com/scikit-learn/scikit-learn/issues/31256 | [
"module:test-suite"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Apr 26, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=75987&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Apr 26, 2025)
- test_precomputed_nearest_neighbors_f... | 31,256 | [
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-0.002998108509927988,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/31256 | [
"module:test-suite"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Apr 26, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=75987&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Apr 26, 2025)
- test_precomputed_nearest_neighbors_f... | 31,256 | [
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-0.014104613102972507,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31256 | [
"module:test-suite"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Apr 26, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=75987&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Apr 26, 2025)
- test_precomputed_nearest_neighbors_f... | 31,256 | [
-0.02432643063366413,
0.005054646171629429,
-0.009543200023472309,
-0.004204684868454933,
0.044799692928791046,
-0.018147820606827736,
0.04718610271811485,
0.04433899745345116,
-0.0007663739379495382,
0.012331477366387844,
0.03217130899429321,
0.032450322061777115,
0.004296023864299059,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31256 | [
"module:test-suite"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Apr 26, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=75987&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Apr 26, 2025)
- test_precomputed_nearest_neighbors_f... | 31,256 | [
-0.01317402720451355,
0.04085446521639824,
-0.021294688805937767,
-0.027921142056584358,
0.04226367548108101,
-0.005481536965817213,
0.04912727698683739,
0.06879755109548569,
0.022353829815983772,
0.019552113488316536,
0.026787901297211647,
0.05653868615627289,
-0.002405028324574232,
0.075... |
https://github.com/scikit-learn/scikit-learn/issues/31248 | [
"Bug"
] | Hangs in LogisticRegression with high intercept_scaling number
### Describe the bug
When using the `LogisticRegression` model with the solver set to `liblinear` and specifying the `intercept_scaling` parameter, the model hangs without any clear reason. The processing time does not increase gradually with the size of ... | 31,248 | [
-0.03524452820420265,
-0.026955999433994293,
0.010299116373062134,
-0.004588813055306673,
0.07638678699731827,
-0.010965158231556416,
0.018329255282878876,
0.0308246910572052,
0.04769974201917648,
0.011964785866439342,
0.06313729286193848,
0.02237396314740181,
-0.03725694119930267,
0.10827... |
https://github.com/scikit-learn/scikit-learn/issues/31248 | [
"Bug"
] | Hangs in LogisticRegression with high intercept_scaling number
### Describe the bug
When using the `LogisticRegression` model with the solver set to `liblinear` and specifying the `intercept_scaling` parameter, the model hangs without any clear reason. The processing time does not increase gradually with the size of ... | 31,248 | [
-0.03524452820420265,
-0.026955999433994293,
0.010299116373062134,
-0.004588813055306673,
0.07638678699731827,
-0.010965158231556416,
0.018329255282878876,
0.0308246910572052,
0.04769974201917648,
0.011964785866439342,
0.06313729286193848,
0.02237396314740181,
-0.03725694119930267,
0.10827... |
https://github.com/scikit-learn/scikit-learn/issues/31248 | [
"Bug"
] | Hangs in LogisticRegression with high intercept_scaling number
### Describe the bug
When using the `LogisticRegression` model with the solver set to `liblinear` and specifying the `intercept_scaling` parameter, the model hangs without any clear reason. The processing time does not increase gradually with the size of ... | 31,248 | [
-0.03524452820420265,
-0.026955999433994293,
0.010299116373062134,
-0.004588813055306673,
0.07638678699731827,
-0.010965158231556416,
0.018329255282878876,
0.0308246910572052,
0.04769974201917648,
0.011964785866439342,
0.06313729286193848,
0.02237396314740181,
-0.03725694119930267,
0.10827... |
https://github.com/scikit-learn/scikit-learn/issues/31248 | [
"Bug"
] | Hangs in LogisticRegression with high intercept_scaling number
### Describe the bug
When using the `LogisticRegression` model with the solver set to `liblinear` and specifying the `intercept_scaling` parameter, the model hangs without any clear reason. The processing time does not increase gradually with the size of ... | 31,248 | [
-0.03524452820420265,
-0.026955999433994293,
0.010299116373062134,
-0.004588813055306673,
0.07638678699731827,
-0.010965158231556416,
0.018329255282878876,
0.0308246910572052,
0.04769974201917648,
0.011964785866439342,
0.06313729286193848,
0.02237396314740181,
-0.03725694119930267,
0.10827... |
https://github.com/scikit-learn/scikit-learn/issues/31246 | [
"New Feature"
] | Faster Eigen Decomposition for Isomap & KernelPCA
(disclaimer: this issue and associated PR are part of a student project supervised by @smarie )
### Summary
Eigendecomposition is slow when number of samples is large. This impacts decomposition models such as KernelPCA and Isomap. A "randomized" eigendecomposition m... | 31,246 | [
-0.019425395876169205,
0.017064401879906654,
-0.016350381076335907,
0.010770509019494057,
-0.015004594810307026,
0.003912781365215778,
-0.0006158873438835144,
-0.002872229553759098,
-0.013372755609452724,
-0.001042932621203363,
0.028454607352614403,
0.03000546246767044,
0.035473089665174484,... |
https://github.com/scikit-learn/scikit-learn/issues/31246 | [
"New Feature"
] | Faster Eigen Decomposition for Isomap & KernelPCA
(disclaimer: this issue and associated PR are part of a student project supervised by @smarie )
### Summary
Eigendecomposition is slow when number of samples is large. This impacts decomposition models such as KernelPCA and Isomap. A "randomized" eigendecomposition m... | 31,246 | [
-0.019425395876169205,
0.017064401879906654,
-0.016350381076335907,
0.010770509019494057,
-0.015004594810307026,
0.003912781365215778,
-0.0006158873438835144,
-0.002872229553759098,
-0.013372755609452724,
-0.001042932621203363,
0.028454607352614403,
0.03000546246767044,
0.035473089665174484,... |
https://github.com/scikit-learn/scikit-learn/issues/31245 | [
"Bug",
"Needs Triage"
] | GradientBoostingClassifier does not have out-of-bag (OOB) score
### Describe the bug
Hi, the [documentation page](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) for Gradient boosting Classifier says that there is an out-of-bag score that can be retrieved by the `oo... | 31,245 | [
-0.010660341940820217,
-0.05250384286046028,
0.043322496116161346,
-0.0038654401432722807,
0.07145506143569946,
-0.012987607158720493,
-0.040592581033706665,
0.017778770998120308,
-0.015535809099674225,
-0.0026187861803919077,
0.023253945633769035,
0.02280537411570549,
0.02471429668366909,
... |
https://github.com/scikit-learn/scikit-learn/issues/31245 | [
"Bug",
"Needs Triage"
] | GradientBoostingClassifier does not have out-of-bag (OOB) score
### Describe the bug
Hi, the [documentation page](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) for Gradient boosting Classifier says that there is an out-of-bag score that can be retrieved by the `oo... | 31,245 | [
-0.010660341940820217,
-0.05250384286046028,
0.043322496116161346,
-0.0038654401432722807,
0.07145506143569946,
-0.012987607158720493,
-0.040592581033706665,
0.017778770998120308,
-0.015535809099674225,
-0.0026187861803919077,
0.023253945633769035,
0.02280537411570549,
0.02471429668366909,
... |
https://github.com/scikit-learn/scikit-learn/issues/31244 | [
"New Feature"
] | Add the baseline corrected accuracy score for (multi-class) classification to sklearn.metrics
### Describe the workflow you want to enable
Would it be possible to add a new score to `sklearn.metrics`, namely the baseline corrected accuracy score (BCAS) ([DOI:10.5281/zenodo.15262049](https://doi.org/10.5281/zenodo.152... | 31,244 | [
-0.025313690304756165,
0.0740816593170166,
0.03731023520231247,
-0.0365249365568161,
0.050439100712537766,
-0.009355883114039898,
0.015984559431672096,
-0.02975289896130562,
0.014864037744700909,
-0.03607937693595886,
0.0028457599692046642,
0.017680587247014046,
-0.008954810909926891,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/31235 | [
"Bug"
] | MLP Classifier "Logistic" activation function providing ~constant prediction probabilities for all inputs when predicting quadratic function
### Describe the bug
Repeatedly the sigmoid activation function produces very similar (multiple dp) outputs for the prediction probabilities, seemingly similar around the averag... | 31,235 | [
-0.0027095614932477474,
0.0584605410695076,
0.02988578751683235,
0.029821133241057396,
0.0746026337146759,
-0.02572687901556492,
0.03270186111330986,
-0.002655754331499338,
0.03315521404147148,
-0.011623953469097614,
0.010914881713688374,
0.005786590278148651,
-0.01623302511870861,
0.05201... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31224 | [
"Bug"
] | OneVsRestClassifier when all estimators predict a sample belongs to the other classes
### Describe the bug
Hello, I stumbled upon quite a funny case by accident.
In OneVsRestClassifier, each classifier predicts whether a sample belongs to a specific class, or to any of the other class. For instance, if you have 3 cl... | 31,224 | [
0.04282081499695778,
0.05232451483607292,
0.02868950553238392,
0.009271381422877312,
0.04922620579600334,
-0.017489908263087273,
0.04477262124419212,
-0.0040867212228477,
0.01800142228603363,
-0.025455810129642487,
0.075002022087574,
-0.0026282446924597025,
0.014838943257927895,
-0.0119676... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31223 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support orthogonal polynomial features (via QR decomposition) in `PolynomialFeatures`
### Describe the workflow you want to enable
I want to introduce support for orthogonal polynomial features via QR decomposition in `PolynomialFeatures`, closely mirroring the behavior of R's `poly()` function.
In regression modeli... | 31,223 | [
-0.04332584887742996,
0.12527751922607422,
0.04036727175116539,
0.028021713718771935,
0.026135630905628204,
-0.023045213893055916,
0.016410142183303833,
-0.01599893718957901,
0.029834281653165817,
-0.014687717892229557,
0.031246231868863106,
0.04169139638543129,
0.0031037875451147556,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31222 | [
"Bug",
"Needs Investigation"
] | SVC Sigmoid sometimes ROC AUC from predict_proba & decision_function are each other's inverse
### Describe the bug
Uncertain if this is a bug or counter-intuitive expected behavior.
Under certain circumstances the ROC AUC calculated for `SVC` with the `sigmoid` kernel will not agree depending on if you use `predict_... | 31,222 | [
0.01865355111658573,
-0.02782660350203514,
0.01983778551220894,
0.019269457086920738,
0.06549178808927536,
-0.02896277606487274,
-0.014506584964692593,
-0.007773110643029213,
-0.00041127996519207954,
0.04219696298241615,
0.022742031142115593,
0.032306693494319916,
0.013411788269877434,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31219 | [
"New Feature"
] | Add Categorical Feature Support to `IterativeImputer`
### Describe the workflow you want to enable
I want to impute missing values in categorical columns using a similar approach to `IterativeImputer`, which currently works only for continuous data. Specifically, I want to enable the following workflow:
- Identify a... | 31,219 | [
0.009385163895785809,
0.1310853660106659,
-0.0046434239484369755,
-0.05871886759996414,
0.032893091440200806,
0.03427375480532646,
0.04452839121222496,
0.028357964009046555,
0.03968740254640579,
0.0015690099680796266,
0.012639368884265423,
0.009997531771659851,
-0.01871423050761223,
0.0159... |
https://github.com/scikit-learn/scikit-learn/issues/31219 | [
"New Feature"
] | Add Categorical Feature Support to `IterativeImputer`
### Describe the workflow you want to enable
I want to impute missing values in categorical columns using a similar approach to `IterativeImputer`, which currently works only for continuous data. Specifically, I want to enable the following workflow:
- Identify a... | 31,219 | [
0.009385163895785809,
0.1310853660106659,
-0.0046434239484369755,
-0.05871886759996414,
0.032893091440200806,
0.03427375480532646,
0.04452839121222496,
0.028357964009046555,
0.03968740254640579,
0.0015690099680796266,
0.012639368884265423,
0.009997531771659851,
-0.01871423050761223,
0.0159... |
https://github.com/scikit-learn/scikit-learn/issues/31219 | [
"New Feature"
] | Add Categorical Feature Support to `IterativeImputer`
### Describe the workflow you want to enable
I want to impute missing values in categorical columns using a similar approach to `IterativeImputer`, which currently works only for continuous data. Specifically, I want to enable the following workflow:
- Identify a... | 31,219 | [
0.009385163895785809,
0.1310853660106659,
-0.0046434239484369755,
-0.05871886759996414,
0.032893091440200806,
0.03427375480532646,
0.04452839121222496,
0.028357964009046555,
0.03968740254640579,
0.0015690099680796266,
0.012639368884265423,
0.009997531771659851,
-0.01871423050761223,
0.0159... |
https://github.com/scikit-learn/scikit-learn/issues/31219 | [
"New Feature"
] | Add Categorical Feature Support to `IterativeImputer`
### Describe the workflow you want to enable
I want to impute missing values in categorical columns using a similar approach to `IterativeImputer`, which currently works only for continuous data. Specifically, I want to enable the following workflow:
- Identify a... | 31,219 | [
0.009385163895785809,
0.1310853660106659,
-0.0046434239484369755,
-0.05871886759996414,
0.032893091440200806,
0.03427375480532646,
0.04452839121222496,
0.028357964009046555,
0.03968740254640579,
0.0015690099680796266,
0.012639368884265423,
0.009997531771659851,
-0.01871423050761223,
0.0159... |
https://github.com/scikit-learn/scikit-learn/issues/31219 | [
"New Feature"
] | Add Categorical Feature Support to `IterativeImputer`
### Describe the workflow you want to enable
I want to impute missing values in categorical columns using a similar approach to `IterativeImputer`, which currently works only for continuous data. Specifically, I want to enable the following workflow:
- Identify a... | 31,219 | [
0.009385163895785809,
0.1310853660106659,
-0.0046434239484369755,
-0.05871886759996414,
0.032893091440200806,
0.03427375480532646,
0.04452839121222496,
0.028357964009046555,
0.03968740254640579,
0.0015690099680796266,
0.012639368884265423,
0.009997531771659851,
-0.01871423050761223,
0.0159... |
https://github.com/scikit-learn/scikit-learn/issues/31218 | [
"New Feature"
] | Add P4 classification metric
### Describe the workflow you want to enable
Hi, while working on a classification problem I found out there is no dedicated function to compute the P4 metric implemented in sklearn. As a reminder, P4 metrics is a binary classification metric that is commonly seen as an extension of the f... | 31,218 | [
-0.02789352461695671,
0.015168095007538795,
0.012601347640156746,
0.015654131770133972,
0.03016427904367447,
-0.0002883565321099013,
-0.007912660017609596,
-0.029514849185943604,
-0.014555368572473526,
-0.05148978903889656,
0.00098633102606982,
-0.03183535113930702,
-0.02227908931672573,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31218 | [
"New Feature"
] | Add P4 classification metric
### Describe the workflow you want to enable
Hi, while working on a classification problem I found out there is no dedicated function to compute the P4 metric implemented in sklearn. As a reminder, P4 metrics is a binary classification metric that is commonly seen as an extension of the f... | 31,218 | [
-0.02789352461695671,
0.015168095007538795,
0.012601347640156746,
0.015654131770133972,
0.03016427904367447,
-0.0002883565321099013,
-0.007912660017609596,
-0.029514849185943604,
-0.014555368572473526,
-0.05148978903889656,
0.00098633102606982,
-0.03183535113930702,
-0.02227908931672573,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31210 | [
"Bug",
"Needs Investigation"
] | Issues with pairwise_distances(metric='euclidean') when used on the output of UMAP
### Describe the bug
When using pairwise_distances with metric='euclidean' on the output of some data from a UMAP, a `RuntimeWarning: divide by zero encountered in matmul ret = a @ b` is raised. This warning is not raised if you just u... | 31,210 | [
-0.03168466314673424,
0.015449415892362595,
0.036625444889068604,
-0.017957063391804695,
0.0976298525929451,
0.00693287281319499,
0.044368620961904526,
-0.010541511699557304,
-0.02825796604156494,
-0.028246629983186722,
0.018557270988821983,
0.02725495584309101,
-0.013347435742616653,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31210 | [
"Bug",
"Needs Investigation"
] | Issues with pairwise_distances(metric='euclidean') when used on the output of UMAP
### Describe the bug
When using pairwise_distances with metric='euclidean' on the output of some data from a UMAP, a `RuntimeWarning: divide by zero encountered in matmul ret = a @ b` is raised. This warning is not raised if you just u... | 31,210 | [
-0.03168466314673424,
0.015449415892362595,
0.036625444889068604,
-0.017957063391804695,
0.0976298525929451,
0.00693287281319499,
0.044368620961904526,
-0.010541511699557304,
-0.02825796604156494,
-0.028246629983186722,
0.018557270988821983,
0.02725495584309101,
-0.013347435742616653,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31210 | [
"Bug",
"Needs Investigation"
] | Issues with pairwise_distances(metric='euclidean') when used on the output of UMAP
### Describe the bug
When using pairwise_distances with metric='euclidean' on the output of some data from a UMAP, a `RuntimeWarning: divide by zero encountered in matmul ret = a @ b` is raised. This warning is not raised if you just u... | 31,210 | [
-0.03168466314673424,
0.015449415892362595,
0.036625444889068604,
-0.017957063391804695,
0.0976298525929451,
0.00693287281319499,
0.044368620961904526,
-0.010541511699557304,
-0.02825796604156494,
-0.028246629983186722,
0.018557270988821983,
0.02725495584309101,
-0.013347435742616653,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31210 | [
"Bug",
"Needs Investigation"
] | Issues with pairwise_distances(metric='euclidean') when used on the output of UMAP
### Describe the bug
When using pairwise_distances with metric='euclidean' on the output of some data from a UMAP, a `RuntimeWarning: divide by zero encountered in matmul ret = a @ b` is raised. This warning is not raised if you just u... | 31,210 | [
-0.03168466314673424,
0.015449415892362595,
0.036625444889068604,
-0.017957063391804695,
0.0976298525929451,
0.00693287281319499,
0.044368620961904526,
-0.010541511699557304,
-0.02825796604156494,
-0.028246629983186722,
0.018557270988821983,
0.02725495584309101,
-0.013347435742616653,
-0.0... |
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