html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
|---|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/31872 | [
"Bug"
] | Strange normalization of semi-supervised label propagation in `_build_graph`
The method `_build_graph` on the `LabelPropagation` class in `sklearn/semi_supervised/_label_propagation.py` [(line 455)](https://github.com/scikit-learn/scikit-learn/blob/7d1d96819172e2a7c826f04c68b9d93188cf6a92/sklearn/semi_supervised/_labe... | 31,872 | [
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0.042677272111177444,
0.03790242597460747,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/31871 | [
"New Feature",
"Needs Decision - Include Feature"
] | Proposal to Contribute Uncertainty Quantification via Aleatoric/Epistemic Decomposition to scikit-learn
### Describe the workflow you want to enable
Hi,
While ensemble methods like RandomForestRegressor are widely used, scikit-learn currently lacks native support for estimating and exposing predictive uncertainty—an... | 31,871 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31871 | [
"New Feature",
"Needs Decision - Include Feature"
] | Proposal to Contribute Uncertainty Quantification via Aleatoric/Epistemic Decomposition to scikit-learn
### Describe the workflow you want to enable
Hi,
While ensemble methods like RandomForestRegressor are widely used, scikit-learn currently lacks native support for estimating and exposing predictive uncertainty—an... | 31,871 | [
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0.15415692329406738,
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... |
https://github.com/scikit-learn/scikit-learn/issues/31870 | [
"New Feature",
"Needs Triage"
] | Faster algorithm for KMeans
### Describe the workflow you want to enable
Dear community and developers,
I think [this work](https://arxiv.org/abs/2308.09701) might be interesting to the scikit-community. In this work, we discuss 2 classical algorithms for an sampling-based version of k-means, which return an epsil... | 31,870 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0.005660112947225571,
0.003635929897427559,
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0.05496787279844284,
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0.06804478168487549,
0.019100062549114227,
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0.025649908930063248,
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0.03530396148562431,
0.000041080915252678096,
... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0.029460759833455086,
0.02103940211236... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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0.05360930413007736,
0.016747476533055305,
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
-0.0467829704284668,
0.036498986184597015,
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/31869 | [
"New Feature",
"help wanted",
"Hard",
"module:calibration",
"Array API"
] | Array API support for CalibratedClassifierCV
### Describe the workflow you want to enable
Towards #26024.
Use `CalibratedClassifierCV` with pytorch or tensorflow models.
This has become even more interesting use case with #31068.
### Describe your proposed solution
In line with out Array API adoption path.
- [x] ... | 31,869 | [
-0.06857173889875412,
0.027772439643740654,
-0.011922293342649937,
0.0018911006627604365,
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0.012578006833791733,
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https://github.com/scikit-learn/scikit-learn/issues/31862 | [
"Bug",
"Needs Triage"
] | Ordinal Encoder Type Hints State unknown_value should be float, but this produces an error.
### Describe the bug
Following the type hints of the OrdinalEncoder I set the unknown_value parameter to -1.0.
<img width="507" height="146" alt="Image" src="https://github.com/user-attachments/assets/b9c86ab1-7a23-47b3-ad89-... | 31,862 | [
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0.014630270190536976,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
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0.004240906331688166,
0.02217194251716137,
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0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
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0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
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0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
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0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
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0.02043028175830841,
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0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31859 | [
"Bug",
"module:linear_model"
] | Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting
### Describe the bug
When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers somewhere.
As a ... | 31,859 | [
-0.008110647089779377,
0.031299084424972534,
0.047233905643224716,
0.0025355929974466562,
0.0442068874835968,
-0.0358281210064888,
0.02043028175830841,
0.016780458390712738,
0.0746697187423706,
0.004240906331688166,
0.02217194251716137,
-0.002334140008315444,
0.01698017679154873,
-0.010837... |
https://github.com/scikit-learn/scikit-learn/issues/31849 | [
"New Feature",
"Needs Triage"
] | Extend make file to inlcude initial setup installations.
### Describe the workflow you want to enable
I recently made my first contribution to sklearn and found it a bit tidious to do the initial setup after cloning the repo. I think that extending the make file to include something similar to `make inital setup` to ... | 31,849 | [
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0.010439252480864525,
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0.010806899517774582,
0.06654343754053116,
0.0734136700630188,
-0.033081214874982834,
... |
https://github.com/scikit-learn/scikit-learn/issues/31840 | [
"New Feature"
] | SkLearn IQR function
### Describe the workflow you want to enable
Recently, I was working on a machine learning project with a dataset that was quite skewed. I repeatedly had to compute the interquartile range (IQR), calculate the 25th and 75th percentiles, visualize the box plot, and then remove outliers — all manua... | 31,840 | [
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0.0951659232378006,
-0.0452253632247448,
0.08075... |
https://github.com/scikit-learn/scikit-learn/issues/31840 | [
"New Feature"
] | SkLearn IQR function
### Describe the workflow you want to enable
Recently, I was working on a machine learning project with a dataset that was quite skewed. I repeatedly had to compute the interquartile range (IQR), calculate the 25th and 75th percentiles, visualize the box plot, and then remove outliers — all manua... | 31,840 | [
-0.03178723156452179,
0.06895070523023605,
0.027486514300107956,
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0.04400838911533356,
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0.0021124081686139107,
0.01687646470963955,
0.0951659232378006,
-0.0452253632247448,
0.08075... |
https://github.com/scikit-learn/scikit-learn/issues/31834 | [
"Bug",
"Needs Triage"
] | Resource cleanup issues in dataset loaders: files opened but not closed.
### Describe the bug
Two dataset loader functions in `sklearn.datasets` have resource cleanup issues where files are opened but not properly closed using context managers, potentially leading to resource leaks.
The first one is more important:
... | 31,834 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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0.10... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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0.10... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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0.110... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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https://github.com/scikit-learn/scikit-learn/issues/31810 | [
"Build / CI",
"Needs Decision"
] | CI: Enable GitHub Actions App for ppc64le (Power architecture) support
Hi scikit-learn team,
We’re reaching out to propose enabling CI support for the ppc64le (IBM Power) architecture in your repository, as part of a broader effort to ensure cross-platform compatibility in the scientific Python ecosystem.
We’re usin... | 31,810 | [
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0.09144891053438187,
0.002161752199754119,
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0.00967999454587698,
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0.1048... |
https://github.com/scikit-learn/scikit-learn/issues/31808 | [
"Enhancement",
"Moderate",
"module:compose",
"module:preprocessing",
"Pandas compatibility"
] | Handle new `pd.StringDtype` that is coming in pandas 3
This issue is the result of investigating https://github.com/scikit-learn/scikit-learn/issues/31778
The failures in the nightlies are due to changes coming in pandas 3.0. In particular the switch to using `StringDtype` as the type for string columns. The old beha... | 31,808 | [
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-0.017133379355072975,
0.01471... |
https://github.com/scikit-learn/scikit-learn/issues/31808 | [
"Enhancement",
"Moderate",
"module:compose",
"module:preprocessing",
"Pandas compatibility"
] | Handle new `pd.StringDtype` that is coming in pandas 3
This issue is the result of investigating https://github.com/scikit-learn/scikit-learn/issues/31778
The failures in the nightlies are due to changes coming in pandas 3.0. In particular the switch to using `StringDtype` as the type for string columns. The old beha... | 31,808 | [
0.03121234104037285,
0.09721659868955612,
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0.01471... |
https://github.com/scikit-learn/scikit-learn/issues/31808 | [
"Enhancement",
"Moderate",
"module:compose",
"module:preprocessing",
"Pandas compatibility"
] | Handle new `pd.StringDtype` that is coming in pandas 3
This issue is the result of investigating https://github.com/scikit-learn/scikit-learn/issues/31778
The failures in the nightlies are due to changes coming in pandas 3.0. In particular the switch to using `StringDtype` as the type for string columns. The old beha... | 31,808 | [
0.03121234104037285,
0.09721659868955612,
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0.01471... |
https://github.com/scikit-learn/scikit-learn/issues/31804 | [
"Documentation",
"Metadata Routing"
] | DOC metadata docstrings generator has wrong indentation
### Describe the issue linked to the documentation
I am a maintainer of a third party package [fastcan](https://github.com/scikit-learn-contrib/fastcan).
After I update the scikit-learn version from 1.7.0 to 1.7.1, the Sphinx document generation gives the follo... | 31,804 | [
0.09522809088230133,
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0.028607826679944992,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/31799 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jul 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=78376&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jul 21, 2025)
- Test Collection Failure
COMMENT:
Th... | 31,799 | [
-0.007143946830183268,
0.05127228796482086,
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0.03686915338039398,
0.055949848145246506,
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0.030495736747980118,
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0.013204255141317844,
-0.0008615810656920075,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31799 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jul 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=78376&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jul 21, 2025)
- Test Collection Failure
COMMENT:
##... | 31,799 | [
-0.007852078415453434,
0.043930403888225555,
-0.022068623453378677,
-0.031207550317049026,
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0.006229349412024021,
0.03753885254263878,
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0.04526757448911667,
0.034614212810993195,
-0.005014320369809866,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31799 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jul 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=78376&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jul 21, 2025)
- Test Collection Failure
COMMENT:
If... | 31,799 | [
-0.008230658248066902,
0.022274039685726166,
-0.027758141979575157,
-0.03262726217508316,
0.04022722691297531,
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0.021211568266153336,
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0.048476699739694595,
0.0015422439901158214,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/31789 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Jul 19, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/16384706430)** (Jul 19, 2025)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/16395867301) on Jul 20... | 31,789 | [
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0.0312555730342865,
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0.083482... |
https://github.com/scikit-learn/scikit-learn/issues/31781 | [
"Documentation",
"Needs Triage"
] | Documentation may be inaccurate regarding deprecation of `multi_class` in LogisticRegression
### Describe the issue linked to the documentation
In the documentation for `LogisticRegression` under `multi_class`, there is a [note:](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressi... | 31,781 | [
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-0.034... |
https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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0.010352174751460552,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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0.019353047013282776,
-0.0143393... |
https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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0.006535783410072327,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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0.0020621423609554768,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31776 | [
"Bug",
"Documentation"
] | Documentation Bug: Warning about "unstable development version"
### Describe the issue linked to the documentation
When browsing the scikit-learn documentation, I selected a stable version (e.g., 1.7.0) from the versions. However, I still see the warning banner at the top of the page: **This is documentation for an u... | 31,776 | [
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0.02686835080385208,
0.005939329043030739,
-0.0094192... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.03801783174276352,
0.05018635094165802,
0.013407045044004917,
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0.04360305145382881,
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0.00597456656396389,
0.007174426689743996,
0.092324... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.036343466490507126,
0.048719003796577454,
0.011686807498335838,
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-0.04654536023736,
0.006205365061759949,
0.006038862746208906,
0.0933... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.039387743920087814,
0.04966552555561066,
0.011434690095484257,
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-0.04970582202076912,
0.005442859139293432,
0.00824038777500391,
0.09664... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.04069101810455322,
0.0633501410484314,
0.01194525882601738,
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0.009306766092777252,
0.003314623376354575,
0.094396442... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.037436194717884064,
0.059708599001169205,
0.010422292165458202,
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0.004698412958532572,
0.0072194356471300125,
0.09161... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.03410706669092178,
0.04966079443693161,
0.011994893662631512,
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0.05006718635559082,
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0.031684666872024536,
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0.016044680029153824,
0.0025315338280051947,
0.0913... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.038033124059438705,
0.05809839442372322,
0.006397099234163761,
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0.037775080651044846,
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0.022216586396098137,
0.020645761862397194,
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-0.005664004944264889,
-0.03526649996638298,
0.006691513117402792,
0.0005103033618070185,
0.0898... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.041530437767505646,
0.045359473675489426,
0.0127497473731637,
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0.04496772214770317,
0.03716302663087845,
0.025051988661289215,
0.018093155696988106,
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-0.004136141389608383,
-0.04239632561802864,
0.0047757914289832115,
0.007479669526219368,
0.089... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.030953967943787575,
0.06525930017232895,
0.003823137143626809,
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0.04822761192917824,
0.03731211647391319,
0.016159716993570328,
0.015496357344090939,
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0.00231985654681921,
-0.030700288712978363,
0.007857432588934898,
0.01078681368380785,
0.0749555... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.039861712604761124,
0.05934181436896324,
0.0146711440756917,
-0.07091514021158218,
0.04667223244905472,
0.03337855637073517,
0.027120038866996765,
0.02243480645120144,
-0.06458128988742828,
-0.008423519320786,
-0.043031640350818634,
0.004816754721105099,
0.0068154106847941875,
0.08191995... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.009846590459346771,
0.04695092886686325,
-0.003089733887463808,
-0.08392274379730225,
0.04042821377515793,
0.03190162777900696,
0.029739128425717354,
0.016427695751190186,
-0.04275985062122345,
0.009418096393346786,
-0.04105447605252266,
0.0017511784099042416,
0.022068532183766365,
0.075... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.02829589508473873,
0.05853468179702759,
0.003088480792939663,
-0.08232862502336502,
0.04556174576282501,
0.037760499864816666,
0.022727519273757935,
0.01332818903028965,
-0.058576446026563644,
0.003950824029743671,
-0.038088057190179825,
0.008678493089973927,
0.022926608100533485,
0.0722... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.04061208292841911,
0.03960518166422844,
0.012767581269145012,
-0.0693674385547638,
0.045950502157211304,
0.03550690785050392,
0.045549795031547546,
0.00455873366445303,
-0.06444264948368073,
0.005641950760036707,
-0.047469522804021835,
0.006360270082950592,
0.014867544174194336,
0.075046... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.044189222157001495,
0.044888220727443695,
0.008898862637579441,
-0.06776915490627289,
0.040739431977272034,
0.03668757528066635,
0.045292776077985764,
0.011348893865942955,
-0.06407146900892258,
-0.0018738185754045844,
-0.051418330520391464,
0.004351980052888393,
0.021595269441604614,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.0402231365442276,
0.0528416782617569,
0.00861764419823885,
-0.07917367666959763,
0.051654864102602005,
0.038226790726184845,
0.02094862423837185,
0.014886247925460339,
-0.05509435385465622,
0.011063038371503353,
-0.04354575276374817,
0.004991796799004078,
0.011538508348166943,
0.08375268... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.043757494539022446,
0.04581393301486969,
0.009074955247342587,
-0.06726298481225967,
0.040622614324092865,
0.036719512194395065,
0.045227888971567154,
0.009417006745934486,
-0.06684931367635727,
-0.0008096044766716659,
-0.050429847091436386,
0.00240371935069561,
0.02192983217537403,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.041507404297590256,
0.04792338237166405,
0.00793442316353321,
-0.06824665516614914,
0.041327111423015594,
0.039659131318330765,
0.02298477105796337,
0.014012033119797707,
-0.06368347257375717,
-0.004380577243864536,
-0.04115166887640953,
0.009808145463466644,
0.008787875063717365,
0.0846... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.041367895901203156,
0.05552094429731369,
0.014031782746315002,
-0.06625375151634216,
0.040053702890872955,
0.037422001361846924,
0.03329586982727051,
0.01510564424097538,
-0.06160760298371315,
-0.005934785585850477,
-0.04307860881090164,
0.0011717007728293538,
0.005564974620938301,
0.088... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.04013907164335251,
0.0517742894589901,
0.0080363554880023,
-0.07939881831407547,
0.05246954411268234,
0.03916815295815468,
0.020502658560872078,
0.014962084591388702,
-0.057273656129837036,
0.012428783811628819,
-0.04393613338470459,
0.007823476567864418,
0.01252798642963171,
0.082144089... |
https://github.com/scikit-learn/scikit-learn/issues/31773 | [
"High Priority"
] | Anaconda new ToS causing CI failures
New Anaconda ToS: https://www.anaconda.com/legal/terms/terms-of-service , effective 15 July 2025, is causing the follow error in our CIs:
```
CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels. Please accept or remove them before procee... | 31,773 | [
0.03758421540260315,
0.05368701368570328,
0.014149386435747147,
-0.0637885183095932,
0.04226597771048546,
0.036780763417482376,
0.03376311436295509,
0.018444735556840897,
-0.059967461973428726,
-0.005638981703668833,
-0.046110253781080246,
0.0036490410566329956,
0.005160362459719181,
0.091... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
0.012199021875858307,
-0.029138442128896713,
0.014206047169864178,
-0.007579275406897068,
0.042719438672065735,
-0.02355697751045227,
0.027982275933027267,
0.02542424015700817,
-0.014816916547715664,
0.008671525865793228,
0.04485366865992546,
0.006545621436089277,
0.029442481696605682,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
-0.010846137069165707,
-0.02052718959748745,
0.011835126206278801,
0.009898715652525425,
0.039204563945531845,
-0.03173668682575226,
0.014043032191693783,
0.027724051848053932,
-0.03406577929854393,
-0.002048116410151124,
0.0500185526907444,
-0.0011743576033040881,
0.0277096014469862,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
-0.0015400283737108111,
-0.02574583701789379,
0.017769448459148407,
-0.011838330887258053,
0.03425949066877365,
-0.03171992301940918,
0.008943055756390095,
0.011233264580368996,
-0.03408995643258095,
0.014862501062452793,
0.04519844055175781,
-0.009271039627492428,
0.045983508229255676,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
-0.004357691388577223,
-0.031420089304447174,
0.027391497045755386,
-0.0030985139310359955,
0.037932053208351135,
-0.02747226692736149,
-0.01152543444186449,
0.0030697640031576157,
-0.03980861231684685,
0.017463766038417816,
0.04389271140098572,
-0.00042537125409580767,
0.03430469334125519,
... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
0.008550371043384075,
-0.029268862679600716,
0.014883124269545078,
-0.007791420444846153,
0.042386773973703384,
-0.02388375997543335,
0.028500966727733612,
0.02160574309527874,
-0.016564253717660904,
0.01228494755923748,
0.04492002725601196,
0.007205227389931679,
0.026205584406852722,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/31761 | [
"API"
] | y_pred changed to y_true in RocCurveDisplay.from_predictions, but not in DetCurveDisplay.from_predictions
The parameter `y_pred` was deprecated in `RocCurveDisplay.from_predictions` and replaced by `y_score`. Although the `y_pred` parameter in `DetCurveDisplay.from_predictions` has an identical docstring (except for... | 31,761 | [
0.010585234500467777,
-0.023341143503785133,
0.013614877127110958,
-0.010296589694917202,
0.04123160243034363,
-0.02372407168149948,
0.027211114764213562,
0.026156334206461906,
-0.016670431941747665,
0.005358681548386812,
0.045969847589731216,
0.009683608077466488,
0.027972443029284477,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
0.012729629874229431,
-0.06499407440423965,
-0.04230550676584244,
0.014957441948354244,
-0.024045631289482117,
0.014132522977888584,
-0.00040425604674965143,
0.06777176260948181,
0.05371866375207901,
-0.009672719053924084,
-0.021470580250024796,
0.039065003395080566,
-0.045126162469387054,
... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
0.0037931897677481174,
-0.05566064268350601,
-0.04335644841194153,
0.01681646890938282,
-0.019908301532268524,
-0.0037900146562606096,
-0.00738164596259594,
0.044872552156448364,
0.03226914256811142,
-0.008867240510880947,
-0.017011893913149834,
0.02396526373922825,
-0.039084430783987045,
... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
0.010887556709349155,
-0.04896856099367142,
-0.04422834515571594,
0.018479429185390472,
-0.01608349196612835,
-0.0032613519579172134,
-0.0033893065992742777,
0.047488290816545486,
0.03253769874572754,
-0.00888343807309866,
-0.02208280749619007,
0.026864653453230858,
-0.044609684497117996,
... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
0.010072323493659496,
-0.03922732174396515,
-0.048490919172763824,
0.008335074409842491,
-0.027041839435696602,
0.004426718223839998,
0.016611168161034584,
0.07193750888109207,
0.049941569566726685,
0.005332099739462137,
0.015425099059939384,
0.02041318453848362,
-0.05501195043325424,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
-0.01769445836544037,
-0.06756625324487686,
-0.07200941443443298,
0.003284894861280918,
-0.025677712634205818,
-0.012272830121219158,
-0.012725349515676498,
0.06062851846218109,
0.026259340345859528,
0.015761511400341988,
-0.0065253423526883125,
0.04327676072716713,
-0.07794077694416046,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31754 | [
"New Feature"
] | In Balltree, filter out/mask specific points in query
### Describe the workflow you want to enable
I would like to be able to query nearest points within a Balltree but excluding some of them.
E.g. I create a Balltree on 60k points. I want to find the k nearest neighbour points but within a subset of the 60k points. ... | 31,754 | [
-0.009667652659118176,
-0.06400936096906662,
-0.06959660351276398,
-0.0019092224538326263,
-0.03394730016589165,
-0.01652553491294384,
-0.008517725393176079,
0.06096244230866432,
0.024695182219147682,
0.015051310881972313,
-0.0006996986921876669,
0.04635314643383026,
-0.07553034275770187,
... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
0.019359350204467773,
0.06806984543800354,
0.03385979309678078,
-0.030190477147698402,
0.020772483199834824,
0.009270502254366875,
-0.006074278615415096,
-0.031220171600580215,
0.043439581990242004,
0.002214323729276657,
0.02432011067867279,
0.09068141132593155,
-0.06009742617607117,
0.127... |
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