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https://github.com/scikit-learn/scikit-learn/issues/28629 | [
"New Feature"
] | Make RFE/RFECV preserve pandas dataframes
### Describe the workflow you want to enable
Hi!
I am currently using xgboost with some categorical features. To get that to work the categorical features have to be marked as such in the pandas dataframe:
```python
df["my_cats"] = df["my_cats"].astype("string").astype("... | 28,629 | [
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https://github.com/scikit-learn/scikit-learn/issues/28629 | [
"New Feature"
] | Make RFE/RFECV preserve pandas dataframes
### Describe the workflow you want to enable
Hi!
I am currently using xgboost with some categorical features. To get that to work the categorical features have to be marked as such in the pandas dataframe:
```python
df["my_cats"] = df["my_cats"].astype("string").astype("... | 28,629 | [
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https://github.com/scikit-learn/scikit-learn/issues/28629 | [
"New Feature"
] | Make RFE/RFECV preserve pandas dataframes
### Describe the workflow you want to enable
Hi!
I am currently using xgboost with some categorical features. To get that to work the categorical features have to be marked as such in the pandas dataframe:
```python
df["my_cats"] = df["my_cats"].astype("string").astype("... | 28,629 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/28629 | [
"New Feature"
] | Make RFE/RFECV preserve pandas dataframes
### Describe the workflow you want to enable
Hi!
I am currently using xgboost with some categorical features. To get that to work the categorical features have to be marked as such in the pandas dataframe:
```python
df["my_cats"] = df["my_cats"].astype("string").astype("... | 28,629 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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-0... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
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https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
-0.021778738126158714,
-0.00048164662439376116,
0.002148267114534974,
-0.041881054639816284,
0.04386753961443901,
0.03242812305688858,
0.0007264737505465746,
0.06147516146302223,
-0.01102943904697895,
0.019550353288650513,
0.01113041304051876,
0.04846615344285965,
-0.020430881530046463,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
-0.021778738126158714,
-0.00048164662439376116,
0.002148267114534974,
-0.041881054639816284,
0.04386753961443901,
0.03242812305688858,
0.0007264737505465746,
0.06147516146302223,
-0.01102943904697895,
0.019550353288650513,
0.01113041304051876,
0.04846615344285965,
-0.020430881530046463,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
-0.021778738126158714,
-0.00048164662439376116,
0.002148267114534974,
-0.041881054639816284,
0.04386753961443901,
0.03242812305688858,
0.0007264737505465746,
0.06147516146302223,
-0.01102943904697895,
0.019550353288650513,
0.01113041304051876,
0.04846615344285965,
-0.020430881530046463,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
-0.021778738126158714,
-0.00048164662439376116,
0.002148267114534974,
-0.041881054639816284,
0.04386753961443901,
0.03242812305688858,
0.0007264737505465746,
0.06147516146302223,
-0.01102943904697895,
0.019550353288650513,
0.01113041304051876,
0.04846615344285965,
-0.020430881530046463,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28625 | [
"cython"
] | BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out
In MNE-Python our Windows [pip-pre job on Azure has started reliably timing out](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=29467&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=d18f7... | 28,625 | [
-0.021778738126158714,
-0.00048164662439376116,
0.002148267114534974,
-0.041881054639816284,
0.04386753961443901,
0.03242812305688858,
0.0007264737505465746,
0.06147516146302223,
-0.01102943904697895,
0.019550353288650513,
0.01113041304051876,
0.04846615344285965,
-0.020430881530046463,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28619 | [
"Enhancement"
] | Add an option handle_unknown="warn" in OneHotEncoder
Follow-up to https://github.com/scikit-learn/scikit-learn/pull/16881
It seems that it could be interested to log an eventual detection of new category during inference and issue a warning instead of silently ignoring them.
Therefore, it seems reasonable to add... | 28,619 | [
-0.01997786946594715,
0.05125065892934799,
0.012749122455716133,
-0.04020350053906441,
0.04173792526125908,
0.04422543942928314,
0.016766952350735664,
0.024252329021692276,
0.030489780008792877,
-0.012996462173759937,
0.11654681712388992,
-0.002064098371192813,
-0.07837313413619995,
0.0838... |
https://github.com/scikit-learn/scikit-learn/issues/28619 | [
"Enhancement"
] | Add an option handle_unknown="warn" in OneHotEncoder
Follow-up to https://github.com/scikit-learn/scikit-learn/pull/16881
It seems that it could be interested to log an eventual detection of new category during inference and issue a warning instead of silently ignoring them.
Therefore, it seems reasonable to add... | 28,619 | [
-0.016907405108213425,
0.055186912417411804,
0.006497688125818968,
-0.04247649013996124,
0.047773923724889755,
0.045321010053157806,
0.009550622664391994,
0.020003776997327805,
0.028049815446138382,
-0.012048018164932728,
0.1141941249370575,
0.0021718095522373915,
-0.07080487906932831,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28618 | [
"New Feature"
] | Add a download_openml util
We should add a `download_openml` utility in `sklearn.datasets` which downloads the file, but doesn't return `X, y`, and instead returns the paths to the downloaded data file (arff or parquet), and the metadata json file.
This utility can then be internally called by `fetch_openml`.
A ... | 28,618 | [
-0.0010721818543970585,
0.03905286267399788,
0.01989743672311306,
0.004197532776743174,
0.018027259036898613,
-0.0026760203763842583,
0.012476272881031036,
-0.019862201064825058,
0.002922821557149291,
-0.016283372417092323,
-0.0345766581594944,
0.12084082514047623,
0.007792951073497534,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28618 | [
"New Feature"
] | Add a download_openml util
We should add a `download_openml` utility in `sklearn.datasets` which downloads the file, but doesn't return `X, y`, and instead returns the paths to the downloaded data file (arff or parquet), and the metadata json file.
This utility can then be internally called by `fetch_openml`.
A ... | 28,618 | [
0.001042540417984128,
0.03552856668829918,
0.019647855311632156,
0.0013551096199080348,
0.02163265086710453,
0.0006178649491630495,
0.026323117315769196,
-0.0245925672352314,
0.001943242852576077,
-0.020392490550875664,
-0.04440024867653847,
0.1337897777557373,
-0.008697126992046833,
0.051... |
https://github.com/scikit-learn/scikit-learn/issues/28618 | [
"New Feature"
] | Add a download_openml util
We should add a `download_openml` utility in `sklearn.datasets` which downloads the file, but doesn't return `X, y`, and instead returns the paths to the downloaded data file (arff or parquet), and the metadata json file.
This utility can then be internally called by `fetch_openml`.
A ... | 28,618 | [
0.003953342791646719,
0.02521205134689808,
0.013450384140014648,
0.0025656288489699364,
0.02507915534079075,
-0.00675782049074769,
0.03389693796634674,
-0.02745337411761284,
0.011236888356506824,
-0.022207187488675117,
-0.04665132239460945,
0.14663667976856232,
-0.01623796485364437,
0.0430... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28617 | [
"Bug",
"cython"
] | Error compiling with GCC14 in i686
### Describe the bug
This is another error compiling with GCC14, different to the error reported in #28530
It happens when compiling in i386 in the Fedora build system. I get an "incompatible pointer type" `between `random_UINT32_t *` and `typedefs_uint32_t *`
A function expect... | 28,617 | [
0.012070094235241413,
-0.009659399278461933,
-0.0016583791002631187,
-0.003770986804738641,
0.018108094111084938,
0.05176316201686859,
0.03066469542682171,
0.031343113631010056,
0.022750848904252052,
-0.05188089236617088,
-0.014687477611005306,
0.003902359399944544,
0.009308730252087116,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28610 | [
"Documentation"
] | DOC: update FAQs to add permission using images
### Describe the issue linked to the documentation
We receive many inquiries on the mailing list if developers can have permission to use the images in scikit-learn for their work.
Add an FAQ to answer this question:
- code is under a BSD 3-clause licence, so the pe... | 28,610 | [
0.0269603431224823,
-0.02723211981356144,
-0.011138088069856167,
0.028722185641527176,
0.006087960675358772,
0.04014763981103897,
0.07489679008722305,
-0.02314569614827633,
0.046688973903656006,
-0.04252580925822258,
-0.00650757784023881,
0.029537225142121315,
-0.014604632742702961,
-0.003... |
https://github.com/scikit-learn/scikit-learn/issues/28609 | [
"Enhancement"
] | Print warning if user passed only one class into StratifiedKFold
### Describe the workflow you want to enable
StratifiedKFold and other stratified splitters were designed to balance cross validation based on target or some features.
Currently, if you pass a column with only one class (which majority of times is a ... | 28,609 | [
-0.04640965163707733,
0.006505083758383989,
0.01431497372686863,
0.0038545308634638786,
0.07614386826753616,
-0.03316653519868851,
-0.017132094129920006,
0.03202955424785614,
-0.02612181007862091,
-0.007651655934751034,
0.07414879649877548,
0.02412363886833191,
-0.05620637536048889,
0.0314... |
https://github.com/scikit-learn/scikit-learn/issues/28609 | [
"Enhancement"
] | Print warning if user passed only one class into StratifiedKFold
### Describe the workflow you want to enable
StratifiedKFold and other stratified splitters were designed to balance cross validation based on target or some features.
Currently, if you pass a column with only one class (which majority of times is a ... | 28,609 | [
-0.047232504934072495,
0.0030491251964122057,
0.014160525985062122,
0.0048886858858168125,
0.07527031749486923,
-0.032372791320085526,
-0.01727188006043434,
0.033173710107803345,
-0.02516566403210163,
-0.007802692707628012,
0.07529008388519287,
0.02306741662323475,
-0.05548887699842453,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28609 | [
"Enhancement"
] | Print warning if user passed only one class into StratifiedKFold
### Describe the workflow you want to enable
StratifiedKFold and other stratified splitters were designed to balance cross validation based on target or some features.
Currently, if you pass a column with only one class (which majority of times is a ... | 28,609 | [
-0.046536289155483246,
-0.0013401210308074951,
0.014140416868031025,
0.0032705178018659353,
0.07889463752508163,
-0.03017403744161129,
-0.015720585361123085,
0.03201603889465332,
-0.02774035558104515,
-0.006164844613522291,
0.07238895446062088,
0.022433260455727577,
-0.052320148795843124,
... |
https://github.com/scikit-learn/scikit-learn/issues/28605 | [
"Bug"
] | TypeError: cpu_count() got an unexpected keyword argument 'only_physical_cores'
### Describe the bug
I am running the KNeighbordsClassifier inside a framework of pytorch_lightning. I am fitting the model correctly, but when I try to predict new results I have an error.
### Steps/Code to Reproduce
```python
estimat... | 28,605 | [
0.0034140474162995815,
-0.023061834275722504,
-0.010526075027883053,
0.012347222305834293,
0.0590987391769886,
0.011361141689121723,
0.015016346238553524,
0.04904317483305931,
0.03729354590177536,
0.009321717545390129,
0.012043697759509087,
0.059698451310396194,
-0.0028672211337834597,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28605 | [
"Bug"
] | TypeError: cpu_count() got an unexpected keyword argument 'only_physical_cores'
### Describe the bug
I am running the KNeighbordsClassifier inside a framework of pytorch_lightning. I am fitting the model correctly, but when I try to predict new results I have an error.
### Steps/Code to Reproduce
```python
estimat... | 28,605 | [
0.0034140474162995815,
-0.023061834275722504,
-0.010526075027883053,
0.012347222305834293,
0.0590987391769886,
0.011361141689121723,
0.015016346238553524,
0.04904317483305931,
0.03729354590177536,
0.009321717545390129,
0.012043697759509087,
0.059698451310396194,
-0.0028672211337834597,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28605 | [
"Bug"
] | TypeError: cpu_count() got an unexpected keyword argument 'only_physical_cores'
### Describe the bug
I am running the KNeighbordsClassifier inside a framework of pytorch_lightning. I am fitting the model correctly, but when I try to predict new results I have an error.
### Steps/Code to Reproduce
```python
estimat... | 28,605 | [
0.0034140474162995815,
-0.023061834275722504,
-0.010526075027883053,
0.012347222305834293,
0.0590987391769886,
0.011361141689121723,
0.015016346238553524,
0.04904317483305931,
0.03729354590177536,
0.009321717545390129,
0.012043697759509087,
0.059698451310396194,
-0.0028672211337834597,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28605 | [
"Bug"
] | TypeError: cpu_count() got an unexpected keyword argument 'only_physical_cores'
### Describe the bug
I am running the KNeighbordsClassifier inside a framework of pytorch_lightning. I am fitting the model correctly, but when I try to predict new results I have an error.
### Steps/Code to Reproduce
```python
estimat... | 28,605 | [
0.0034140474162995815,
-0.023061834275722504,
-0.010526075027883053,
0.012347222305834293,
0.0590987391769886,
0.011361141689121723,
0.015016346238553524,
0.04904317483305931,
0.03729354590177536,
0.009321717545390129,
0.012043697759509087,
0.059698451310396194,
-0.0028672211337834597,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28596 | [
"Bug",
"Build / CI"
] | Missing _ZdlPv symbol in _argkmin_classmode for manylinux wheels produced by meson
The current work-around is to use `-fno-sized-deallocation` see https://github.com/scikit-learn/scikit-learn/pull/28506#discussion_r1512897297 for more details.
This can be reproduced locally with cibuildwheel.
```
python -m cibuil... | 28,596 | [
-0.001201785751618445,
-0.009107249788939953,
0.011677918955683708,
0.0012055777478963137,
0.07178542762994766,
0.05643514543771744,
0.013370944187045097,
-0.009098263457417488,
-0.03458412364125252,
-0.0036985697224736214,
0.04260619357228279,
0.08888973295688629,
-0.03627600520849228,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28596 | [
"Bug",
"Build / CI"
] | Missing _ZdlPv symbol in _argkmin_classmode for manylinux wheels produced by meson
The current work-around is to use `-fno-sized-deallocation` see https://github.com/scikit-learn/scikit-learn/pull/28506#discussion_r1512897297 for more details.
This can be reproduced locally with cibuildwheel.
```
python -m cibuil... | 28,596 | [
-0.001201785751618445,
-0.009107249788939953,
0.011677918955683708,
0.0012055777478963137,
0.07178542762994766,
0.05643514543771744,
0.013370944187045097,
-0.009098263457417488,
-0.03458412364125252,
-0.0036985697224736214,
0.04260619357228279,
0.08888973295688629,
-0.03627600520849228,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28596 | [
"Bug",
"Build / CI"
] | Missing _ZdlPv symbol in _argkmin_classmode for manylinux wheels produced by meson
The current work-around is to use `-fno-sized-deallocation` see https://github.com/scikit-learn/scikit-learn/pull/28506#discussion_r1512897297 for more details.
This can be reproduced locally with cibuildwheel.
```
python -m cibuil... | 28,596 | [
-0.001201785751618445,
-0.009107249788939953,
0.011677918955683708,
0.0012055777478963137,
0.07178542762994766,
0.05643514543771744,
0.013370944187045097,
-0.009098263457417488,
-0.03458412364125252,
-0.0036985697224736214,
0.04260619357228279,
0.08888973295688629,
-0.03627600520849228,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28587 | [
"Bug",
"Needs Triage"
] | `DecisionTreeClassifier` does not handle `Nan`
### Describe the bug
We implemented Decision Tree classifiers for a graduate course in Machine Learning. Part of my test suite compares the performance of my `DecisionTree` to the `sklearn.DecisionTreeClassifier` on the Iris dataset, with a specified amount of the data... | 28,587 | [
0.0049085733480751514,
0.03747549653053284,
0.002741375006735325,
-0.05825039744377136,
0.05654498562216759,
-0.022317415103316307,
0.011820681393146515,
0.04504416137933731,
-0.041021011769771576,
-0.002466842532157898,
0.04685036092996597,
0.039134856313467026,
0.037447232753038406,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.01631004363298416,
-0.01920485496520996,
-0.03312908113002777,
-0.01255617942661047,
0.0029430671129375696,
0.01164969801902771,
0.09967546164989471,
-0.06509151309728622,
-0.0689433142542839,
-0.01673237420618534,
0.03826243057847023,
-0.02466878853738308,
0.06027461588382721,
-0.032792... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.010039431042969227,
-0.020271072164177895,
-0.02918681502342224,
-0.006878203246742487,
-0.00157977978233248,
0.011676333844661713,
0.09667963534593582,
-0.06499841809272766,
-0.07019400596618652,
-0.015282807871699333,
0.029684346169233322,
-0.03485783189535141,
0.0626007467508316,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.005731281358748674,
-0.011649910360574722,
-0.030599793419241905,
-0.017051255330443382,
-0.0016695644007995725,
0.010203669779002666,
0.09012549370527267,
-0.0612468458712101,
-0.07210741937160492,
-0.01978827640414238,
0.035865556448698044,
-0.033332422375679016,
0.057658351957798004,
... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.011637823656201363,
-0.008759113028645515,
-0.033253055065870285,
-0.01206781342625618,
0.0039347377605736256,
0.008794477209448814,
0.09338800609111786,
-0.06528351455926895,
-0.06494637578725815,
-0.01650131866335869,
0.03889436274766922,
-0.02193823829293251,
0.05765734240412712,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.010564700700342655,
-0.02288198471069336,
-0.03147362172603607,
-0.019660310819745064,
-0.00461692176759243,
0.006783338729292154,
0.09594129025936127,
-0.05997433885931969,
-0.06663968414068222,
-0.019663330167531967,
0.03425834700465202,
-0.030865749344229698,
0.058311861008405685,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.014217478223145008,
-0.0058497413992881775,
-0.027283677831292152,
-0.012453624978661537,
0.0074370806105434895,
0.014206668362021446,
0.09967097640037537,
-0.06731166690587997,
-0.07234442979097366,
-0.015900075435638428,
0.029649537056684494,
-0.03276174142956734,
0.06127786263823509,
... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.008720273151993752,
-0.019432438537478447,
-0.02974645234644413,
-0.001202214160002768,
-0.0026898880023509264,
0.006928077898919582,
0.09462611377239227,
-0.05619605630636215,
-0.06774944812059402,
-0.013726125471293926,
0.03212776035070419,
-0.024980423972010612,
0.05562339350581169,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.01687001623213291,
-0.003621222684159875,
-0.03414957970380783,
-0.012248318642377853,
-0.000663979328237474,
0.009415538050234318,
0.0879196971654892,
-0.061300069093704224,
-0.071680948138237,
-0.02146490104496479,
0.032340578734874725,
-0.02755325846374035,
0.05230103060603142,
-0.036... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.020556557923555374,
-0.015868576243519783,
-0.0272519588470459,
-0.01845272071659565,
-0.00031227312865667045,
0.0061998493038117886,
0.0835573822259903,
-0.04954802989959717,
-0.07007568329572678,
-0.02310183085501194,
0.02557000331580639,
-0.03283189237117767,
0.0568326860666275,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.013655368238687515,
-0.018293125554919243,
-0.03195352852344513,
-0.015069672837853432,
0.007059734780341387,
0.010553274303674698,
0.09590156376361847,
-0.05940613895654678,
-0.07144851982593536,
-0.019482215866446495,
0.03914208710193634,
-0.020763445645570755,
0.06197312846779823,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.00682585034519434,
-0.00662941625341773,
-0.032087188214063644,
-0.009569353424012661,
-0.0010415614815428853,
0.014854650013148785,
0.08547237515449524,
-0.0624726302921772,
-0.07551512122154236,
-0.023081699386239052,
0.03554021939635277,
-0.03114805370569229,
0.049081601202487946,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28585 | [
"Documentation"
] | Macro vs micro-averaging switched up in user guide
### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#roc-curve-using-micro-averaged-ovr)
it says:
"In a multi-class cl... | 28,585 | [
0.018787413835525513,
-0.017886951565742493,
-0.03241928294301033,
-0.013158751651644707,
0.0023861743975430727,
0.009488443844020367,
0.09946926683187485,
-0.06286939978599548,
-0.07092121243476868,
-0.01937081664800644,
0.03858107700943947,
-0.02489544078707695,
0.06196224316954613,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.029822273179888725,
-0.01501079648733139,
0.0009537755977362394,
0.024372780695557594,
0.04186079651117325,
0.012483550235629082,
-0.0118626793846488,
-0.02992001362144947,
-0.020807763561606407,
-0.01891225390136242,
0.08376544713973999,
0.03895646333694458,
0.02712921053171158,
0.01396... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.027510100975632668,
-0.013953655026853085,
-0.000043126627133460715,
0.024986308068037033,
0.04079888388514519,
0.011673376895487309,
-0.010475789196789265,
-0.03145979344844818,
-0.01960030198097229,
-0.01765133999288082,
0.08376827090978622,
0.039567600935697556,
0.022691000252962112,
... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.02900257706642151,
-0.012158808298408985,
-0.00028417675639502704,
0.024248706176877022,
0.04088324308395386,
0.012973755598068237,
-0.007607543841004372,
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-0.019196974113583565,
0.08417525887489319,
0.03863787278532982,
0.026099758222699165,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.0290420800447464,
-0.011454527266323566,
0.0004809624224435538,
0.02429712563753128,
0.04081416502594948,
0.008798981085419655,
-0.016931850463151932,
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-0.01992669142782688,
0.08309712260961533,
0.04015940800309181,
0.023804087191820145,
0.0198... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.03920435532927513,
-0.022412899881601334,
0.003707027295604348,
0.025083858519792557,
0.039929721504449844,
0.006304666865617037,
-0.0012174133444204926,
-0.02810971438884735,
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-0.014219320379197598,
0.08604594320058823,
0.030071092769503593,
0.03447034955024719,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28580 | [
"Documentation"
] | RFECV docstring does not state how the `cv_results_` attribute is ordered by
### Describe the issue linked to the documentation
[This StackOverflow post](https://stackoverflow.com/questions/78111803/how-is-scikit-learns-rfecv-cv-results-attribute-ordered-by) has more details regarding this small issue.
In essence,... | 28,580 | [
0.028554042801260948,
-0.01081331167370081,
0.0019215474603697658,
0.02505665086209774,
0.03994029015302658,
0.01053227111697197,
-0.013090157881379128,
-0.03258202224969864,
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-0.01844991371035576,
0.08497767150402069,
0.03700930252671242,
0.0257301926612854,
0.014474... |
https://github.com/scikit-learn/scikit-learn/issues/28575 | [
"Bug",
"Needs Triage"
] | GridSearchCV do not weight the score by the size of the fold when providing custom split for CV
### Describe the bug
When providing an iterable for the `cv` arguments for GridSearchCV, if the splits have different size (as it can be the case when doing "leave one group out") the "best" score computed at the end is ... | 28,575 | [
-0.02431817539036274,
-0.016462434083223343,
0.015699682757258415,
0.009588675573468208,
0.038184117525815964,
-0.03641420975327492,
0.04385765269398689,
0.024677008390426636,
0.06181661784648895,
-0.02316517010331154,
0.020924963057041168,
0.03777007386088371,
0.0034217429347336292,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/28575 | [
"Bug",
"Needs Triage"
] | GridSearchCV do not weight the score by the size of the fold when providing custom split for CV
### Describe the bug
When providing an iterable for the `cv` arguments for GridSearchCV, if the splits have different size (as it can be the case when doing "leave one group out") the "best" score computed at the end is ... | 28,575 | [
-0.02431817539036274,
-0.016462434083223343,
0.015699682757258415,
0.009588675573468208,
0.038184117525815964,
-0.03641420975327492,
0.04385765269398689,
0.024677008390426636,
0.06181661784648895,
-0.02316517010331154,
0.020924963057041168,
0.03777007386088371,
0.0034217429347336292,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/28575 | [
"Bug",
"Needs Triage"
] | GridSearchCV do not weight the score by the size of the fold when providing custom split for CV
### Describe the bug
When providing an iterable for the `cv` arguments for GridSearchCV, if the splits have different size (as it can be the case when doing "leave one group out") the "best" score computed at the end is ... | 28,575 | [
-0.02431817539036274,
-0.016462434083223343,
0.015699682757258415,
0.009588675573468208,
0.038184117525815964,
-0.03641420975327492,
0.04385765269398689,
0.024677008390426636,
0.06181661784648895,
-0.02316517010331154,
0.020924963057041168,
0.03777007386088371,
0.0034217429347336292,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/28575 | [
"Bug",
"Needs Triage"
] | GridSearchCV do not weight the score by the size of the fold when providing custom split for CV
### Describe the bug
When providing an iterable for the `cv` arguments for GridSearchCV, if the splits have different size (as it can be the case when doing "leave one group out") the "best" score computed at the end is ... | 28,575 | [
-0.02431817539036274,
-0.016462434083223343,
0.015699682757258415,
0.009588675573468208,
0.038184117525815964,
-0.03641420975327492,
0.04385765269398689,
0.024677008390426636,
0.06181661784648895,
-0.02316517010331154,
0.020924963057041168,
0.03777007386088371,
0.0034217429347336292,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/28575 | [
"Bug",
"Needs Triage"
] | GridSearchCV do not weight the score by the size of the fold when providing custom split for CV
### Describe the bug
When providing an iterable for the `cv` arguments for GridSearchCV, if the splits have different size (as it can be the case when doing "leave one group out") the "best" score computed at the end is ... | 28,575 | [
-0.02431817539036274,
-0.016462434083223343,
0.015699682757258415,
0.009588675573468208,
0.038184117525815964,
-0.03641420975327492,
0.04385765269398689,
0.024677008390426636,
0.06181661784648895,
-0.02316517010331154,
0.020924963057041168,
0.03777007386088371,
0.0034217429347336292,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
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0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
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0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
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0.022272448986768723,
-0.027812005952000618,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
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0.020672334358096123,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.01825973019003868,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
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-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.01825973019003868,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
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-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
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-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/28574 | [
"New Feature",
"Moderate",
"help wanted",
"module:calibration"
] | Implement temperature scaling for (multi-class) calibration
### Describe the workflow you want to enable
It would be great to have temperature scaling available as a post-hoc calibration method for binary and multi-class classifiers, for example in `CalibratedClassifierCV`.
### Describe your proposed solution
Tempe... | 28,574 | [
-0.0508379302918911,
0.001938972738571465,
0.022272448986768723,
-0.027812005952000618,
0.020672334358096123,
0.012459270656108856,
0.028084689751267433,
0.04559094086289406,
0.02649819664657116,
-0.01825973019003868,
-0.06802821904420853,
-0.005693341139703989,
0.03599292412400246,
0.0001... |
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