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/30512 | [
"Bug"
] | Fail to pickle `SplineTransformer` with `scipy==1.15.0rc1`
### Describe the bug
Spotted in scikit-lego, running `check_estimators_pickle` fails with `SplineTransformer` and `readonly_memmap=True`.
cc: @koaning
### Steps/Code to Reproduce
```py
from sklearn.utils.estimator_checks import check_estimators_pickle
... | 30,512 | [
-0.00935110542923212,
0.012634733691811562,
0.005591660272330046,
-0.012229752726852894,
0.04452751576900482,
-0.034950096160173416,
0.029711244627833366,
0.02920880727469921,
0.06403571367263794,
0.007595106493681669,
0.042392030358314514,
0.06975366920232773,
0.03276006877422333,
0.08609... |
https://github.com/scikit-learn/scikit-learn/issues/30512 | [
"Bug"
] | Fail to pickle `SplineTransformer` with `scipy==1.15.0rc1`
### Describe the bug
Spotted in scikit-lego, running `check_estimators_pickle` fails with `SplineTransformer` and `readonly_memmap=True`.
cc: @koaning
### Steps/Code to Reproduce
```py
from sklearn.utils.estimator_checks import check_estimators_pickle
... | 30,512 | [
-0.00935110542923212,
0.012634733691811562,
0.005591660272330046,
-0.012229752726852894,
0.04452751576900482,
-0.034950096160173416,
0.029711244627833366,
0.02920880727469921,
0.06403571367263794,
0.007595106493681669,
0.042392030358314514,
0.06975366920232773,
0.03276006877422333,
0.08609... |
https://github.com/scikit-learn/scikit-learn/issues/30512 | [
"Bug"
] | Fail to pickle `SplineTransformer` with `scipy==1.15.0rc1`
### Describe the bug
Spotted in scikit-lego, running `check_estimators_pickle` fails with `SplineTransformer` and `readonly_memmap=True`.
cc: @koaning
### Steps/Code to Reproduce
```py
from sklearn.utils.estimator_checks import check_estimators_pickle
... | 30,512 | [
-0.00935110542923212,
0.012634733691811562,
0.005591660272330046,
-0.012229752726852894,
0.04452751576900482,
-0.034950096160173416,
0.029711244627833366,
0.02920880727469921,
0.06403571367263794,
0.007595106493681669,
0.042392030358314514,
0.06975366920232773,
0.03276006877422333,
0.08609... |
https://github.com/scikit-learn/scikit-learn/issues/30512 | [
"Bug"
] | Fail to pickle `SplineTransformer` with `scipy==1.15.0rc1`
### Describe the bug
Spotted in scikit-lego, running `check_estimators_pickle` fails with `SplineTransformer` and `readonly_memmap=True`.
cc: @koaning
### Steps/Code to Reproduce
```py
from sklearn.utils.estimator_checks import check_estimators_pickle
... | 30,512 | [
-0.00935110542923212,
0.012634733691811562,
0.005591660272330046,
-0.012229752726852894,
0.04452751576900482,
-0.034950096160173416,
0.029711244627833366,
0.02920880727469921,
0.06403571367263794,
0.007595106493681669,
0.042392030358314514,
0.06975366920232773,
0.03276006877422333,
0.08609... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.005370901431888342,
-0.025878628715872765,
-0.0006000932189635932,
-0.019446631893515587,
0.055480826646089554,
0.004560702480375767,
-0.000639411446172744,
0.05483821779489517,
0.019335322082042694,
-0.009057474322617054,
0.013565299101173878,
0.028208374977111816,
-0.02545115537941456,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.03293571621179581,
-0.04345298558473587,
-0.00925588421523571,
0.008493643254041672,
0.03392253816127777,
-0.0030906677711755037,
-0.01621069759130478,
0.04873989522457123,
-0.01393745094537735,
0.012668323703110218,
0.02325010672211647,
0.01791336201131344,
0.01058724895119667,
0.04057... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.012812385335564613,
-0.0015636748867109418,
-0.00793981272727251,
-0.04788677766919136,
0.029738672077655792,
-0.004192837979644537,
0.014227624982595444,
0.050996750593185425,
0.012106328271329403,
0.003806494642049074,
0.047129031270742416,
0.01453475933521986,
-0.021895892918109894,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.015112162567675114,
-0.023568347096443176,
-0.016586998477578163,
-0.04333708435297012,
0.036802828311920166,
-0.0022196865174919367,
0.006846338510513306,
0.06237124651670456,
0.017412610352039337,
0.01138210017234087,
0.046662766486406326,
0.021766938269138336,
-0.012953185476362705,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.009745175950229168,
-0.0370703861117363,
-0.015211641788482666,
-0.029697204008698463,
0.04524163901805878,
-0.012340893968939781,
-0.010766450315713882,
0.037315573543310165,
-0.005377486813813448,
0.005702352616935968,
0.04685023054480553,
-0.002391204470768571,
0.008635517209768295,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.027146805077791214,
0.002186715370044112,
-0.0017865517875179648,
0.008459681645035744,
0.030017033219337463,
0.01261600386351347,
-0.007154117804020643,
0.015356152318418026,
-0.027445543557405472,
-0.007513055577874184,
0.009029433131217957,
0.03260413184762001,
0.019548695534467697,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.03007771633565426,
-0.025567464530467987,
-0.007004235405474901,
-0.010710211470723152,
0.03868664801120758,
-0.0014056807849556208,
0.015826242044568062,
0.03908389061689377,
0.00010024707444244996,
-0.006945861969143152,
0.010301330126821995,
0.012369523756206036,
-0.006243680603802204,... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.018973005935549736,
-0.0017213074024766684,
-0.020178010687232018,
-0.046659648418426514,
0.041967201977968216,
-0.0019578414503484964,
0.011836833320558071,
0.05995815992355347,
0.004854266531765461,
0.014381187967956066,
0.05241946130990982,
0.020625226199626923,
-0.02453581616282463,
... |
https://github.com/scikit-learn/scikit-learn/issues/30509 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Dec 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73034&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 22, 2024)
- test_euclidean_distances... | 30,509 | [
-0.021961243823170662,
0.0012651786673814058,
-0.008734678849577904,
-0.0199753288179636,
0.018211809918284416,
-0.011012373492121696,
0.021633228287100792,
0.05348508059978485,
0.012253837659955025,
-0.010640936903655529,
0.023845069110393524,
-0.00696591567248106,
0.00038412268622778356,
... |
https://github.com/scikit-learn/scikit-learn/issues/30507 | [
"Needs Triage"
] | Sensitivity Analysis with Random Forest Moel
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/19112
<div type='discussions-op-text'>
<sup>Originally posted by **lesteve** January 5, 2021</sup>
## 👋 Welcome!
We’re using Discussions as a place to connect with other members of our c... | 30,507 | [
-0.024634692817926407,
0.017224593088030815,
0.0154835544526577,
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0.007085559889674187,
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0.05570204183459282,
0.018557410687208176,
0.022115632891654968,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30507 | [
"Needs Triage"
] | Sensitivity Analysis with Random Forest Moel
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/19112
<div type='discussions-op-text'>
<sup>Originally posted by **lesteve** January 5, 2021</sup>
## 👋 Welcome!
We’re using Discussions as a place to connect with other members of our c... | 30,507 | [
0.011046183295547962,
0.04125332459807396,
0.022523073479533195,
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0.009775211103260517,
0.005320338066667318,
0.018478479236364365,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30507 | [
"Needs Triage"
] | Sensitivity Analysis with Random Forest Moel
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/19112
<div type='discussions-op-text'>
<sup>Originally posted by **lesteve** January 5, 2021</sup>
## 👋 Welcome!
We’re using Discussions as a place to connect with other members of our c... | 30,507 | [
-0.009913896210491657,
0.029924586415290833,
0.008098648861050606,
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0.015849050134420395,
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0.04762125760316849,
0.03385353460907936,
0.01642734557390213,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30507 | [
"Needs Triage"
] | Sensitivity Analysis with Random Forest Moel
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/19112
<div type='discussions-op-text'>
<sup>Originally posted by **lesteve** January 5, 2021</sup>
## 👋 Welcome!
We’re using Discussions as a place to connect with other members of our c... | 30,507 | [
-0.010425509884953499,
0.02627873793244362,
0.008968538604676723,
-0.0005873535992577672,
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0.021977895870804787,
0.010569552890956402,
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0.052503813058137894,
0.03340606018900871,
0.013392736203968525,
... |
https://github.com/scikit-learn/scikit-learn/issues/30503 | [
"Documentation"
] | Mention setting env variable SCIPY_ARRAY_API=1 in Array API support doc
### Describe the issue linked to the documentation
https://scikit-learn.org/dev/modules/array_api.html#array-api-support-experimental does not mention `SCIPY_ARRAY_API=1`
### Suggest a potential alternative/fix
Maybe it should mention s... | 30,503 | [
0.016355203464627266,
0.023520106449723244,
-0.000991365290246904,
-0.006027427967637777,
0.05777529254555702,
0.04502987861633301,
0.08121874183416367,
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0.05565422400832176,
0.004703842103481293,
0.03458784520626068,
0.08123088628053665,
-0.02679440751671791,
0.0028... |
https://github.com/scikit-learn/scikit-learn/issues/30503 | [
"Documentation"
] | Mention setting env variable SCIPY_ARRAY_API=1 in Array API support doc
### Describe the issue linked to the documentation
https://scikit-learn.org/dev/modules/array_api.html#array-api-support-experimental does not mention `SCIPY_ARRAY_API=1`
### Suggest a potential alternative/fix
Maybe it should mention s... | 30,503 | [
0.027217010036110878,
0.02844107896089554,
0.007272083777934313,
-0.025715261697769165,
0.05733226239681244,
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0.019781583920121193,
0.03487325832247734,
0.10647042095661163,
-0.029227090999484062,
0.062877... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.006047616712749004,
0.049472011625766754,
0.007565111853182316,
0.032573796808719635,
0.022130263969302177,
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0.08997604995965958,
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0.004721812903881073,
-0.0027263120282441378,
0.047945376485586166,
0.04056146740913391,
0.023244697600603104,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.041315604001283646,
0.029730437323451042,
0.010886100120842457,
0.03979642316699028,
0.0326448455452919,
0.02000669576227665,
0.0550539493560791,
0.08313779532909393,
-0.020448654890060425,
-0.014948757365345955,
0.02744416333734989,
0.023066643625497818,
0.019014345481991768,
-0.0119881... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.03675687313079834,
0.03281659260392189,
0.015556829050183296,
0.042952850461006165,
0.02707243151962757,
0.017271090298891068,
0.06333648413419724,
0.07824738323688507,
-0.016318844631314278,
-0.012024147436022758,
0.024917811155319214,
0.0333036445081234,
0.017032235860824585,
-0.007996... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.020128097385168076,
0.029403716325759888,
0.007361515425145626,
0.03843575343489647,
0.024415837600827217,
0.016413358971476555,
0.06588581204414368,
0.06345272809267044,
-0.006518429610878229,
-0.015265947207808495,
0.03046507015824318,
0.039733100682497025,
0.023940280079841614,
0.0044... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.051223836839199066,
0.033966295421123505,
0.014499181881546974,
0.0411042720079422,
0.025335879996418953,
0.019132385030388832,
0.04673682898283005,
0.07487280666828156,
-0.015374593436717987,
-0.02449793368577957,
0.024146782234311104,
0.02354404143989086,
0.01968138851225376,
-0.039610... |
https://github.com/scikit-learn/scikit-learn/issues/30498 | [
"Bug"
] | `remainder='passthrough'` block is missing from `ColumnTransformer` HTML repr since 1.5
In the following example, the `repr` of `ColumnTransformer` does not seem to work as I expect it:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-ins... | 30,498 | [
0.017071764916181564,
0.03636596351861954,
0.011638650670647621,
0.03321577608585358,
0.023313933983445168,
0.013182209804654121,
0.09086497128009796,
0.06389877945184708,
0.008093824610114098,
-0.008692380972206593,
0.03352075442671776,
0.04894452542066574,
0.022343970835208893,
0.0142233... |
https://github.com/scikit-learn/scikit-learn/issues/30493 | [
"Bug",
"Needs Info"
] | DBSCAN AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am trying to use DBSCAN to do clustering on a normalized np.ndarray (571,128) named all_encodings.
I use VSCode on Mac M1.
### Steps/Code to Reproduce
```py
from sklearn.cluster import DBSCAN
all_encodings = normaliz... | 30,493 | [
-0.040666364133358,
-0.06913493573665619,
0.004498131573200226,
0.03831116110086441,
0.09878432005643845,
0.014990264549851418,
0.045117877423763275,
0.041773293167352676,
-0.008511712774634361,
-0.0015672605950385332,
0.021792428568005562,
-0.003347283462062478,
-0.003535391064360738,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30493 | [
"Bug",
"Needs Info"
] | DBSCAN AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am trying to use DBSCAN to do clustering on a normalized np.ndarray (571,128) named all_encodings.
I use VSCode on Mac M1.
### Steps/Code to Reproduce
```py
from sklearn.cluster import DBSCAN
all_encodings = normaliz... | 30,493 | [
-0.040666364133358,
-0.06913493573665619,
0.004498131573200226,
0.03831116110086441,
0.09878432005643845,
0.014990264549851418,
0.045117877423763275,
0.041773293167352676,
-0.008511712774634361,
-0.0015672605950385332,
0.021792428568005562,
-0.003347283462062478,
-0.003535391064360738,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30493 | [
"Bug",
"Needs Info"
] | DBSCAN AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am trying to use DBSCAN to do clustering on a normalized np.ndarray (571,128) named all_encodings.
I use VSCode on Mac M1.
### Steps/Code to Reproduce
```py
from sklearn.cluster import DBSCAN
all_encodings = normaliz... | 30,493 | [
-0.040666364133358,
-0.06913493573665619,
0.004498131573200226,
0.03831116110086441,
0.09878432005643845,
0.014990264549851418,
0.045117877423763275,
0.041773293167352676,
-0.008511712774634361,
-0.0015672605950385332,
0.021792428568005562,
-0.003347283462062478,
-0.003535391064360738,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30493 | [
"Bug",
"Needs Info"
] | DBSCAN AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am trying to use DBSCAN to do clustering on a normalized np.ndarray (571,128) named all_encodings.
I use VSCode on Mac M1.
### Steps/Code to Reproduce
```py
from sklearn.cluster import DBSCAN
all_encodings = normaliz... | 30,493 | [
-0.040666364133358,
-0.06913493573665619,
0.004498131573200226,
0.03831116110086441,
0.09878432005643845,
0.014990264549851418,
0.045117877423763275,
0.041773293167352676,
-0.008511712774634361,
-0.0015672605950385332,
0.021792428568005562,
-0.003347283462062478,
-0.003535391064360738,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30492 | [
"Documentation"
] | Version 1.6 docs inconsistency related to isolation forest.
### Describe the issue linked to the documentation
The current [isolation forest docs](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest) say this:
![image](https://github.com/user-a... | 30,492 | [
0.023197025060653687,
0.019672248512506485,
-0.004078743979334831,
0.039292436093091965,
-0.018396491184830666,
-0.022088851779699326,
0.06841099262237549,
-0.017559319734573364,
-0.017024340108036995,
-0.0010810246458277106,
0.04412446916103363,
-0.005940679926425219,
0.008306133560836315,
... |
https://github.com/scikit-learn/scikit-learn/issues/30492 | [
"Documentation"
] | Version 1.6 docs inconsistency related to isolation forest.
### Describe the issue linked to the documentation
The current [isolation forest docs](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest) say this:
![image](https://github.com/user-a... | 30,492 | [
0.0223996639251709,
0.03332706540822983,
-0.0030962168239057064,
0.02271747775375843,
-0.028169013559818268,
-0.03139780834317207,
0.06928881257772446,
-0.009255682118237019,
-0.01776740700006485,
-0.008176783099770546,
0.057854071259498596,
-0.01799621619284153,
0.014382909052073956,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30492 | [
"Documentation"
] | Version 1.6 docs inconsistency related to isolation forest.
### Describe the issue linked to the documentation
The current [isolation forest docs](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest) say this:
![image](https://github.com/user-a... | 30,492 | [
0.009400824084877968,
0.022204892709851265,
-0.00323821185156703,
0.03794329613447189,
-0.018514031544327736,
-0.028163503855466843,
0.06784038990736008,
-0.01437030453234911,
-0.02198098599910736,
-0.006798034068197012,
0.05327022448182106,
-0.00954496767371893,
0.004948345012962818,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30492 | [
"Documentation"
] | Version 1.6 docs inconsistency related to isolation forest.
### Describe the issue linked to the documentation
The current [isolation forest docs](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest) say this:
![image](https://github.com/user-a... | 30,492 | [
0.01929629035294056,
0.004159955773502588,
-0.005255823954939842,
0.0380268432199955,
-0.03774408623576164,
-0.025347255170345306,
0.06590359658002853,
-0.02316201850771904,
-0.030339673161506653,
-0.00524406973272562,
0.047727134078741074,
-0.013385104946792126,
0.020307408645749092,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30479 | [
"Bug",
"Regression"
] | Version 1.6.X: ClassifierMixIn failing with new __sklearn_tags__ function
### Describe the bug
Hi,
we are using Sklearn in our projects for different classification training methods on production level. In the dev stage we upgraded to the latest release and our Training failed due to changes in the ClassifierMix... | 30,479 | [
0.02825416438281536,
-0.006028575822710991,
0.021397871896624565,
-0.033552855253219604,
0.08143521845340729,
-0.0032951459288597107,
0.03760349005460739,
0.04327806830406189,
0.08522173762321472,
-0.030934322625398636,
0.023908454924821854,
0.09351341426372528,
-0.03826251998543739,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30478 | [
"Bug",
"Needs Triage"
] | `SVC` incorrectly swaps the weights for the positive and negative classes
### Describe the bug
See the example below. `C` is set to `100`, so with the class weights applied, `C` should be `100` for the positive class and `C` should be `50` for the negative class. But after adding some logging, we can see the `Cp` and... | 30,478 | [
0.021019142121076584,
-0.0660223513841629,
-0.010228503495454788,
0.0429455004632473,
0.06981657445430756,
-0.0009635004680603743,
-0.023089423775672913,
-0.025956500321626663,
-0.049222029745578766,
-0.0006854634266346693,
0.04259331151843071,
0.03508052974939346,
0.04400141164660454,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30478 | [
"Bug",
"Needs Triage"
] | `SVC` incorrectly swaps the weights for the positive and negative classes
### Describe the bug
See the example below. `C` is set to `100`, so with the class weights applied, `C` should be `100` for the positive class and `C` should be `50` for the negative class. But after adding some logging, we can see the `Cp` and... | 30,478 | [
0.021019142121076584,
-0.0660223513841629,
-0.010228503495454788,
0.0429455004632473,
0.06981657445430756,
-0.0009635004680603743,
-0.023089423775672913,
-0.025956500321626663,
-0.049222029745578766,
-0.0006854634266346693,
0.04259331151843071,
0.03508052974939346,
0.04400141164660454,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30477 | [
"New Feature"
] | Add missing value support for AdaBoost?
### Describe the bug
I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator.
The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well... | 30,477 | [
-0.013366053812205791,
0.033624254167079926,
0.015597112476825714,
-0.042538389563560486,
0.06728816032409668,
-0.02335335873067379,
0.022533157840371132,
-0.012715090997517109,
-0.036127686500549316,
0.012605085968971252,
0.042849328368902206,
0.006013893987983465,
0.05381712317466736,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30470 | [
"New Feature",
"Needs Triage"
] | How can I obtain the explained variance for each latent component in PLS?
### Describe the workflow you want to enable
How can I further obtain the explained variance for each latent component in PLS using **sklearn.cross_decomposition.PLSRegression**?
### Describe your proposed solution
I need proposed solution
#... | 30,470 | [
-0.01302365679293871,
-0.0231204554438591,
0.01030596811324358,
0.014711468480527401,
0.038171276450157166,
-0.00020735716680064797,
0.0647771805524826,
-0.02464236505329609,
-0.05596446245908737,
0.013993567787110806,
0.0321420356631279,
0.0705399215221405,
0.02205580100417137,
0.09516848... |
https://github.com/scikit-learn/scikit-learn/issues/30467 | [
"API",
"RFC"
] | API Deprecate n_alphas in LinearModelCV
In LassoCV, ElasticNetCV, ... we have two parameters, `alphas` and `n_alphas`, that have the same purpose, i.e. determine the alpha values to test.
I'd be in favor of deprecating `n_alphas` and make `alphas` accept either an int or an array-like, filling both roles.
I chos... | 30,467 | [
-0.01715707965195179,
0.07428862899541855,
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0.021241387352347374,
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0.06753109395503998,
0.044882435351610184,
-0.05313462018966675,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/30467 | [
"API",
"RFC"
] | API Deprecate n_alphas in LinearModelCV
In LassoCV, ElasticNetCV, ... we have two parameters, `alphas` and `n_alphas`, that have the same purpose, i.e. determine the alpha values to test.
I'd be in favor of deprecating `n_alphas` and make `alphas` accept either an int or an array-like, filling both roles.
I chos... | 30,467 | [
-0.015584222972393036,
0.07641877233982086,
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0.006913117598742247,
0.04746101051568985,
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0.04320641607046127,
0.04008963704109192,
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0.0794660672545433,
0.04143188148736954,
-0.04371741786599159,
0.0498892... |
https://github.com/scikit-learn/scikit-learn/issues/30467 | [
"API",
"RFC"
] | API Deprecate n_alphas in LinearModelCV
In LassoCV, ElasticNetCV, ... we have two parameters, `alphas` and `n_alphas`, that have the same purpose, i.e. determine the alpha values to test.
I'd be in favor of deprecating `n_alphas` and make `alphas` accept either an int or an array-like, filling both roles.
I chos... | 30,467 | [
-0.02296357974410057,
0.06986428797245026,
-0.01828085631132126,
0.015343643724918365,
0.047117456793785095,
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0.03197365254163742,
0.03737132251262665,
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0.07641837745904922,
0.03921256214380264,
-0.048669472336769104,
0.0423493... |
https://github.com/scikit-learn/scikit-learn/issues/30464 | [
"Bug",
"Needs Triage"
] | ColumnTransformer raises a TypeError when used in a Pipeline
### Describe the bug
`ColumnTransformer` raises error _ColumnTransformer is subscriptable after it is fitted_ when used in a `Pipeline`.
This happens when the arguments to expected methods are gathered in `Pipeline._check_method_params`: destructuring a ... | 30,464 | [
-0.006448735017329454,
0.044991929084062576,
0.02455519326031208,
-0.04595494270324707,
0.07267487049102783,
0.020187992602586746,
0.056161705404520035,
0.01852700300514698,
0.005013815127313137,
0.024261925369501114,
0.0386013425886631,
-0.009259866550564766,
0.02023172751069069,
-0.00124... |
https://github.com/scikit-learn/scikit-learn/issues/30464 | [
"Bug",
"Needs Triage"
] | ColumnTransformer raises a TypeError when used in a Pipeline
### Describe the bug
`ColumnTransformer` raises error _ColumnTransformer is subscriptable after it is fitted_ when used in a `Pipeline`.
This happens when the arguments to expected methods are gathered in `Pipeline._check_method_params`: destructuring a ... | 30,464 | [
-0.006448735017329454,
0.044991929084062576,
0.02455519326031208,
-0.04595494270324707,
0.07267487049102783,
0.020187992602586746,
0.056161705404520035,
0.01852700300514698,
0.005013815127313137,
0.024261925369501114,
0.0386013425886631,
-0.009259866550564766,
0.02023172751069069,
-0.00124... |
https://github.com/scikit-learn/scikit-learn/issues/30464 | [
"Bug",
"Needs Triage"
] | ColumnTransformer raises a TypeError when used in a Pipeline
### Describe the bug
`ColumnTransformer` raises error _ColumnTransformer is subscriptable after it is fitted_ when used in a `Pipeline`.
This happens when the arguments to expected methods are gathered in `Pipeline._check_method_params`: destructuring a ... | 30,464 | [
-0.006448735017329454,
0.044991929084062576,
0.02455519326031208,
-0.04595494270324707,
0.07267487049102783,
0.020187992602586746,
0.056161705404520035,
0.01852700300514698,
0.005013815127313137,
0.024261925369501114,
0.0386013425886631,
-0.009259866550564766,
0.02023172751069069,
-0.00124... |
https://github.com/scikit-learn/scikit-learn/issues/30461 | [
"Bug",
"Needs Triage"
] | from sklearn.datasets import make_regression FileNotFoundError
### Describe the bug
When running examples/application/plot_prediction_latency.py a FileNotFoundError occurs as there is no file named make_regression in datasets dir.
I have cloned the scikit-learn repo and installed it using ```pip install -e .```
... | 30,461 | [
0.027242515236139297,
0.051779694855213165,
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0.056031860411167145,
0.022009309381246567,
0.0760670080780983,
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0.060016632080078125,
0.031035497784614563,
0.02376990020275116,
0.07214879244565964,
-0.0037373888771981,
0.04575... |
https://github.com/scikit-learn/scikit-learn/issues/30457 | [
"New Feature",
"Needs Info"
] | Add checking if tree criterion/splitter are classes
### Describe the workflow you want to enable
In the process of creating custom splitters, criterions & models that inherit from the respective _scikit-learn_ classes, a very convenient (albeit currently impossible) solution is to add the splitter & criterion class... | 30,457 | [
0.005699428729712963,
0.05500996485352516,
0.02395426668226719,
0.040818952023983,
0.010702804662287235,
-0.03442971780896187,
0.0006421316065825522,
0.01524139940738678,
-0.012679928913712502,
-0.08305121213197708,
0.00913381576538086,
0.019791286438703537,
-0.038714416325092316,
0.052117... |
https://github.com/scikit-learn/scikit-learn/issues/30457 | [
"New Feature",
"Needs Info"
] | Add checking if tree criterion/splitter are classes
### Describe the workflow you want to enable
In the process of creating custom splitters, criterions & models that inherit from the respective _scikit-learn_ classes, a very convenient (albeit currently impossible) solution is to add the splitter & criterion class... | 30,457 | [
-0.00863844994455576,
0.04587370902299881,
0.018515698611736298,
0.04394887760281563,
0.021108586341142654,
-0.031969837844371796,
-0.0057695358991622925,
0.01921696588397026,
0.0024023842997848988,
-0.06840971857309341,
0.011519990861415863,
0.023286061361432076,
-0.04371258243918419,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30452 | [
"New Feature"
] | Multiple thresholds in FixedThresholdClassifier
### Describe the workflow you want to enable
Currently FixedThresholdClassifier only allows for a unique threshold as a float. It would be nicer to be also able to accept a list of floats and that multiple classes would be produced accordingly.
### Describe your p... | 30,452 | [
-0.044461384415626526,
0.037467531859874725,
-0.00043917386210523546,
-0.036544319242239,
0.04621125012636185,
-0.028501519933342934,
0.0662800520658493,
0.012234741821885109,
-0.0424116849899292,
-0.03231240063905716,
0.013379687443375587,
0.020016374066472054,
-0.01395482663065195,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/30452 | [
"New Feature"
] | Multiple thresholds in FixedThresholdClassifier
### Describe the workflow you want to enable
Currently FixedThresholdClassifier only allows for a unique threshold as a float. It would be nicer to be also able to accept a list of floats and that multiple classes would be produced accordingly.
### Describe your p... | 30,452 | [
-0.04439032822847366,
0.04689902067184448,
0.004871292971074581,
-0.028253136202692986,
0.047369908541440964,
-0.022786898538470268,
0.06953556090593338,
0.010327478870749474,
-0.039881009608507156,
-0.0326356403529644,
0.01621619239449501,
0.021380359306931496,
-0.02207363024353981,
0.048... |
https://github.com/scikit-learn/scikit-learn/issues/30452 | [
"New Feature"
] | Multiple thresholds in FixedThresholdClassifier
### Describe the workflow you want to enable
Currently FixedThresholdClassifier only allows for a unique threshold as a float. It would be nicer to be also able to accept a list of floats and that multiple classes would be produced accordingly.
### Describe your p... | 30,452 | [
-0.04707436263561249,
0.03572896867990494,
0.002004323760047555,
-0.0359610952436924,
0.04620968550443649,
-0.02637275867164135,
0.06437405198812485,
0.006212330423295498,
-0.040282655507326126,
-0.028933316469192505,
0.017940150573849678,
0.022154774516820908,
-0.022870367392897606,
0.060... |
https://github.com/scikit-learn/scikit-learn/issues/30452 | [
"New Feature"
] | Multiple thresholds in FixedThresholdClassifier
### Describe the workflow you want to enable
Currently FixedThresholdClassifier only allows for a unique threshold as a float. It would be nicer to be also able to accept a list of floats and that multiple classes would be produced accordingly.
### Describe your p... | 30,452 | [
-0.0462532676756382,
0.039582327008247375,
0.0027448146138340235,
-0.03314786031842232,
0.045111291110515594,
-0.027491889894008636,
0.062138061970472336,
0.005278877913951874,
-0.033872850239276886,
-0.029784047976136208,
0.01426924578845501,
0.030796747654676437,
-0.03094855509698391,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30450 | [
"Bug"
] | Scikit-learn v1.6.0 breaks SelectFromModel when using a non-sklearn model
### Describe the bug
There seem to be a bug introduced by v1.6.0 where the SelectFromModel must use a model for which the parent class also has a `__sklearn_tags__` method. This works with sklearn models but not with 3rd party models using a sk... | 30,450 | [
0.03469286113977432,
0.036605704575777054,
0.018721478059887886,
-0.03584568575024605,
0.05125494301319122,
-0.022695764899253845,
0.045416466891765594,
0.03430371358990669,
0.08876984566450119,
-0.03298225998878479,
0.027664057910442352,
0.0695725828409195,
0.011839841492474079,
0.0520848... |
https://github.com/scikit-learn/scikit-learn/issues/30450 | [
"Bug"
] | Scikit-learn v1.6.0 breaks SelectFromModel when using a non-sklearn model
### Describe the bug
There seem to be a bug introduced by v1.6.0 where the SelectFromModel must use a model for which the parent class also has a `__sklearn_tags__` method. This works with sklearn models but not with 3rd party models using a sk... | 30,450 | [
0.03469286113977432,
0.036605704575777054,
0.018721478059887886,
-0.03584568575024605,
0.05125494301319122,
-0.022695764899253845,
0.045416466891765594,
0.03430371358990669,
0.08876984566450119,
-0.03298225998878479,
0.027664057910442352,
0.0695725828409195,
0.011839841492474079,
0.0520848... |
https://github.com/scikit-learn/scikit-learn/issues/30450 | [
"Bug"
] | Scikit-learn v1.6.0 breaks SelectFromModel when using a non-sklearn model
### Describe the bug
There seem to be a bug introduced by v1.6.0 where the SelectFromModel must use a model for which the parent class also has a `__sklearn_tags__` method. This works with sklearn models but not with 3rd party models using a sk... | 30,450 | [
0.03469286113977432,
0.036605704575777054,
0.018721478059887886,
-0.03584568575024605,
0.05125494301319122,
-0.022695764899253845,
0.045416466891765594,
0.03430371358990669,
0.08876984566450119,
-0.03298225998878479,
0.027664057910442352,
0.0695725828409195,
0.011839841492474079,
0.0520848... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.019845012575387955,
0.03841209411621094,
0.03442547470331192,
-0.0001347329089185223,
0.035325661301612854,
-0.005385932512581348,
0.03273287042975426,
0.03909061476588249,
0.06410950422286987,
-0.032604992389678955,
0.04838068038225174,
0.06647726893424988,
0.015196788124740124,
-0.013... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.01520376093685627,
0.05170833319425583,
0.03388853743672371,
-0.0002914624637924135,
0.03671939671039581,
-0.004070156253874302,
0.03609364107251167,
0.037692051380872726,
0.05951782688498497,
-0.022514062002301216,
0.04516401141881943,
0.05863543972373009,
0.0023067111615091562,
-0.002... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.025699736550450325,
0.034811556339263916,
0.03227677941322327,
0.011977029033005238,
0.041182734072208405,
-0.0022822546306997538,
0.0259786918759346,
0.03748045861721039,
0.06806855648756027,
-0.03454866260290146,
0.04122653231024742,
0.07864965498447418,
0.018410423770546913,
-0.00102... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.026045432314276695,
0.04150236025452614,
0.03739849850535393,
0.010436083190143108,
0.041306037455797195,
-0.0013219376560300589,
0.02828706055879593,
0.03458492085337639,
0.07260623574256897,
-0.03186947479844093,
0.035117946565151215,
0.07878612726926804,
0.02527870610356331,
-0.00081... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.01158312987536192,
0.026979638263583183,
0.03549940511584282,
0.010923569090664387,
0.02155563049018383,
-0.008653104305267334,
0.04150870069861412,
0.035514406859874725,
0.05567349120974541,
-0.04133656993508339,
0.034529928117990494,
0.05741608142852783,
0.010155603289604187,
0.002837... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.010154973715543747,
0.051516856998205185,
0.03897299990057945,
0.0011427785502746701,
0.03590724989771843,
-0.0024710304569453,
0.038957733660936356,
0.03261474892497063,
0.07386809587478638,
-0.025969648733735085,
0.04117356613278389,
0.07675637304782867,
0.01284602377563715,
-0.022459... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.010121949948370457,
0.048202868551015854,
0.041297122836112976,
-0.009422157891094685,
0.032009005546569824,
-0.014913118444383144,
0.03675335273146629,
0.022784177213907242,
0.035779453814029694,
-0.04003771394491196,
0.03665823116898537,
0.08066926896572113,
0.01722789742052555,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/30449 | [
"Bug"
] | duck typed estimators fail in check_estimator
### Describe the bug
I believe these 5 lines, which check for specific types:
https://github.com/scikit-learn/scikit-learn/blob/76ae0a539a0e87145c9f6fedcd7033494082fa17/sklearn/utils/estimator_checks.py#L4439-L4443
breaks the documentation in https://scikit-learn.or... | 30,449 | [
-0.013240517117083073,
0.0535447932779789,
0.044993843883275986,
-0.005378199741244316,
0.027771256864070892,
-0.005562750156968832,
0.03832602873444557,
0.036613184958696365,
0.07779556512832642,
-0.029521547257900238,
0.04262087121605873,
0.07506629824638367,
0.014226282946765423,
-0.008... |
https://github.com/scikit-learn/scikit-learn/issues/30447 | [
"Bug"
] | `cross_validate` raises an exception when metadata routing is enabled
### Describe the bug
In the latest release (v1.6.0), `cross_validate` raises an exception when using it with metadata routing enabled. This is because `params` dict gets unpacked even if `None`, which is the default value. See this line:
https:/... | 30,447 | [
0.005321442615240812,
-0.022075485438108444,
0.031832996755838394,
-0.005311689805239439,
0.08982601761817932,
-0.013885683380067348,
0.049809530377388,
0.017199017107486725,
-0.013242682442069054,
-0.03505100682377815,
0.01542806625366211,
0.08217353373765945,
0.006543281022459269,
-0.051... |
https://github.com/scikit-learn/scikit-learn/issues/30447 | [
"Bug"
] | `cross_validate` raises an exception when metadata routing is enabled
### Describe the bug
In the latest release (v1.6.0), `cross_validate` raises an exception when using it with metadata routing enabled. This is because `params` dict gets unpacked even if `None`, which is the default value. See this line:
https:/... | 30,447 | [
0.005321442615240812,
-0.022075485438108444,
0.031832996755838394,
-0.005311689805239439,
0.08982601761817932,
-0.013885683380067348,
0.049809530377388,
0.017199017107486725,
-0.013242682442069054,
-0.03505100682377815,
0.01542806625366211,
0.08217353373765945,
0.006543281022459269,
-0.051... |
https://github.com/scikit-learn/scikit-learn/issues/30445 | [
"Documentation"
] | DOC add FAQ link to scikit-learn course
### Describe the issue linked to the documentation
Given there are so many inquiries such as "How do I get started with scikit-learn?" let's add a resource to the FAQ here:
https://scikit-learn.org/stable/faq.html
resource:
https://inria.github.io/scikit-learn-mooc/append... | 30,445 | [
0.008001443929970264,
-0.05033623054623604,
-0.023757223039865494,
0.029830433428287506,
0.029984476044774055,
0.04967102035880089,
0.06503796577453613,
0.0046807341277599335,
0.03726132586598396,
-0.03307104483246803,
0.040816787630319595,
0.04128176346421242,
0.023331571370363235,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/30445 | [
"Documentation"
] | DOC add FAQ link to scikit-learn course
### Describe the issue linked to the documentation
Given there are so many inquiries such as "How do I get started with scikit-learn?" let's add a resource to the FAQ here:
https://scikit-learn.org/stable/faq.html
resource:
https://inria.github.io/scikit-learn-mooc/append... | 30,445 | [
0.009291351772844791,
-0.04975635185837746,
-0.013553456403315067,
0.024301016703248024,
0.020191390067338943,
0.05137958377599716,
0.06233910843729973,
0.004281685687601566,
0.038719698786735535,
-0.02929416485130787,
0.05488106235861778,
0.02282528206706047,
0.018427226692438126,
0.00595... |
https://github.com/scikit-learn/scikit-learn/issues/30442 | [
"New Feature"
] | Missing `inverse_transform` in `DictionaryLearning`and `SparseCoder`
### Describe the workflow you want to enable
The method is currently missing in those two classes which prevent doing a loop over all Linear decomposition methods when evaluation them for denoising for instance.
### Describe your proposed solution... | 30,442 | [
-0.006805896293371916,
0.056482914835214615,
0.03178262338042259,
0.00790499709546566,
0.029798950999975204,
0.0046910070814192295,
0.031011421233415604,
0.056913819164037704,
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0.0026857752818614244,
0.05543261021375656,
0.055337291210889816,
0.010167925618588924,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30430 | [
"Documentation"
] | Example of binning of continous variables for chi2
### Describe the issue linked to the documentation
The [chi2](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html) doesn't work on continuous variables. This issue has numerous discussions, e.g. [here](https://stats.stackexchange.com... | 30,430 | [
-0.059724003076553345,
0.03510931506752968,
-0.006274766754359007,
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0.033133335411548615,
0.06777147948741913,
0.0014322682982310653,
-0.05155192315578461,
0.04178687930107117,
0.04203224554657936,
0.009197988547384739,
0.0485834963619709,
0.1151... |
https://github.com/scikit-learn/scikit-learn/issues/30430 | [
"Documentation"
] | Example of binning of continous variables for chi2
### Describe the issue linked to the documentation
The [chi2](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html) doesn't work on continuous variables. This issue has numerous discussions, e.g. [here](https://stats.stackexchange.com... | 30,430 | [
-0.050930581986904144,
0.024110186845064163,
0.0011191369267180562,
-0.026573294773697853,
-0.029568083584308624,
0.04308796674013138,
0.056668177247047424,
0.012829795479774475,
-0.055566057562828064,
0.04340973496437073,
0.04122697561979294,
0.021303914487361908,
0.053177595138549805,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30430 | [
"Documentation"
] | Example of binning of continous variables for chi2
### Describe the issue linked to the documentation
The [chi2](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html) doesn't work on continuous variables. This issue has numerous discussions, e.g. [here](https://stats.stackexchange.com... | 30,430 | [
-0.05257797986268997,
0.020538372918963432,
-0.007518383674323559,
-0.026457227766513824,
-0.023420201614499092,
0.03431401774287224,
0.040489815175533295,
0.019015785306692123,
-0.03893743455410004,
0.023049334064126015,
0.02462242916226387,
0.020370930433273315,
0.05660188943147659,
0.12... |
https://github.com/scikit-learn/scikit-learn/issues/30430 | [
"Documentation"
] | Example of binning of continous variables for chi2
### Describe the issue linked to the documentation
The [chi2](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html) doesn't work on continuous variables. This issue has numerous discussions, e.g. [here](https://stats.stackexchange.com... | 30,430 | [
-0.0626356452703476,
0.01923901028931141,
-0.008530388586223125,
-0.02634742669761181,
-0.022445807233452797,
0.03381524980068207,
0.06382523477077484,
0.010256730951368809,
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0.044451210647821426,
0.030881088227033615,
0.022934818640351295,
0.046908944845199585,
0.122... |
https://github.com/scikit-learn/scikit-learn/issues/30425 | [
"New Feature"
] | Make sklearn.neighbors algorithms treat all samples as neighbors when `n_neighbors is None`/`radius is None`
### Describe the workflow you want to enable
The proposed feature is that algorithms in `sklearn.neighbors`, when created with parameter `n_neighbors is None` or `radius is None`, treat all samples used for ... | 30,425 | [
0.003143601818010211,
0.06053180247545242,
0.018817780539393425,
0.006540612783282995,
-0.006058925297111273,
-0.031144751235842705,
0.03987443819642067,
0.022615524008870125,
0.06712454557418823,
0.009167199023067951,
0.013098949566483498,
0.03780755773186684,
-0.045685864984989166,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30425 | [
"New Feature"
] | Make sklearn.neighbors algorithms treat all samples as neighbors when `n_neighbors is None`/`radius is None`
### Describe the workflow you want to enable
The proposed feature is that algorithms in `sklearn.neighbors`, when created with parameter `n_neighbors is None` or `radius is None`, treat all samples used for ... | 30,425 | [
0.003143601818010211,
0.06053180247545242,
0.018817780539393425,
0.006540612783282995,
-0.006058925297111273,
-0.031144751235842705,
0.03987443819642067,
0.022615524008870125,
0.06712454557418823,
0.009167199023067951,
0.013098949566483498,
0.03780755773186684,
-0.045685864984989166,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30425 | [
"New Feature"
] | Make sklearn.neighbors algorithms treat all samples as neighbors when `n_neighbors is None`/`radius is None`
### Describe the workflow you want to enable
The proposed feature is that algorithms in `sklearn.neighbors`, when created with parameter `n_neighbors is None` or `radius is None`, treat all samples used for ... | 30,425 | [
0.003143601818010211,
0.06053180247545242,
0.018817780539393425,
0.006540612783282995,
-0.006058925297111273,
-0.031144751235842705,
0.03987443819642067,
0.022615524008870125,
0.06712454557418823,
0.009167199023067951,
0.013098949566483498,
0.03780755773186684,
-0.045685864984989166,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30422 | [
"New Feature",
"Needs Triage"
] | Code Smells and Linting Errors in check-meson-openmp-dependencies.py
### Describe the workflow you want to enable
Using the Python Linter set to PEP 8 and Test Driven Development using the Sci-Kit Lean testing suite.
### Describe your proposed solution
I propose to reduce redundant code with helper functions, speci... | 30,422 | [
0.0008868378354236484,
0.021584469825029373,
-0.013281479477882385,
0.008127573877573013,
0.03251880034804344,
0.033644385635852814,
-0.02912363037467003,
0.03153363615274429,
0.05743087828159332,
-0.052532121539115906,
0.023425059393048286,
0.07822414487600327,
-0.003087509423494339,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30413 | [
"Bug"
] | Identical branches in the conditional statement in "svm.cpp"
### Describe the bug
File svm/src/libsvm/svm.cpp, lines 1895-1903 contain the same statements. Is it correct?
### Steps/Code to Reproduce
if(fabs(alpha[i]) > 0)
{
++nSV;
if(prob->y[i] > 0)
{
if(fabs(alpha[i]) >= si.upper_bound[i])
... | 30,413 | [
0.013421031646430492,
-0.04716776683926582,
-0.048142217099666595,
-0.0030759861692786217,
0.030793460085988045,
-0.011849533766508102,
-0.010931429453194141,
-0.035794470459222794,
-0.062596395611763,
0.00565996253862977,
0.046848688274621964,
0.0030108578503131866,
0.06384885311126709,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30413 | [
"Bug"
] | Identical branches in the conditional statement in "svm.cpp"
### Describe the bug
File svm/src/libsvm/svm.cpp, lines 1895-1903 contain the same statements. Is it correct?
### Steps/Code to Reproduce
if(fabs(alpha[i]) > 0)
{
++nSV;
if(prob->y[i] > 0)
{
if(fabs(alpha[i]) >= si.upper_bound[i])
... | 30,413 | [
0.019884759560227394,
-0.02057550847530365,
-0.03551166504621506,
-0.01525045931339264,
0.03470360115170479,
0.010545328259468079,
0.017741169780492783,
-0.04014449566602707,
-0.01925944909453392,
0.030593719333410263,
0.055280935019254684,
0.011711487546563148,
0.0599764809012413,
0.05110... |
https://github.com/scikit-learn/scikit-learn/issues/30411 | [
"New Feature",
"Needs Triage"
] | Make `param_grid` in `GridSearchCV` a callable with the `X` and `y` as the parameters
### Describe the workflow you want to enable
**CASE 1:**
I use a "pipeline" approach with `SelectKBest` and `RandomForestClassifier`, and I want to use `RandomForestClassifier.monotonic_cst` which is a number array now.
As `... | 30,411 | [
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0.021864738315343857,
0.0007909589330665767,
-0.03462918847799301,
0.02538774348795414,
-0.06419675797224045,
-0.016643395647406578,
0.054705310612916946,
0.01687674969434738,
0.019749419763684273,
0.02425995282828808,
0.03197886794805527,
-0.052907370030879974,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30408 | [
"New Feature",
"Needs Decision - Include Feature"
] | `partial_fit` for `RobustScaler`
### Describe the workflow you want to enable
I would like to be able to use `partial_fit` with the `RobustScaler` preprocessing for streaming cases or when my data doesn't fit in memory.
As I understand from this paper https://sites.cs.ucsb.edu/~suri/psdir/ency.pdf, it would probably... | 30,408 | [
-0.024696892127394676,
0.0458722785115242,
0.02488483302295208,
0.0026731581892818213,
0.05492514371871948,
0.002138094510883093,
0.00460728770121932,
0.041288457810878754,
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0.014763724990189075,
0.07468865066766739,
-0.02067387104034424,
-0.038322579115629196,
0.06555... |
https://github.com/scikit-learn/scikit-learn/issues/30408 | [
"New Feature",
"Needs Decision - Include Feature"
] | `partial_fit` for `RobustScaler`
### Describe the workflow you want to enable
I would like to be able to use `partial_fit` with the `RobustScaler` preprocessing for streaming cases or when my data doesn't fit in memory.
As I understand from this paper https://sites.cs.ucsb.edu/~suri/psdir/ency.pdf, it would probably... | 30,408 | [
-0.024696892127394676,
0.0458722785115242,
0.02488483302295208,
0.0026731581892818213,
0.05492514371871948,
0.002138094510883093,
0.00460728770121932,
0.041288457810878754,
-0.01147995050996542,
0.014763724990189075,
0.07468865066766739,
-0.02067387104034424,
-0.038322579115629196,
0.06555... |
https://github.com/scikit-learn/scikit-learn/issues/30408 | [
"New Feature",
"Needs Decision - Include Feature"
] | `partial_fit` for `RobustScaler`
### Describe the workflow you want to enable
I would like to be able to use `partial_fit` with the `RobustScaler` preprocessing for streaming cases or when my data doesn't fit in memory.
As I understand from this paper https://sites.cs.ucsb.edu/~suri/psdir/ency.pdf, it would probably... | 30,408 | [
-0.024696892127394676,
0.0458722785115242,
0.02488483302295208,
0.0026731581892818213,
0.05492514371871948,
0.002138094510883093,
0.00460728770121932,
0.041288457810878754,
-0.01147995050996542,
0.014763724990189075,
0.07468865066766739,
-0.02067387104034424,
-0.038322579115629196,
0.06555... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
https://github.com/scikit-learn/scikit-learn/issues/30400 | [
"good first issue"
] | Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base... | 30,400 | [
-0.018440138548612595,
0.07996737957000732,
0.0056650820188224316,
-0.033587440848350525,
-0.010338649153709412,
-0.020304720848798752,
0.08347807824611664,
0.011415643617510796,
0.04955180361866951,
-0.002320877043530345,
0.02442643977701664,
-0.00034748887992464006,
-0.013437528163194656,
... |
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