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/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 | [
-0.03956654295325279,
0.13195696473121643,
0.008377266116440296,
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-0.042531050741672516,
0.04699299484491348,
0.016320008784532547,
0.03210342675447464,
0.02065916918218136,
0.030908867716789246,
0.07903342694044113,
-0.023141074925661087,
0.0653... |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 | [
-0.028265677392482758,
0.13111703097820282,
0.013402510434389114,
-0.030693411827087402,
-0.056445132941007614,
-0.04650292173027992,
0.04556351527571678,
-0.000010255358574795537,
0.044957034289836884,
0.014459749683737755,
0.036482714116573334,
0.10503017902374268,
-0.02446713112294674,
... |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 | [
-0.032629434019327164,
0.12474848330020905,
0.007900651544332504,
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0.04986874386668205,
0.0182973500341177,
0.0354548878967762,
0.01216214895248413,
0.03426291421055794,
0.07575958222150803,
-0.019595632329583168,
0.070644512... |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 | [
-0.036748964339494705,
0.12227261811494827,
0.008035395294427872,
-0.04069569334387779,
-0.06306750327348709,
-0.04231923073530197,
0.039966002106666565,
0.01681252382695675,
0.036313287913799286,
0.013941925950348377,
0.03584219142794609,
0.07151622325181961,
-0.01528013776987791,
0.06862... |
https://github.com/scikit-learn/scikit-learn/issues/22758 | [
"Bug",
"Needs Reproducible Code"
] | can't convert a list to lowercase list
### Describe the bug
```pytb
[sklearn/feature_extraction/text.py]n _preprocess(doc, accent_function, lower)
69 """
70 if lower:
---> 71 doc = doc.lower()
72 if accent_function is not None:
73 doc = accent_function(doc)
A... | 22,758 | [
0.036176953464746475,
-0.00003560041659511626,
-0.007791164331138134,
-0.01956314779818058,
0.06786171346902847,
0.03804406896233559,
0.048078253865242004,
0.05101104453206062,
-0.05270341783761978,
-0.05096173658967018,
-0.030383184552192688,
0.04471960291266441,
0.04764551296830177,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22758 | [
"Bug",
"Needs Reproducible Code"
] | can't convert a list to lowercase list
### Describe the bug
```pytb
[sklearn/feature_extraction/text.py]n _preprocess(doc, accent_function, lower)
69 """
70 if lower:
---> 71 doc = doc.lower()
72 if accent_function is not None:
73 doc = accent_function(doc)
A... | 22,758 | [
0.036176953464746475,
-0.00003560041659511626,
-0.007791164331138134,
-0.01956314779818058,
0.06786171346902847,
0.03804406896233559,
0.048078253865242004,
0.05101104453206062,
-0.05270341783761978,
-0.05096173658967018,
-0.030383184552192688,
0.04471960291266441,
0.04764551296830177,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.042412206530570984,
0.025929272174835205,
0.05874984338879585,
-0.04530267417430878,
0.024424342438578606,
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-0.02087152749300003,
0.03542496636509895,
0.017904875800013542,
-0.019315024837851524,
0.04919327422976494,
0.022991077974438667,
-0.027650445699691772,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.0478631965816021,
0.039266277104616165,
0.051356881856918335,
-0.044546227902173996,
0.021818213164806366,
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0.04456104710698128,
0.0020891546737402678,
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0.042570486664772034,
0.010881965979933739,
-0.043984752148389816,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.0410110205411911,
-0.00014959549298509955,
0.05766450613737106,
-0.05094703286886215,
0.019172150641679764,
0.00585463922470808,
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0.029763221740722656,
0.024031680077314377,
-0.017458833754062653,
0.049297768622636795,
0.012876509688794613,
-0.010898696258664131,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.06012161448597908,
0.02025606855750084,
0.04703278839588165,
-0.05284285545349121,
0.011408415623009205,
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-0.007539486046880484,
0.0525372214615345,
0.008879899978637695,
-0.03088212013244629,
0.0517... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.050343722105026245,
0.016869157552719116,
0.045630618929862976,
-0.053244709968566895,
0.014692405238747597,
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0.0542885921895504,
-0.0015326783759519458,
-0.0297784972935915,
... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.054491475224494934,
0.016996435821056366,
0.03638097643852234,
-0.05029091611504555,
0.0006349082104861736,
-0.004410979337990284,
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0.038812603801488876,
-0.0042207855731248856,
-0.02424924075603485,
0.059671252965927124,
0.01401200145483017,
-0.016034094616770744,
... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.08414369821548462,
0.021604686975479126,
0.04291928932070732,
-0.03181789442896843,
0.0010177241638302803,
-0.0038649116177111864,
-0.05677323415875435,
0.03464512154459953,
0.025796813890337944,
0.010271581821143627,
0.046949341893196106,
0.007290777284651995,
-0.022807428613305092,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.06617298722267151,
0.01152252871543169,
0.04994185268878937,
-0.03834562003612518,
0.01705632358789444,
0.00501238601282239,
-0.037621356546878815,
0.029357999563217163,
0.05486331507563591,
-0.03301805630326271,
0.046881020069122314,
0.02835948020219803,
-0.03998827934265137,
0.0410008... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.0566958449780941,
0.0070576914586126804,
0.042564794421195984,
-0.04789002984762192,
0.011677822098135948,
-0.018231607973575592,
-0.013199529610574245,
0.02696637064218521,
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-0.025317128747701645,
0.04813561215996742,
0.02574891969561577,
-0.03196043521165848,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 | [
-0.046162743121385574,
0.04703536257147789,
0.047164954245090485,
-0.05646669492125511,
0.012381115928292274,
-0.016417238861322403,
-0.016089551150798798,
0.04957679286599159,
-0.008929487317800522,
-0.024272875860333443,
0.035586487501859665,
0.008681947365403175,
-0.017065128311514854,
... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 | [
-0.03806405887007713,
0.03525340184569359,
-0.018503468483686447,
0.011404960416257381,
-0.03859312832355499,
-0.05190958455204964,
0.020668914541602135,
0.010682887397706509,
-0.054936740547418594,
-0.034327585250139236,
-0.01307710912078619,
0.041174255311489105,
-0.017688950523734093,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22750 | [
"Bug",
"module:cluster",
"Needs Triage"
] | Unable to compute AgglomerativeClustering with affinity 'precomputed' and linkage 'ward'
### Describe the bug
When trying to compute AgglomerativeClustering with affinity='precomputed', linkage='ward' I get the following error:
`ValueError: precomputed was provided as affinity. Ward can only work with euclidean di... | 22,750 | [
-0.045136719942092896,
-0.0775979608297348,
0.008834334090352058,
-0.033289529383182526,
0.025224847719073296,
0.0011687814258038998,
0.021931489929556847,
-0.011052739806473255,
0.06405843794345856,
0.019501814618706703,
-0.0030591515824198723,
0.006542063783854246,
0.01342521607875824,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22750 | [
"Bug",
"module:cluster",
"Needs Triage"
] | Unable to compute AgglomerativeClustering with affinity 'precomputed' and linkage 'ward'
### Describe the bug
When trying to compute AgglomerativeClustering with affinity='precomputed', linkage='ward' I get the following error:
`ValueError: precomputed was provided as affinity. Ward can only work with euclidean di... | 22,750 | [
-0.045136719942092896,
-0.0775979608297348,
0.008834334090352058,
-0.033289529383182526,
0.025224847719073296,
0.0011687814258038998,
0.021931489929556847,
-0.011052739806473255,
0.06405843794345856,
0.019501814618706703,
-0.0030591515824198723,
0.006542063783854246,
0.01342521607875824,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22746 | [
"Bug",
"Needs Triage"
] | PCA.fit_transform() failing
### Describe the bug
I have data in a numpy array of shape (2970, 291) that contains `NaN` and `inf` values. `np.nan_to_num()` was called on the array prior to `fit_transform()` within the function provided below but `ValueError: array must not contain infs or NaNs` was raised instead. T... | 22,746 | [
-0.03848776966333389,
0.04018993303179741,
0.02664085477590561,
0.03303118795156479,
0.08532460778951645,
-0.018159132450819016,
-0.007035703863948584,
0.041717611253261566,
-0.04819914326071739,
0.0298629030585289,
0.023548541590571404,
0.02258780039846897,
0.017551226541399956,
0.0160855... |
https://github.com/scikit-learn/scikit-learn/issues/22746 | [
"Bug",
"Needs Triage"
] | PCA.fit_transform() failing
### Describe the bug
I have data in a numpy array of shape (2970, 291) that contains `NaN` and `inf` values. `np.nan_to_num()` was called on the array prior to `fit_transform()` within the function provided below but `ValueError: array must not contain infs or NaNs` was raised instead. T... | 22,746 | [
-0.03848776966333389,
0.04018993303179741,
0.02664085477590561,
0.03303118795156479,
0.08532460778951645,
-0.018159132450819016,
-0.007035703863948584,
0.041717611253261566,
-0.04819914326071739,
0.0298629030585289,
0.023548541590571404,
0.02258780039846897,
0.017551226541399956,
0.0160855... |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 | [
0.0037530814297497272,
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0.03090500645339489,
0.05261942371726036,
-0.02853109873831272,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 | [
0.0037530814297497272,
0.019818512722849846,
-0.0033717770129442215,
0.050157416611909866,
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0.03090500645339489,
0.05261942371726036,
-0.02853109873831272,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 | [
0.0037530814297497272,
0.019818512722849846,
-0.0033717770129442215,
0.050157416611909866,
0.02870866097509861,
-0.0032513197511434555,
-0.008210579864680767,
0.05144864693284035,
0.015640078112483025,
-0.027652772143483162,
0.03090500645339489,
0.05261942371726036,
-0.02853109873831272,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 | [
0.0037530814297497272,
0.019818512722849846,
-0.0033717770129442215,
0.050157416611909866,
0.02870866097509861,
-0.0032513197511434555,
-0.008210579864680767,
0.05144864693284035,
0.015640078112483025,
-0.027652772143483162,
0.03090500645339489,
0.05261942371726036,
-0.02853109873831272,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 | [
0.014500558376312256,
-0.0013400521129369736,
0.04781723767518997,
-0.007753642275929451,
0.06449726223945618,
0.027786921709775925,
0.05864400416612625,
0.037429600954055786,
-0.020262079313397408,
0.01998012140393257,
0.0373300202190876,
0.023886673152446747,
0.02215849980711937,
0.04466... |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 | [
0.014500558376312256,
-0.0013400521129369736,
0.04781723767518997,
-0.007753642275929451,
0.06449726223945618,
0.027786921709775925,
0.05864400416612625,
0.037429600954055786,
-0.020262079313397408,
0.01998012140393257,
0.0373300202190876,
0.023886673152446747,
0.02215849980711937,
0.04466... |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 | [
0.014500558376312256,
-0.0013400521129369736,
0.04781723767518997,
-0.007753642275929451,
0.06449726223945618,
0.027786921709775925,
0.05864400416612625,
0.037429600954055786,
-0.020262079313397408,
0.01998012140393257,
0.0373300202190876,
0.023886673152446747,
0.02215849980711937,
0.04466... |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 | [
0.014500558376312256,
-0.0013400521129369736,
0.04781723767518997,
-0.007753642275929451,
0.06449726223945618,
0.027786921709775925,
0.05864400416612625,
0.037429600954055786,
-0.020262079313397408,
0.01998012140393257,
0.0373300202190876,
0.023886673152446747,
0.02215849980711937,
0.04466... |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 | [
0.014500558376312256,
-0.0013400521129369736,
0.04781723767518997,
-0.007753642275929451,
0.06449726223945618,
0.027786921709775925,
0.05864400416612625,
0.037429600954055786,
-0.020262079313397408,
0.01998012140393257,
0.0373300202190876,
0.023886673152446747,
0.02215849980711937,
0.04466... |
https://github.com/scikit-learn/scikit-learn/issues/22730 | [
"Needs Triage"
] | How to use Hierarchical Navigable Small Worlds (HNSW) and LSH for Data classification rather than just retrieving Nearest neighbours
I want to use Hierarchical Navigable Small Worlds (HNSW) and LSH for data classification. How can I modify their fit and train functions???
For example if you want to use them like ba... | 22,730 | [
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0.0077634695917367935,
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0.001340061193332076,
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0.012261500582098961,
... |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 | [
-0.03463263809680939,
-0.03893638774752617,
0.03195276856422424,
-0.01911740191280842,
0.05871649831533432,
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0.011015140451490879,
0.02712497115135193,
0.0010024042567238212,
0.04371226951479912,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 | [
-0.03463263809680939,
-0.03893638774752617,
0.03195276856422424,
-0.01911740191280842,
0.05871649831533432,
-0.036162156611680984,
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0.011015140451490879,
0.02712497115135193,
0.0010024042567238212,
0.04371226951479912,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 | [
-0.03463263809680939,
-0.03893638774752617,
0.03195276856422424,
-0.01911740191280842,
0.05871649831533432,
-0.036162156611680984,
-0.005060564260929823,
-0.011542647145688534,
-0.013940524309873581,
0.011015140451490879,
0.02712497115135193,
0.0010024042567238212,
0.04371226951479912,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 | [
-0.0011333450675010681,
-0.028208663687109947,
-0.034443773329257965,
0.021379144862294197,
0.04200249910354614,
0.013324057683348656,
0.021564742550253868,
0.017661651596426964,
-0.04400479793548584,
-0.027873022481799126,
0.04061056300997734,
0.02352132461965084,
-0.022576522082090378,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22708 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Implement Repeated Group CV
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/model_selection/_split.py#L505
I tried to implement repeated group cv using GroupKFold and _RepeatedSplits. But it did not work unless I included `shuffle=False, random_state=None` to `def ... | 22,708 | [
-0.018991488963365555,
-0.0013465906959027052,
0.009089581668376923,
0.021961431950330734,
-0.021594610065221786,
-0.009921971708536148,
0.10028442740440369,
-0.011589190922677517,
0.022683128714561462,
-0.04995524138212204,
0.05249472334980965,
0.030365033075213432,
0.0010625696741044521,
... |
https://github.com/scikit-learn/scikit-learn/issues/22708 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Implement Repeated Group CV
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/model_selection/_split.py#L505
I tried to implement repeated group cv using GroupKFold and _RepeatedSplits. But it did not work unless I included `shuffle=False, random_state=None` to `def ... | 22,708 | [
-0.03165051341056824,
0.016821861267089844,
0.007662260439246893,
0.02439388446509838,
0.013165973126888275,
0.0029692272655665874,
0.10241342335939407,
-0.029366599395871162,
0.040356092154979706,
-0.03857475146651268,
0.05568395555019379,
0.03468421474099159,
-0.007414358202368021,
0.058... |
https://github.com/scikit-learn/scikit-learn/issues/22699 | [
"Bug",
"Packaging"
] | Installing scipy-wheels-nightly using pip shows error
### Describe the bug
When installing torch from https://pypi.anaconda.org/scipy-wheels-nightly/simple the console show a warning.
### Steps/Code to Reproduce
```bash
~: pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple ... | 22,699 | [
0.039653729647397995,
-0.03989299386739731,
-0.0311338622123003,
-0.018998868763446808,
0.05690786987543106,
0.06194694712758064,
0.019523655995726585,
0.0725293830037117,
0.001958058215677738,
-0.026866376399993896,
-0.019093798473477364,
0.04325450584292412,
0.006673269905149937,
-0.0482... |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 | [
0.0466047078371048,
-0.006905793212354183,
0.026651984080672264,
0.026227770373225212,
0.04950492084026337,
-0.004020174965262413,
-0.011250353418290615,
-0.0050560119561851025,
-0.005661523900926113,
-0.0026587601751089096,
0.060618020594120026,
0.016495099291205406,
0.0016730388160794973,
... |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 | [
0.0466047078371048,
-0.006905793212354183,
0.026651984080672264,
0.026227770373225212,
0.04950492084026337,
-0.004020174965262413,
-0.011250353418290615,
-0.0050560119561851025,
-0.005661523900926113,
-0.0026587601751089096,
0.060618020594120026,
0.016495099291205406,
0.0016730388160794973,
... |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 | [
0.0466047078371048,
-0.006905793212354183,
0.026651984080672264,
0.026227770373225212,
0.04950492084026337,
-0.004020174965262413,
-0.011250353418290615,
-0.0050560119561851025,
-0.005661523900926113,
-0.0026587601751089096,
0.060618020594120026,
0.016495099291205406,
0.0016730388160794973,
... |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 | [
0.0466047078371048,
-0.006905793212354183,
0.026651984080672264,
0.026227770373225212,
0.04950492084026337,
-0.004020174965262413,
-0.011250353418290615,
-0.0050560119561851025,
-0.005661523900926113,
-0.0026587601751089096,
0.060618020594120026,
0.016495099291205406,
0.0016730388160794973,
... |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 | [
-0.03445413336157799,
-0.00883459858596325,
0.03432859852910042,
-0.03639686107635498,
0.041413143277168274,
-0.0039563365280628204,
0.01799251139163971,
0.01447457354515791,
-0.030579915270209312,
-0.03851310536265373,
0.051631662994623184,
-0.03010181523859501,
-0.01070859283208847,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 | [
-0.032687023282051086,
-0.005512929987162352,
0.03127671033143997,
-0.037022802978754044,
0.040501900017261505,
-0.0019458549795672297,
0.022762659937143326,
0.012637319974601269,
-0.03286535292863846,
-0.040368687361478806,
0.04822729155421257,
-0.028688417747616768,
-0.011288088746368885,
... |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 | [
-0.027913426980376244,
-0.004533824045211077,
0.036460764706134796,
-0.03633882477879524,
0.0422096773982048,
-0.006106287240982056,
0.02351861447095871,
0.014387324452400208,
-0.03743002563714981,
-0.045304253697395325,
0.05140027776360512,
-0.030438706278800964,
-0.005704620853066444,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
0.05466071143746376,
-0.028078129515051842,
-0.01438218168914318,
0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
-0.00378115801140666,
-0.02780722640454769,
0.059068601578474045,
-0.009369526989758015,
0.02065003477036953,
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0.009913437999784946,
0.06970690935850143,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
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0.009913437999784946,
0.06970690935850143,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
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0.06970690935850143,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 | [
0.003882789285853505,
-0.014548704959452152,
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0.009913437999784946,
0.06970690935850143,
-0.02331753447651863,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22683 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with a callable weights stopped working with numpy 1.22.2
### Describe the bug
When you use a callable for the weights param you get:
AttributeError: 'list' object has no attribute 'shape'
`neigh = KNeighborsRegressor(n_neighbors=5, algorithm='brute', metric=euclidean_distance, weights=weigh... | 22,683 | [
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0.05490880459547043,
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0.019932379946112633,
0.022359265014529228,
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-0... |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 | [
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0.07324622571468353,
0.03717810660600662,
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0.02529306896030903,
0.07508692890405655,
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0.012496883049607277,
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0.015634... |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 | [
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0.07324622571468353,
0.03717810660600662,
0.019282422959804535,
0.07173191010951996,
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0.012496883049607277,
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-0.01914849504828453,
0.015634... |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 | [
-0.04454122856259346,
0.07324622571468353,
0.03717810660600662,
0.019282422959804535,
0.07173191010951996,
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0.02529306896030903,
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-0.0357387475669384,
0.012496883049607277,
-0.004832872189581394,
-0.01914849504828453,
0.015634... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
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0.00634552538394928,
0.028834549710154533,
-0.05445927008986473,
0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
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0.028834549710154533,
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0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
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0.028834549710154533,
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0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
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0.028834549710154533,
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0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
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0.012088442221283913,
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0.09534737467765808,
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0.00634552538394928,
0.028834549710154533,
-0.05445927008986473,
0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 | [
-0.0514993779361248,
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-0.010378529317677021,
0.012088442221283913,
0.01698601432144642,
0.018349794670939445,
0.09534737467765808,
0.061647772789001465,
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-0.008576038293540478,
0.00634552538394928,
0.028834549710154533,
-0.05445927008986473,
0.065... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
-0.02679889276623726,
0.01550618838518858,
0.02479819394648075,
0.0018892864463850856,
0.05517735704779625,
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0.02048814669251442,
0.026996154338121414,
0.06438568979501724,
0.006194833666086197,
-0.02311468869447708,
0.04328338801860809,
-0.0023114944342523813,
-0.004... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
-0.029183460399508476,
0.06111284717917442,
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0.011303920298814774,
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0.03296596184372902,
-0.012911101803183556,
0.006... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
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0.0409148745238781,
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0.039496395736932755,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
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0.0343269445002079,
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0.012456420809030533,
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0.02561580389738083,
0.010533231310546398,
0.017176... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
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0.015017381869256496,
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0.027942687273025513,
0.02481764368712902,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
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0.0021569172386080027,
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0.0030703956726938486,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
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0.02999335154891014,
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0.0030175731517374516,
0.001... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
-0.030145687982439995,
0.033698003739118576,
0.01425888855010271,
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0.04169571399688721,
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 | [
-0.035528119653463364,
0.031074227765202522,
0.016068214550614357,
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0.02794337458908558,
-0.008991497568786144,
0.040971897542476654,
-0.00504589406773448,
0.0... |
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