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/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25627 | [
"Bug",
"module:ensemble"
] | OrdinalEncoder does not work with HistGradientBoostingClassifier when there are NULLs
### Describe the bug
If you use the ordinal encoder when there is NULLS you need to put them to a Negative Value otherwise you get. The following error
_The used value for unknown_value is one of the values already used for en... | 25,627 | [
-0.009759302251040936,
0.06893999129533768,
0.03167257085442543,
-0.04177350923418999,
0.10421353578567505,
-0.02450450137257576,
0.007950368337333202,
0.023538818582892418,
-0.06934570521116257,
0.016626957803964615,
0.07973717898130417,
-0.011962899938225746,
-0.003874350106343627,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/25627 | [
"Bug",
"module:ensemble"
] | OrdinalEncoder does not work with HistGradientBoostingClassifier when there are NULLs
### Describe the bug
If you use the ordinal encoder when there is NULLS you need to put them to a Negative Value otherwise you get. The following error
_The used value for unknown_value is one of the values already used for en... | 25,627 | [
-0.009759302251040936,
0.06893999129533768,
0.03167257085442543,
-0.04177350923418999,
0.10421353578567505,
-0.02450450137257576,
0.007950368337333202,
0.023538818582892418,
-0.06934570521116257,
0.016626957803964615,
0.07973717898130417,
-0.011962899938225746,
-0.003874350106343627,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/25627 | [
"Bug",
"module:ensemble"
] | OrdinalEncoder does not work with HistGradientBoostingClassifier when there are NULLs
### Describe the bug
If you use the ordinal encoder when there is NULLS you need to put them to a Negative Value otherwise you get. The following error
_The used value for unknown_value is one of the values already used for en... | 25,627 | [
-0.009759302251040936,
0.06893999129533768,
0.03167257085442543,
-0.04177350923418999,
0.10421353578567505,
-0.02450450137257576,
0.007950368337333202,
0.023538818582892418,
-0.06934570521116257,
0.016626957803964615,
0.07973717898130417,
-0.011962899938225746,
-0.003874350106343627,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/25627 | [
"Bug",
"module:ensemble"
] | OrdinalEncoder does not work with HistGradientBoostingClassifier when there are NULLs
### Describe the bug
If you use the ordinal encoder when there is NULLS you need to put them to a Negative Value otherwise you get. The following error
_The used value for unknown_value is one of the values already used for en... | 25,627 | [
-0.009759302251040936,
0.06893999129533768,
0.03167257085442543,
-0.04177350923418999,
0.10421353578567505,
-0.02450450137257576,
0.007950368337333202,
0.023538818582892418,
-0.06934570521116257,
0.016626957803964615,
0.07973717898130417,
-0.011962899938225746,
-0.003874350106343627,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/25627 | [
"Bug",
"module:ensemble"
] | OrdinalEncoder does not work with HistGradientBoostingClassifier when there are NULLs
### Describe the bug
If you use the ordinal encoder when there is NULLS you need to put them to a Negative Value otherwise you get. The following error
_The used value for unknown_value is one of the values already used for en... | 25,627 | [
-0.009759302251040936,
0.06893999129533768,
0.03167257085442543,
-0.04177350923418999,
0.10421353578567505,
-0.02450450137257576,
0.007950368337333202,
0.023538818582892418,
-0.06934570521116257,
0.016626957803964615,
0.07973717898130417,
-0.011962899938225746,
-0.003874350106343627,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/25626 | [
"Needs Triage"
] | ValueError: dimension mismatch for Logistic Regression.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25625
<div type='discussions-op-text'>
<sup>Originally posted by **samarthpatel1289** February 16, 2023</sup>
I was looking for solution to this. Followed the sklearn documentaion no... | 25,626 | [
0.030401887372136116,
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0.02469080314040184,
0.021946445107460022,
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0.01832621917128563,
-0.012520944699645042,
0.00362212210893631,
0.05401... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
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0.02859751135110855,
0.006836519110947847,
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0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
-0.042775996029376984,
0.02859751135110855,
0.006836519110947847,
-0.034310195595026016,
0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
-0.042775996029376984,
0.02859751135110855,
0.006836519110947847,
-0.034310195595026016,
0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
-0.042775996029376984,
0.02859751135110855,
0.006836519110947847,
-0.034310195595026016,
0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
-0.042775996029376984,
0.02859751135110855,
0.006836519110947847,
-0.034310195595026016,
0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25623 | [
"Bug",
"module:neighbors"
] | KernelDensity incorrect handling of bandwidth
### Describe the bug
I was using kernel density estimator
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
using 'silverman' or 'scott' as the bandwidth argument. Then I found that the bandwidth automatically adjusted by the algor... | 25,623 | [
-0.017041459679603577,
0.01775447651743889,
-0.006803067866712809,
0.0343453511595726,
0.012069220654666424,
-0.042775996029376984,
0.02859751135110855,
0.006836519110947847,
-0.034310195595026016,
0.02048790641129017,
0.008052979595959187,
-0.007214228622615337,
0.02370072714984417,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25616 | [
"Bug",
"Needs Triage"
] | Standard Deviation with GPR always between 0 and 1
### Describe the bug
I have been trying to fit a gpr interpolation to a set of data, but I keep finding that the standard deviation is always between 0 and 1. I have tried a 1-dimensional example which uses a sin graph and that produces std with a much greater rang... | 25,616 | [
-0.031381819397211075,
-0.03591696172952652,
0.037627529352903366,
0.025449223816394806,
0.05449002981185913,
-0.0652899295091629,
-0.006434999406337738,
0.011032842099666595,
0.015037545934319496,
0.054021697491407394,
0.05348962917923927,
0.04002268612384796,
0.0288618803024292,
0.031861... |
https://github.com/scikit-learn/scikit-learn/issues/25616 | [
"Bug",
"Needs Triage"
] | Standard Deviation with GPR always between 0 and 1
### Describe the bug
I have been trying to fit a gpr interpolation to a set of data, but I keep finding that the standard deviation is always between 0 and 1. I have tried a 1-dimensional example which uses a sin graph and that produces std with a much greater rang... | 25,616 | [
-0.031381819397211075,
-0.03591696172952652,
0.037627529352903366,
0.025449223816394806,
0.05449002981185913,
-0.0652899295091629,
-0.006434999406337738,
0.011032842099666595,
0.015037545934319496,
0.054021697491407394,
0.05348962917923927,
0.04002268612384796,
0.0288618803024292,
0.031861... |
https://github.com/scikit-learn/scikit-learn/issues/25612 | [
"New Feature",
"module:tree",
"Needs Investigation"
] | Simplify decision tree removing redundant decisions
### Describe the workflow you want to enable
Description: Add a new method simplify() to the decision tree Class that returns a simplified version of the decision tree by pruning redundant leaves that do not add new decision paths. This simplification method will cr... | 25,612 | [
-0.02932412177324295,
0.02433822676539421,
-0.0419795885682106,
0.002073944779112935,
0.0012951830867677927,
0.0026824933011084795,
-0.08257535099983215,
0.028405001387000084,
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0.00966334342956543,
0.010582915507256985,
0.12449241429567337,
0.01773332990705967,
-0.0112... |
https://github.com/scikit-learn/scikit-learn/issues/25612 | [
"New Feature",
"module:tree",
"Needs Investigation"
] | Simplify decision tree removing redundant decisions
### Describe the workflow you want to enable
Description: Add a new method simplify() to the decision tree Class that returns a simplified version of the decision tree by pruning redundant leaves that do not add new decision paths. This simplification method will cr... | 25,612 | [
-0.02932412177324295,
0.02433822676539421,
-0.0419795885682106,
0.002073944779112935,
0.0012951830867677927,
0.0026824933011084795,
-0.08257535099983215,
0.028405001387000084,
-0.08977001160383224,
0.00966334342956543,
0.010582915507256985,
0.12449241429567337,
0.01773332990705967,
-0.0112... |
https://github.com/scikit-learn/scikit-learn/issues/25611 | [
"Documentation"
] | Improve the visibility of the projects governance
### Describe the issue linked to the documentation
When I navigate to https://scikit-learn.org/stable/governance.html#governance, I first got to scikit-learn.org -> More -> About Us, and then there is a link to the governance. This should be improved!
### Suggest a p... | 25,611 | [
0.02149404212832451,
0.03085559606552124,
-0.03992156311869621,
0.01516995020210743,
0.044028349220752716,
0.0077628144063055515,
0.012712805531919003,
0.019039683043956757,
0.011772528290748596,
0.020855318754911423,
0.045037079602479935,
0.021571697667241096,
0.007865003310143948,
0.0708... |
https://github.com/scikit-learn/scikit-learn/issues/25609 | [
"cython"
] | [MAINT, Cython] Implicit `noexcept` is deprecated in Cython 3.0
Hi,
I was trying some stuff out on Cython 3.0, and I saw a bunch of errors of the form:
```
...
warning: sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx:954:49: Implicit noexcept declaration is deprecated. Function declaratio... | 25,609 | [
0.003233511233702302,
0.019230753183364868,
-0.021282970905303955,
-0.008482299745082855,
0.0401189886033535,
0.007879320532083511,
0.045103855431079865,
0.012985186651349068,
0.006319182924926281,
-0.046808790415525436,
0.031089432537555695,
0.06088212504982948,
-0.05393952131271362,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25609 | [
"cython"
] | [MAINT, Cython] Implicit `noexcept` is deprecated in Cython 3.0
Hi,
I was trying some stuff out on Cython 3.0, and I saw a bunch of errors of the form:
```
...
warning: sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx:954:49: Implicit noexcept declaration is deprecated. Function declaratio... | 25,609 | [
0.003233511233702302,
0.019230753183364868,
-0.021282970905303955,
-0.008482299745082855,
0.0401189886033535,
0.007879320532083511,
0.045103855431079865,
0.012985186651349068,
0.006319182924926281,
-0.046808790415525436,
0.031089432537555695,
0.06088212504982948,
-0.05393952131271362,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25609 | [
"cython"
] | [MAINT, Cython] Implicit `noexcept` is deprecated in Cython 3.0
Hi,
I was trying some stuff out on Cython 3.0, and I saw a bunch of errors of the form:
```
...
warning: sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx:954:49: Implicit noexcept declaration is deprecated. Function declaratio... | 25,609 | [
0.003233511233702302,
0.019230753183364868,
-0.021282970905303955,
-0.008482299745082855,
0.0401189886033535,
0.007879320532083511,
0.045103855431079865,
0.012985186651349068,
0.006319182924926281,
-0.046808790415525436,
0.031089432537555695,
0.06088212504982948,
-0.05393952131271362,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25607 | [
"Bug",
"Needs Triage"
] | Ordinal encoder not encoding missing values as np.nan
### Describe the bug
The documentation for OrdinalEncoder states that the default encoded_missing_value value is np.nan but when I run the encoder, it replace missing values with -9223372036854775808. The same behaviour is seen even if I manually specify the argum... | 25,607 | [
-0.0015779590466991067,
0.07263042777776718,
0.030037110671401024,
-0.005055049434304237,
0.09141328930854797,
0.025837451219558716,
0.037339117377996445,
0.0596371665596962,
-0.07844915241003036,
-0.00891966838389635,
0.06422516703605652,
0.022427212446928024,
0.009861689060926437,
0.0341... |
https://github.com/scikit-learn/scikit-learn/issues/25604 | [
"Bug",
"Needs Info"
] | MLPR with solver='lbfgs', nonzero alpha doesn't make the same result from multiple run.
### Describe the bug
I have tested the simple regression modeling with MLPRegressor(solver='lbfgs', alpha=0.01, tol=0.0001, random_state=42) with sample data with three input parameters and three targets.
I tested multiple run ... | 25,604 | [
0.002976424992084503,
-0.0006946709472686052,
0.03319698944687843,
0.014699084684252739,
0.05895764008164406,
-0.04505764693021774,
0.03973538428544998,
0.031869299709796906,
0.0034179387148469687,
0.0019418332958593965,
0.048095718026161194,
0.04655524715781212,
0.022030578926205635,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25604 | [
"Bug",
"Needs Info"
] | MLPR with solver='lbfgs', nonzero alpha doesn't make the same result from multiple run.
### Describe the bug
I have tested the simple regression modeling with MLPRegressor(solver='lbfgs', alpha=0.01, tol=0.0001, random_state=42) with sample data with three input parameters and three targets.
I tested multiple run ... | 25,604 | [
0.002976424992084503,
-0.0006946709472686052,
0.03319698944687843,
0.014699084684252739,
0.05895764008164406,
-0.04505764693021774,
0.03973538428544998,
0.031869299709796906,
0.0034179387148469687,
0.0019418332958593965,
0.048095718026161194,
0.04655524715781212,
0.022030578926205635,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25604 | [
"Bug",
"Needs Info"
] | MLPR with solver='lbfgs', nonzero alpha doesn't make the same result from multiple run.
### Describe the bug
I have tested the simple regression modeling with MLPRegressor(solver='lbfgs', alpha=0.01, tol=0.0001, random_state=42) with sample data with three input parameters and three targets.
I tested multiple run ... | 25,604 | [
0.002976424992084503,
-0.0006946709472686052,
0.03319698944687843,
0.014699084684252739,
0.05895764008164406,
-0.04505764693021774,
0.03973538428544998,
0.031869299709796906,
0.0034179387148469687,
0.0019418332958593965,
0.048095718026161194,
0.04655524715781212,
0.022030578926205635,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25603 | [
"Documentation"
] | Building from source fails on Linux systems with pre-installed Intel OpenMP
### Describe the issue linked to the documentation
On systems that have Intel compilers with OpenMP support (like `icx` and `icpc`), building scikit-learn from source fails with the following error.
`ImportError: libomp.so: cannot open s... | 25,603 | [
-0.024739842861890793,
0.018891526386141777,
-0.0290848296135664,
-0.02184453047811985,
-0.011389841325581074,
0.020651020109653473,
0.042854372411966324,
0.028698451817035675,
0.017056012526154518,
0.029494676738977432,
-0.03265144303441048,
0.07468316704034805,
0.01992117427289486,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25603 | [
"Documentation"
] | Building from source fails on Linux systems with pre-installed Intel OpenMP
### Describe the issue linked to the documentation
On systems that have Intel compilers with OpenMP support (like `icx` and `icpc`), building scikit-learn from source fails with the following error.
`ImportError: libomp.so: cannot open s... | 25,603 | [
-0.02219446562230587,
0.02278032712638378,
-0.02972984127700329,
-0.0379498228430748,
-0.005536039359867573,
0.012165283784270287,
0.02992340363562107,
0.03237343952059746,
0.0009026756742969155,
0.025036413222551346,
-0.030729951336979866,
0.09270630031824112,
0.012794330716133118,
-0.041... |
https://github.com/scikit-learn/scikit-learn/issues/25603 | [
"Documentation"
] | Building from source fails on Linux systems with pre-installed Intel OpenMP
### Describe the issue linked to the documentation
On systems that have Intel compilers with OpenMP support (like `icx` and `icpc`), building scikit-learn from source fails with the following error.
`ImportError: libomp.so: cannot open s... | 25,603 | [
-0.027430223301053047,
0.020304610952734947,
-0.0346330925822258,
-0.03028407320380211,
-0.007041784469038248,
0.012035183608531952,
0.03057929500937462,
0.03538912907242775,
0.013065053150057793,
0.01972210220992565,
-0.03789854422211647,
0.08035609871149063,
0.009087396785616875,
-0.0349... |
https://github.com/scikit-learn/scikit-learn/issues/25603 | [
"Documentation"
] | Building from source fails on Linux systems with pre-installed Intel OpenMP
### Describe the issue linked to the documentation
On systems that have Intel compilers with OpenMP support (like `icx` and `icpc`), building scikit-learn from source fails with the following error.
`ImportError: libomp.so: cannot open s... | 25,603 | [
-0.025870393961668015,
0.015000429004430771,
-0.03739055246114731,
-0.026154495775699615,
-0.008872759528458118,
0.009164842776954174,
0.029506465420126915,
0.034105122089385986,
0.012545865029096603,
0.01830652914941311,
-0.0405169241130352,
0.08575974404811859,
0.014219476841390133,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25597 | [
"Documentation",
"module:ensemble"
] | Unsupported multioutput stacking regressor
### Describe the bug
The method `fit_transform` of `sklearn.ensemble.StackingRegressor`, according to the documentation, should support as second argument (`y`) an array-like of shape (n_samples,) or (n_samples, n_outputs). However, if an array of shape (n_sample, n_output... | 25,597 | [
-0.013173804618418217,
-0.005758323241025209,
0.023331111297011375,
-0.00906519964337349,
0.1100318655371666,
0.001472019823268056,
0.06868623197078705,
-0.0021870641503483057,
-0.01674230769276619,
0.020003756508231163,
0.002505253301933408,
0.029793715104460716,
0.0053316629491746426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25597 | [
"Documentation",
"module:ensemble"
] | Unsupported multioutput stacking regressor
### Describe the bug
The method `fit_transform` of `sklearn.ensemble.StackingRegressor`, according to the documentation, should support as second argument (`y`) an array-like of shape (n_samples,) or (n_samples, n_outputs). However, if an array of shape (n_sample, n_output... | 25,597 | [
-0.013173804618418217,
-0.005758323241025209,
0.023331111297011375,
-0.00906519964337349,
0.1100318655371666,
0.001472019823268056,
0.06868623197078705,
-0.0021870641503483057,
-0.01674230769276619,
0.020003756508231163,
0.002505253301933408,
0.029793715104460716,
0.0053316629491746426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25597 | [
"Documentation",
"module:ensemble"
] | Unsupported multioutput stacking regressor
### Describe the bug
The method `fit_transform` of `sklearn.ensemble.StackingRegressor`, according to the documentation, should support as second argument (`y`) an array-like of shape (n_samples,) or (n_samples, n_outputs). However, if an array of shape (n_sample, n_output... | 25,597 | [
-0.013173804618418217,
-0.005758323241025209,
0.023331111297011375,
-0.00906519964337349,
0.1100318655371666,
0.001472019823268056,
0.06868623197078705,
-0.0021870641503483057,
-0.01674230769276619,
0.020003756508231163,
0.002505253301933408,
0.029793715104460716,
0.0053316629491746426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25596 | [
"New Feature",
"Needs Triage"
] | Train-test-split for multilabel datasets
### Describe the workflow you want to enable
Train-test splits for 2-dimensional targets (i.e. multi-label datasets).
I saw couple of issues related to multi-label classification, but as far as I can tell, train-test-split has not been addressed there.
Related forum questi... | 25,596 | [
-0.021028244867920876,
0.02639487013220787,
-0.013605509884655476,
0.005945878569036722,
0.006353582721203566,
-0.016301942989230156,
0.123408742249012,
0.05153979733586311,
0.01620630733668804,
-0.06136523559689522,
-0.004072620067745447,
0.0033475779928267,
-0.053430743515491486,
0.01285... |
https://github.com/scikit-learn/scikit-learn/issues/25596 | [
"New Feature",
"Needs Triage"
] | Train-test-split for multilabel datasets
### Describe the workflow you want to enable
Train-test splits for 2-dimensional targets (i.e. multi-label datasets).
I saw couple of issues related to multi-label classification, but as far as I can tell, train-test-split has not been addressed there.
Related forum questi... | 25,596 | [
-0.008338771760463715,
0.03550012782216072,
-0.00662646209821105,
0.008845926262438297,
0.010621950961649418,
-0.020521080121397972,
0.11198493093252182,
0.05252179875969887,
0.00856536440551281,
-0.06623340398073196,
0.0007798286387696862,
0.0042838589288294315,
-0.043229781091213226,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25595 | [
"New Feature",
"Needs Triage"
] | About using feature_Selection many times
### Describe the workflow you want to enable
I will state my question first.
When using a pipeline that combines a variable selection method with multiple estimators.
If I evaluate them with cross_validation, is there a good way not to evaluate fit for variable selection e... | 25,595 | [
-0.013964634388685226,
0.033470842987298965,
-0.004196327645331621,
-0.029565630480647087,
0.02764299511909485,
0.010703956708312035,
0.05201170966029167,
-0.039568912237882614,
0.08896155655384064,
0.019986549392342567,
-0.005469008348882198,
0.031604960560798645,
0.020737258717417717,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25594 | [
"Bug",
"Needs Decision",
"module:preprocessing"
] | KBinsDiscretizer creates wrong bins.
### Describe the bug
`KBinsDiscretizer` gives wrong bins and wrong transformed data, when the inputs contains only 2 distinct values, and `n_bins=3`. It only produces 1 bin, which is not expected. The warning shows some bins are too small so they are merged.
Note that when s... | 25,594 | [
-0.016194039955735207,
-0.03043607994914055,
-0.0011583914747461677,
0.02513459138572216,
-0.013358461670577526,
-0.01991194859147072,
-0.0035258843563497066,
0.041185397654771805,
-0.06691211462020874,
0.029266351833939552,
0.03823459520936012,
0.033726565539836884,
0.03879675269126892,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25592 | [
"New Feature",
"Needs Decision - Include Feature"
] | set_config(transform_output="pandas") does not act on inverse_transform
### Describe the bug
The new [`set_config(transform_output='pandas')` functionality](https://blog.scikit-learn.org/technical/pandas-dataframe-output-for-sklearn-transformer/) is very useful, but unfortunately it is only taking an effect on the `t... | 25,592 | [
0.00477035203948617,
-0.03487595170736313,
0.05455475673079491,
-0.04689183831214905,
0.07526072859764099,
-0.005822307430207729,
0.06870366632938385,
0.02953951433300972,
0.010492397472262383,
0.015912510454654694,
0.007451601326465607,
0.03936674818396568,
0.0246291421353817,
0.034341782... |
https://github.com/scikit-learn/scikit-learn/issues/25590 | [
"Bug",
"Needs Investigation"
] | Importing BaseEstimator leads to unnecessary memory usage
### Describe the bug
Importing `BaseEstimator` from `sklearn.base` causes a cascade of imports that leads to unnecessary memory usage (500MiB of stuff at peak, see screenshot below).
![Screenshot from 2023-02-10 19-23-53](https://user-images.githubuserconte... | 25,590 | [
0.010707134380936623,
0.030924057587981224,
0.01586436852812767,
0.01737894117832184,
0.030361736193299294,
-0.0012643263908103108,
0.012476467527449131,
0.05008237436413765,
0.02013174444437027,
0.004505818244069815,
0.024752454832196236,
0.047760386019945145,
-0.04311351105570793,
0.0145... |
https://github.com/scikit-learn/scikit-learn/issues/25590 | [
"Bug",
"Needs Investigation"
] | Importing BaseEstimator leads to unnecessary memory usage
### Describe the bug
Importing `BaseEstimator` from `sklearn.base` causes a cascade of imports that leads to unnecessary memory usage (500MiB of stuff at peak, see screenshot below).
![Screenshot from 2023-02-10 19-23-53](https://user-images.githubuserconte... | 25,590 | [
0.010707134380936623,
0.030924057587981224,
0.01586436852812767,
0.01737894117832184,
0.030361736193299294,
-0.0012643263908103108,
0.012476467527449131,
0.05008237436413765,
0.02013174444437027,
0.004505818244069815,
0.024752454832196236,
0.047760386019945145,
-0.04311351105570793,
0.0145... |
https://github.com/scikit-learn/scikit-learn/issues/25590 | [
"Bug",
"Needs Investigation"
] | Importing BaseEstimator leads to unnecessary memory usage
### Describe the bug
Importing `BaseEstimator` from `sklearn.base` causes a cascade of imports that leads to unnecessary memory usage (500MiB of stuff at peak, see screenshot below).
![Screenshot from 2023-02-10 19-23-53](https://user-images.githubuserconte... | 25,590 | [
0.010707134380936623,
0.030924057587981224,
0.01586436852812767,
0.01737894117832184,
0.030361736193299294,
-0.0012643263908103108,
0.012476467527449131,
0.05008237436413765,
0.02013174444437027,
0.004505818244069815,
0.024752454832196236,
0.047760386019945145,
-0.04311351105570793,
0.0145... |
https://github.com/scikit-learn/scikit-learn/issues/25590 | [
"Bug",
"Needs Investigation"
] | Importing BaseEstimator leads to unnecessary memory usage
### Describe the bug
Importing `BaseEstimator` from `sklearn.base` causes a cascade of imports that leads to unnecessary memory usage (500MiB of stuff at peak, see screenshot below).
![Screenshot from 2023-02-10 19-23-53](https://user-images.githubuserconte... | 25,590 | [
0.010707134380936623,
0.030924057587981224,
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0.004505818244069815,
0.024752454832196236,
0.047760386019945145,
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0.0145... |
https://github.com/scikit-learn/scikit-learn/issues/25590 | [
"Bug",
"Needs Investigation"
] | Importing BaseEstimator leads to unnecessary memory usage
### Describe the bug
Importing `BaseEstimator` from `sklearn.base` causes a cascade of imports that leads to unnecessary memory usage (500MiB of stuff at peak, see screenshot below).
![Screenshot from 2023-02-10 19-23-53](https://user-images.githubuserconte... | 25,590 | [
0.010707134380936623,
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0.0145... |
https://github.com/scikit-learn/scikit-learn/issues/25588 | [
"Bug",
"module:ensemble"
] | Broken estimator_ attribute on some ensemble models
### Describe the bug
Several ensemble models raise an error when trying to access the existing `estimator_` attribute.
The problem is that this `property` tries to access `self._estimator`, which is set by `sklearn.ensemble.BaseEnsemble._validate_estimator`, bu... | 25,588 | [
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https://github.com/scikit-learn/scikit-learn/issues/25588 | [
"Bug",
"module:ensemble"
] | Broken estimator_ attribute on some ensemble models
### Describe the bug
Several ensemble models raise an error when trying to access the existing `estimator_` attribute.
The problem is that this `property` tries to access `self._estimator`, which is set by `sklearn.ensemble.BaseEnsemble._validate_estimator`, bu... | 25,588 | [
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0.003003300167620182,
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0.02806214429438114,
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-0.01956339180469513,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25588 | [
"Bug",
"module:ensemble"
] | Broken estimator_ attribute on some ensemble models
### Describe the bug
Several ensemble models raise an error when trying to access the existing `estimator_` attribute.
The problem is that this `property` tries to access `self._estimator`, which is set by `sklearn.ensemble.BaseEnsemble._validate_estimator`, bu... | 25,588 | [
0.017933785915374756,
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0.003003300167620182,
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0.02806214429438114,
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-0.01956339180469513,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25588 | [
"Bug",
"module:ensemble"
] | Broken estimator_ attribute on some ensemble models
### Describe the bug
Several ensemble models raise an error when trying to access the existing `estimator_` attribute.
The problem is that this `property` tries to access `self._estimator`, which is set by `sklearn.ensemble.BaseEnsemble._validate_estimator`, bu... | 25,588 | [
0.017933785915374756,
0.06795180588960648,
0.037985339760780334,
0.003252144902944565,
0.0675790086388588,
0.005514349322766066,
0.003003300167620182,
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-0.0031514375004917383,
0.02806214429438114,
-0.0017131711356341839,
-0.01956339180469513,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25588 | [
"Bug",
"module:ensemble"
] | Broken estimator_ attribute on some ensemble models
### Describe the bug
Several ensemble models raise an error when trying to access the existing `estimator_` attribute.
The problem is that this `property` tries to access `self._estimator`, which is set by `sklearn.ensemble.BaseEnsemble._validate_estimator`, bu... | 25,588 | [
0.017933785915374756,
0.06795180588960648,
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0.003252144902944565,
0.0675790086388588,
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0.003003300167620182,
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-0.0031514375004917383,
0.02806214429438114,
-0.0017131711356341839,
-0.01956339180469513,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25584 | [
"Bug",
"cython"
] | ValueError: buffer source array is read-only when derializing a Tree from a readonly buffer.
As observed on our Circle CI and reproduced locally:
```python-traceback
/home/circleci/project/examples/release_highlights/plot_release_highlights_0_24_0.py failed leaving traceback:
Traceback (most recent call last):
... | 25,584 | [
0.018863171339035034,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25584 | [
"Bug",
"cython"
] | ValueError: buffer source array is read-only when derializing a Tree from a readonly buffer.
As observed on our Circle CI and reproduced locally:
```python-traceback
/home/circleci/project/examples/release_highlights/plot_release_highlights_0_24_0.py failed leaving traceback:
Traceback (most recent call last):
... | 25,584 | [
0.018863171339035034,
0.005551368463784456,
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0.06819485127925873,
-0.020056365057826042,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25584 | [
"Bug",
"cython"
] | ValueError: buffer source array is read-only when derializing a Tree from a readonly buffer.
As observed on our Circle CI and reproduced locally:
```python-traceback
/home/circleci/project/examples/release_highlights/plot_release_highlights_0_24_0.py failed leaving traceback:
Traceback (most recent call last):
... | 25,584 | [
0.018863171339035034,
0.005551368463784456,
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0.02469555474817753,
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0.06819485127925873,
-0.020056365057826042,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25584 | [
"Bug",
"cython"
] | ValueError: buffer source array is read-only when derializing a Tree from a readonly buffer.
As observed on our Circle CI and reproduced locally:
```python-traceback
/home/circleci/project/examples/release_highlights/plot_release_highlights_0_24_0.py failed leaving traceback:
Traceback (most recent call last):
... | 25,584 | [
0.018863171339035034,
0.005551368463784456,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
Just curious, I quickly read the article and I am not to sure this would be a game changer, but maybe I missed something ...
> Before ... | 25,583 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
I think it reduces the likelihood of wasting CI when using conditional delayed merges. | 25,583 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
As I understand it the problem that a merge queue solves is that PRs are "constantly" being merged into `main` and you have to rebase on ... | 25,583 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
From memory, the issue that merge queue solves for happens around ~2 times a year in the past 3 years. For example, when a new test was a... | 25,583 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
I enabled it for `main`. Let's see how it goes. | 25,583 | [
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https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
I had a problem when trying to use it today in #25585. Not sure what's going one because everything was green on this PR but apparently t... | 25,583 | [
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https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
I saw the same issue with the merge queue in https://github.com/scikit-learn/scikit-learn/pull/25613. | 25,583 | [
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0.03... |
https://github.com/scikit-learn/scikit-learn/issues/25583 | [
"RFC"
] | RFC enable github's pull request merge queue?
https://github.blog/changelog/2023-02-08-pull-request-merge-queue-public-beta/
It seems like a nice usability improvement.
COMMENT:
I saw the same issue with the merge queue in https://github.com/scikit-learn/scikit-learn/pull/25633. I increase the wait time to 90 minu... | 25,583 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/25582 | [
"New Feature"
] | Add option to scale Matthews correlation coefficient (MCC) output to the [0, 1] range
### Describe the workflow you want to enable
Matthew's correlation coefficient is known to have a different range of possible values from most other classification performance metrics. While it's usual for metrics to lie in the [0... | 25,582 | [
-0.058877911418676376,
0.03442682325839996,
0.022247163578867912,
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0.014204910956323147,
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0.043771568685770035,
-0.016335900872945786,
... |
https://github.com/scikit-learn/scikit-learn/issues/25582 | [
"New Feature"
] | Add option to scale Matthews correlation coefficient (MCC) output to the [0, 1] range
### Describe the workflow you want to enable
Matthew's correlation coefficient is known to have a different range of possible values from most other classification performance metrics. While it's usual for metrics to lie in the [0... | 25,582 | [
-0.058877911418676376,
0.03442682325839996,
0.022247163578867912,
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-0.00325910747051239,
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0.014204910956323147,
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0.007909304462373257,
-0.004439403302967548,
0.043771568685770035,
-0.016335900872945786,
... |
https://github.com/scikit-learn/scikit-learn/issues/25582 | [
"New Feature"
] | Add option to scale Matthews correlation coefficient (MCC) output to the [0, 1] range
### Describe the workflow you want to enable
Matthew's correlation coefficient is known to have a different range of possible values from most other classification performance metrics. While it's usual for metrics to lie in the [0... | 25,582 | [
-0.058877911418676376,
0.03442682325839996,
0.022247163578867912,
-0.024099156260490417,
-0.00325910747051239,
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0.014204910956323147,
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0.007909304462373257,
-0.004439403302967548,
0.043771568685770035,
-0.016335900872945786,
... |
https://github.com/scikit-learn/scikit-learn/issues/25580 | [
"Bug",
"Needs Triage"
] | Proposal to change default value of n_neighbors in mutual_info_regression
### Describe the bug
Hi, recently I figured out that for short sequences default value of 3 is way too unstable and gives poor results.
Don't know the reasons why 3 was used, my testing shows that 2 is a far more appropriate choice for som... | 25,580 | [
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0.023984547704458237,
0.02914576046168804,
0.02028176560997963,
-0.030... |
https://github.com/scikit-learn/scikit-learn/issues/25580 | [
"Bug",
"Needs Triage"
] | Proposal to change default value of n_neighbors in mutual_info_regression
### Describe the bug
Hi, recently I figured out that for short sequences default value of 3 is way too unstable and gives poor results.
Don't know the reasons why 3 was used, my testing shows that 2 is a far more appropriate choice for som... | 25,580 | [
-0.022717662155628204,
0.043417587876319885,
0.042090222239494324,
0.02722923643887043,
-0.005534678231924772,
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0.04892899468541145,
0.01378382183611393,
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0.023984547704458237,
0.02914576046168804,
0.02028176560997963,
-0.030... |
https://github.com/scikit-learn/scikit-learn/issues/25578 | [
"New Feature",
"Needs Triage"
] | Support pandas nullable dtypes for scoring metrics
### Describe the workflow you want to enable
I would like to be able to pass data with the nullable pandas dtypes (`Int64`, `Float64`, and `boolean`) into sklearn metrics such as `matthews_corrcoef`, `accuracy_score`, and `f1_score` (and more) even if the data does n... | 25,578 | [
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0.02104833349585533,
0.05345699563622475,
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0.004274161532521248,
0.008978099562227726,
-0.009755364619195461,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25578 | [
"New Feature",
"Needs Triage"
] | Support pandas nullable dtypes for scoring metrics
### Describe the workflow you want to enable
I would like to be able to pass data with the nullable pandas dtypes (`Int64`, `Float64`, and `boolean`) into sklearn metrics such as `matthews_corrcoef`, `accuracy_score`, and `f1_score` (and more) even if the data does n... | 25,578 | [
-0.045388974249362946,
0.02104833349585533,
0.05345699563622475,
-0.022434040904045105,
0.10318979620933533,
0.005117651075124741,
0.050001394003629684,
0.04546106606721878,
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0.004274161532521248,
0.008978099562227726,
-0.009755364619195461,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25578 | [
"New Feature",
"Needs Triage"
] | Support pandas nullable dtypes for scoring metrics
### Describe the workflow you want to enable
I would like to be able to pass data with the nullable pandas dtypes (`Int64`, `Float64`, and `boolean`) into sklearn metrics such as `matthews_corrcoef`, `accuracy_score`, and `f1_score` (and more) even if the data does n... | 25,578 | [
-0.045388974249362946,
0.02104833349585533,
0.05345699563622475,
-0.022434040904045105,
0.10318979620933533,
0.005117651075124741,
0.050001394003629684,
0.04546106606721878,
-0.04670000076293945,
-0.044047821313142776,
0.004274161532521248,
0.008978099562227726,
-0.009755364619195461,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25578 | [
"New Feature",
"Needs Triage"
] | Support pandas nullable dtypes for scoring metrics
### Describe the workflow you want to enable
I would like to be able to pass data with the nullable pandas dtypes (`Int64`, `Float64`, and `boolean`) into sklearn metrics such as `matthews_corrcoef`, `accuracy_score`, and `f1_score` (and more) even if the data does n... | 25,578 | [
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0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.019866835325956345,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.011103972792625427,
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0.011001071892678738,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.009836656972765923,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
-0.016266046091914177,
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0.0003716783248819411,
... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.015274446457624435,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
-0.023964442312717438,
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0.008755112066864967,
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0.005487346556037664,
... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.02578023634850979,
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0.010602081194519997,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
-0.04276924207806587,
0.016856199130415916,
0.0062743001617491245,
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0.01407813560217619,
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0.007989776320755482,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
-0.03351351246237755,
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0.005631750915199518,
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0.014590341597795486,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25572 | [
"RFC",
"cython"
] | RFC Guideline for usage of Cython types
## Goal
Have a documented consensus on which types to use in Cython code.
### Types
We should distinguish between floating point numbers and integers. We may also split the use cases of integers: As data value and as index for pointers and memoryviews.
## Linked issues
... | 25,572 | [
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0.013520033098757267,
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https://github.com/scikit-learn/scikit-learn/issues/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 | [
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0.0014105772133916616,
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0.030021214857697487,
0.05505407229065895,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 | [
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0.032884370535612106,
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0.057558681815862656,
... |
https://github.com/scikit-learn/scikit-learn/issues/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 | [
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-0.02802751399576664,
0.0023151077330112457,
0.030444707721471786,
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0.026359964162111282,
0.01691640168428421,
0.030771030113101006,
0.056987132877111435,
0.... |
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