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https://github.com/scikit-learn/scikit-learn/issues/23354 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_ICC.pylatest_conda_forge_mkl ⚠️
**CI Failed on [Linux_Nightly_ICC.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42057&view=logs&j=8628a494-79d0-53fa-274c-1b00464f7121)**
Unable to find junit file. Please see link for details.
COMMENT:
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
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] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
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Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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0.045128... |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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0.045128... |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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0.045128... |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 | [
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0.045128... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
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0.04161198064684868,
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0.08858703076839447,
0.0418... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
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0.019070729613304138,
0.03545624017715454,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
-0.04131172597408295,
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0.03476329892873764,
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0.08990921825170517,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
-0.0479888916015625,
0.013176214881241322,
0.034213535487651825,
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0.058... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
-0.04741818457841873,
0.00494894664734602,
0.034763213247060776,
-0.0059685478918254375,
0.015334702096879482,
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0.05766173079609871,
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0.07990584522485733,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
-0.05435434356331825,
0.0021601158659905195,
0.04041363298892975,
-0.006812757812440395,
0.01773373782634735,
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0.061336029320955276,
-0.04669283702969551,
0.08033714443445206,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 | [
-0.04881756752729416,
-0.0023135636001825333,
0.04496585205197334,
-0.008475595153868198,
0.01323253009468317,
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0.06388787925243378,
-0.05188486725091934,
0.08870575577020645,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
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0.0313028... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
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0.021141299977898598,
-0.02908041514456272,
0.029817944392561913,
0.04003724455833435,
-0.017657484859228134,
0.02176... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
-0.0026436399202793837,
0.13442733883857727,
0.03853732347488403,
-0.024796895682811737,
0.044735752046108246,
0.018503449857234955,
0.02516753226518631,
0.013990696519613266,
0.0037030105013400316,
-0.03512435406446457,
0.026114212349057198,
0.04443218186497688,
-0.019698001444339752,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
0.007320227101445198,
0.1295461505651474,
0.05030876398086548,
-0.011444799602031708,
0.04845556244254112,
0.015857908874750137,
0.006132625509053469,
0.013659306801855564,
0.015121878124773502,
-0.05239928141236305,
0.04430464282631874,
0.07423115521669388,
-0.022193992510437965,
0.031498... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
0.0028539032209664583,
0.1394406259059906,
0.04470443353056908,
-0.01408469583839178,
0.03803091496229172,
0.014417790807783604,
0.020620591938495636,
0.006699100602418184,
0.008761579170823097,
-0.04729718714952469,
0.040665872395038605,
0.0642627701163292,
-0.01953815296292305,
0.0331734... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
0.012217291630804539,
0.1373729705810547,
0.04723174124956131,
-0.014591904357075691,
0.03742597997188568,
0.015082930214703083,
0.02387852594256401,
0.001546042854897678,
0.019658785313367844,
-0.04798645153641701,
0.026682915166020393,
0.05955815315246582,
-0.01759871281683445,
0.0281656... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
0.012003127485513687,
0.13792884349822998,
0.04673099145293236,
-0.015302189625799656,
0.03681706264615059,
0.015200822614133358,
0.024729058146476746,
0.001395852887071669,
0.0194967370480299,
-0.047952644526958466,
0.02717316523194313,
0.05974448099732399,
-0.01758771762251854,
0.0280071... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
-0.005138296168297529,
0.13486404716968536,
0.040925804525613785,
-0.01710638590157032,
0.039308689534664154,
0.015157464891672134,
0.026191547513008118,
0.010327281430363655,
0.00836041197180748,
-0.04460151493549347,
0.043916989117860794,
0.06315984576940536,
-0.021232586354017258,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 | [
-0.00011424878903198987,
0.13396751880645752,
0.04506780579686165,
-0.016991667449474335,
0.04172040522098541,
0.012162633240222931,
0.017749689519405365,
0.005669137462973595,
0.015628715977072716,
-0.04956430569291115,
0.038319289684295654,
0.0647609680891037,
-0.020979415625333786,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 | [
-0.021139271557331085,
0.056966088712215424,
0.027658751234412193,
-0.009214616380631924,
0.07137590646743774,
0.015294316224753857,
0.036240581423044205,
0.016010254621505737,
0.017960024997591972,
-0.010502228513360023,
0.01740957610309124,
0.0002585039765108377,
0.01437030453234911,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 | [
-0.021139271557331085,
0.056966088712215424,
0.027658751234412193,
-0.009214616380631924,
0.07137590646743774,
0.015294316224753857,
0.036240581423044205,
0.016010254621505737,
0.017960024997591972,
-0.010502228513360023,
0.01740957610309124,
0.0002585039765108377,
0.01437030453234911,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 | [
-0.021139271557331085,
0.056966088712215424,
0.027658751234412193,
-0.009214616380631924,
0.07137590646743774,
0.015294316224753857,
0.036240581423044205,
0.016010254621505737,
0.017960024997591972,
-0.010502228513360023,
0.01740957610309124,
0.0002585039765108377,
0.01437030453234911,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 | [
-0.021139271557331085,
0.056966088712215424,
0.027658751234412193,
-0.009214616380631924,
0.07137590646743774,
0.015294316224753857,
0.036240581423044205,
0.016010254621505737,
0.017960024997591972,
-0.010502228513360023,
0.01740957610309124,
0.0002585039765108377,
0.01437030453234911,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 | [
-0.021139271557331085,
0.056966088712215424,
0.027658751234412193,
-0.009214616380631924,
0.07137590646743774,
0.015294316224753857,
0.036240581423044205,
0.016010254621505737,
0.017960024997591972,
-0.010502228513360023,
0.01740957610309124,
0.0002585039765108377,
0.01437030453234911,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 | [
0.006073462311178446,
0.026929780840873718,
0.02641860395669937,
-0.017682678997516632,
0.10799229145050049,
-0.023085301741957664,
-0.004676797892898321,
0.02756945975124836,
-0.027058199048042297,
0.006502240896224976,
0.029745768755674362,
0.013935195282101631,
0.018779782578349113,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23313 | [
"Bug",
"module:model_selection"
] | Problem with maximal `int` value in `train_test_split`
### Describe the bug
It seems that above ~10^9, ints are not accepted and `train_test_split` spits out some nonsense values ... This occurs only if I pass numpy arrays, with lists all seems to work fine.
I have been able to circumvent it by just dividing the v... | 23,313 | [
-0.03150755539536476,
0.011460806243121624,
0.0006138096214272082,
0.05064420774579048,
0.092347152531147,
-0.03066214919090271,
0.052684105932712555,
0.042593058198690414,
0.0055760848335921764,
-0.04317859187722206,
0.023675067350268364,
0.02101411111652851,
-0.05719084292650223,
0.01744... |
https://github.com/scikit-learn/scikit-learn/issues/23313 | [
"Bug",
"module:model_selection"
] | Problem with maximal `int` value in `train_test_split`
### Describe the bug
It seems that above ~10^9, ints are not accepted and `train_test_split` spits out some nonsense values ... This occurs only if I pass numpy arrays, with lists all seems to work fine.
I have been able to circumvent it by just dividing the v... | 23,313 | [
-0.03150755539536476,
0.011460806243121624,
0.0006138096214272082,
0.05064420774579048,
0.092347152531147,
-0.03066214919090271,
0.052684105932712555,
0.042593058198690414,
0.0055760848335921764,
-0.04317859187722206,
0.023675067350268364,
0.02101411111652851,
-0.05719084292650223,
0.01744... |
https://github.com/scikit-learn/scikit-learn/issues/23311 | [
"Needs Triage"
] | Spurious warning with DecisionBoundaryPlot
The following snippet will raise a warning regarding feature names
```python
# %%
from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
X = iris.data[["sepal width (cm)", "petal width (cm)"]]
y = iris.target
# %%
from sklearn.model_selection imp... | 23,311 | [
0.011377418413758278,
0.010878573171794415,
0.00022093865845818073,
-0.020529408007860184,
0.07428107410669327,
0.027364404872059822,
0.06157650426030159,
0.051752761006355286,
0.02495521865785122,
0.04368384927511215,
0.039828553795814514,
0.05071895569562912,
0.003871225519105792,
0.0589... |
https://github.com/scikit-learn/scikit-learn/issues/23311 | [
"Needs Triage"
] | Spurious warning with DecisionBoundaryPlot
The following snippet will raise a warning regarding feature names
```python
# %%
from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
X = iris.data[["sepal width (cm)", "petal width (cm)"]]
y = iris.target
# %%
from sklearn.model_selection imp... | 23,311 | [
0.011377418413758278,
0.010878573171794415,
0.00022093865845818073,
-0.020529408007860184,
0.07428107410669327,
0.027364404872059822,
0.06157650426030159,
0.051752761006355286,
0.02495521865785122,
0.04368384927511215,
0.039828553795814514,
0.05071895569562912,
0.003871225519105792,
0.0589... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.017513196915388107,
0.08132690191268921,
-0.02726983278989792,
-0.011014697141945362,
-0.007937370799481869,
0.027397479861974716,
0.03816850483417511,
-0.011549325659871101,
-0.020109117031097412,
-0.040874142199754715,
-0.012751761823892593,
0.019403599202632904,
-0.02007252536714077,
... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.01263501774519682,
0.07455593347549438,
-0.026773786172270775,
-0.014041769318282604,
-0.006876438856124878,
0.02979540452361107,
0.03498252108693123,
-0.008855581283569336,
-0.02043955959379673,
-0.041092295199632645,
-0.012778573669493198,
0.027524525299668312,
-0.016699228435754776,
... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.016424110159277916,
0.07793688774108887,
-0.02250620536506176,
-0.018382111564278603,
-0.011529472656548023,
0.028137221932411194,
0.04347493499517441,
-0.01644083485007286,
-0.017614934593439102,
-0.0372198112308979,
-0.005755442660301924,
0.021974915638566017,
-0.018351523205637932,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.013438612222671509,
0.06849607825279236,
-0.034558963030576706,
0.0038547951262444258,
0.0017118204850703478,
0.034453753381967545,
0.040206506848335266,
-0.0022198748774826527,
-0.021765898913145065,
-0.04546811059117317,
-0.019059250131249428,
0.04364856705069542,
-0.016444223001599312,... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.0038908631540834904,
0.07838734984397888,
-0.02576703205704689,
-0.02773318439722061,
-0.002700774697586894,
0.03401027247309685,
0.044631633907556534,
-0.0030453307554125786,
-0.01219929102808237,
-0.03421349078416824,
-0.007905795238912106,
0.030901899561285973,
-0.024447310715913773,
... |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 | [
-0.018664555624127388,
0.07395540177822113,
-0.02347511611878872,
-0.015141152776777744,
-0.00823512114584446,
0.0286539476364851,
0.043335821479558945,
-0.013548976741731167,
-0.017501041293144226,
-0.03959216549992561,
-0.008258713409304619,
0.027345076203346252,
-0.017162837088108063,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 | [
-0.015698567032814026,
0.003013543551787734,
0.010043703950941563,
0.011862105689942837,
0.01574283093214035,
-0.03348185867071152,
-0.027398834004998207,
0.01906103827059269,
-0.018930140882730484,
-0.0008486936567351222,
0.0530446395277977,
0.02384430356323719,
-0.011474205181002617,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 | [
-0.015698567032814026,
0.003013543551787734,
0.010043703950941563,
0.011862105689942837,
0.01574283093214035,
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0.01906103827059269,
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0.0530446395277977,
0.02384430356323719,
-0.011474205181002617,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 | [
-0.015698567032814026,
0.003013543551787734,
0.010043703950941563,
0.011862105689942837,
0.01574283093214035,
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0.01906103827059269,
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-0.0008486936567351222,
0.0530446395277977,
0.02384430356323719,
-0.011474205181002617,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23287 | [
"Bug",
"Needs Triage"
] | randomized_svd uses all available cpus
### Describe the bug
When i run randomized_svd it uses all available cpus
### Steps/Code to Reproduce
```
from sklearn.utils.extmath import randomized_svd
a = np.random.rand(1000000,2000)
output = randomized_svd(a, n_components=20, flip_sign=True, n_iter=20, random... | 23,287 | [
0.0005850547458976507,
-0.05727629363536835,
-0.02701561339199543,
0.05711152032017708,
0.018271038308739662,
-0.0005788903217762709,
-0.010837647132575512,
0.02626238390803337,
0.011097907088696957,
0.0005254688439890742,
0.0264580100774765,
0.06483341008424759,
-0.0014278260059654713,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23283 | [
"Bug",
"Needs Triage"
] | i need some help with this pyinstaller
### Describe the bug
when i do the download for pyinstaller it says ERROR
### Steps/Code to Reproduce
pip install pyinstaller
Collecting pyinstaller
Using cached pyinstaller-5.0.1-py3-none-win_amd64.whl (2.0 MB)
Requirement already satisfied: setuptools in c:\program file... | 23,283 | [
0.015662798658013344,
0.03605630621314049,
0.021896403282880783,
0.0005582018638961017,
0.023495329543948174,
0.027885807678103447,
0.04419976845383644,
0.017242249101400375,
-0.005675811320543289,
-0.023613590747117996,
-0.03953969478607178,
-0.004845301620662212,
0.025566471740603447,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23280 | [
"module:covariance",
"Needs Investigation"
] | clarification on OAS estimator formula (sklearn.covariance.OAS) (mean instead of trace is used)
Dear sklearn experts,
I was comparing different shrinkage algorithms and when looking at sklearn implementation of the OAS estimator I found something strange in the definition of the shrinkage factor or at least not cle... | 23,280 | [
-0.018404532223939896,
-0.03347370773553848,
0.03127472102642059,
0.000781567650847137,
-0.0030710704158991575,
-0.012316128239035606,
0.043032728135585785,
0.006221768446266651,
-0.049030039459466934,
-0.001179379876703024,
0.05727499723434448,
0.003442391287535429,
0.06603608280420303,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23277 | [
"Bug",
"module:feature_selection"
] | partial_fit from SelectFromModel doesn't validate the parameters
### Describe the bug
Bug discovered while reviewing #23271.
in `SelectFromModel`, the `partial_fit` method doesn't do any validation. It should do the same validation as the `fit`method. It should also set ``n_features_in_`` and co.
### Steps/Code to ... | 23,277 | [
0.01513330265879631,
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0.02935461699962616,
0.02413327246904373,
0.1120782345533371,
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0.0685739815235138,
-0.03120316192507744,
0.025666063651442528,
0.04364429786801338,
0.0014478429220616817,
0.00868255... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 | [
-0.03046899288892746,
-0.12825840711593628,
0.033571962267160416,
-0.014729443937540054,
0.11690579354763031,
-0.01881992258131504,
0.008240181021392345,
-0.0025302402209490538,
-0.016454946249723434,
0.02545931749045849,
-0.014882701449096203,
0.025470253080129623,
-0.00022309627092909068,
... |
https://github.com/scikit-learn/scikit-learn/issues/23267 | [
"Bug"
] | Regression in `SelectFromModel` where `max_features_` does not exist with `prefit=True`.
### Describe the bug
While testing the RC in `xgboost`, there is a test failure due to a regression after introducing https://github.com/scikit-learn/scikit-learn/pull/22356.
I assume that we did not think about the case when ... | 23,267 | [
-0.033867958933115005,
-0.016313571482896805,
0.011560415849089622,
-0.016956089064478874,
0.08997929096221924,
-0.0569911003112793,
0.018839837983250618,
0.042931973934173584,
0.020454443991184235,
0.011602775193750858,
0.03313681110739708,
0.04613018408417702,
-0.008415618911385536,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 | [
0.010995821096003056,
-0.08047115802764893,
-0.014237514697015285,
-0.04322975128889084,
0.07636870443820953,
0.012369610369205475,
-0.007831829600036144,
0.030825303867459297,
-0.013533373363316059,
-0.023876963183283806,
-0.004627828486263752,
0.10450278222560883,
0.00417361781001091,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 | [
0.010995821096003056,
-0.08047115802764893,
-0.014237514697015285,
-0.04322975128889084,
0.07636870443820953,
0.012369610369205475,
-0.007831829600036144,
0.030825303867459297,
-0.013533373363316059,
-0.023876963183283806,
-0.004627828486263752,
0.10450278222560883,
0.00417361781001091,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 | [
0.010995821096003056,
-0.08047115802764893,
-0.014237514697015285,
-0.04322975128889084,
0.07636870443820953,
0.012369610369205475,
-0.007831829600036144,
0.030825303867459297,
-0.013533373363316059,
-0.023876963183283806,
-0.004627828486263752,
0.10450278222560883,
0.00417361781001091,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23262 | [
"Bug"
] | Randomized SVD benchmark is broken
While updating the call of `fetch_openml`, I saw that the following benchmark is broken:
https://github.com/scikit-learn/scikit-learn/blob/main/benchmarks/bench_plot_randomized_svd.py
We should probably solve the issues.
COMMENT:
I think the first thing to do is to use named par... | 23,262 | [
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0.015444503165781498,
-0.01518832053989172,
0.025019774213433266,
0.06686921417713165,
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0.049889110028743744,
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-0.00861185509711504,
0.02845981903374195,
0.03404955565929413,
0.020728563889861107,
-0.0466... |
https://github.com/scikit-learn/scikit-learn/issues/23257 | [
"New Feature",
"Needs Triage"
] | Add standard-deviation output to sklearn.ensemble.RandomForestRegressor
### Describe the workflow you want to enable
I think it would be awesome if the RF regressor returns the standard deviation (not only the mean) of the output of the different trees.
### Describe your proposed solution
This is not a de... | 23,257 | [
0.022952349856495857,
0.02377036213874817,
0.045644450932741165,
0.02291005849838257,
0.033643290400505066,
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-0.03888122737407684,
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0.012425153516232967,
-0.01839348115026951,
-0.014242151752114296,
0.0011328752152621746,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23257 | [
"New Feature",
"Needs Triage"
] | Add standard-deviation output to sklearn.ensemble.RandomForestRegressor
### Describe the workflow you want to enable
I think it would be awesome if the RF regressor returns the standard deviation (not only the mean) of the output of the different trees.
### Describe your proposed solution
This is not a de... | 23,257 | [
0.02935873158276081,
0.010113297030329704,
0.05076075717806816,
0.009234550409018993,
0.037688467651605606,
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-0.05773744732141495,
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0.01023363322019577,
-0.01888999156653881,
-0.013738545589148998,
0.012814778834581375,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23254 | [
"Documentation"
] | RandomizedSearchCV verbose parameter description is not describing the verbosity levels.
### Describe the issue linked to the documentation
In the website of the RandomizedSearchCV the `verbose` parameter is not discussing the verbosity levels:
"verbose : int
Controls the verbosity: the higher, the more messages."
... | 23,254 | [
0.006256384309381247,
-0.036999400705099106,
0.002046433510258794,
0.011044354178011417,
0.0400322824716568,
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0.035159945487976074,
0.025869376957416534,
-0.0032769169192761183,
0.0390162393450737,
0.05585620179772377,
0.0004363057669252157,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23254 | [
"Documentation"
] | RandomizedSearchCV verbose parameter description is not describing the verbosity levels.
### Describe the issue linked to the documentation
In the website of the RandomizedSearchCV the `verbose` parameter is not discussing the verbosity levels:
"verbose : int
Controls the verbosity: the higher, the more messages."
... | 23,254 | [
0.004039584193378687,
-0.041766781359910965,
0.002802580362185836,
0.01572500541806221,
0.04910415783524513,
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-0.025919748470187187,
0.038729168474674225,
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0.0036591230891644955,
0.03992161899805069,
0.05902814492583275,
-0.0027589411474764347,
0.... |
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