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/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 | [
-0.0027408336754888296,
-0.05533674731850624,
0.02400643192231655,
0.03757006675004959,
0.08649501204490662,
-0.031765397638082504,
0.05191301181912422,
0.03645620122551918,
-0.00882194098085165,
-0.019224924966692924,
0.027469169348478317,
0.006287117954343557,
0.02593817748129368,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 | [
-0.0027408336754888296,
-0.05533674731850624,
0.02400643192231655,
0.03757006675004959,
0.08649501204490662,
-0.031765397638082504,
0.05191301181912422,
0.03645620122551918,
-0.00882194098085165,
-0.019224924966692924,
0.027469169348478317,
0.006287117954343557,
0.02593817748129368,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 | [
-0.0027408336754888296,
-0.05533674731850624,
0.02400643192231655,
0.03757006675004959,
0.08649501204490662,
-0.031765397638082504,
0.05191301181912422,
0.03645620122551918,
-0.00882194098085165,
-0.019224924966692924,
0.027469169348478317,
0.006287117954343557,
0.02593817748129368,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 | [
-0.0027408336754888296,
-0.05533674731850624,
0.02400643192231655,
0.03757006675004959,
0.08649501204490662,
-0.031765397638082504,
0.05191301181912422,
0.03645620122551918,
-0.00882194098085165,
-0.019224924966692924,
0.027469169348478317,
0.006287117954343557,
0.02593817748129368,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/23411 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | HalvingRandomSearchCV - Custom Factor
### Describe the workflow you want to enable
There is often the problem with successive Halving that there are too many candidates and not enough resources. At the moment you can handle this by using Aggressive Elimination or trying to adjust the halving factor.
I would like t... | 23,411 | [
-0.005618684459477663,
0.018832143396139145,
-0.00417792284861207,
-0.03681338578462601,
0.016072863712906837,
-0.03528532385826111,
-0.003739527892321348,
0.029536619782447815,
-0.03988543152809143,
-0.02870328351855278,
0.042707741260528564,
0.012292105704545975,
-0.08611392229795456,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.0030504034366458654,
-0.01991986110806465,
0.026681480929255486,
-0.024218950420618057,
0.05925038084387779,
0.0393805094063282,
0.04457957297563553,
0.013117323629558086,
-0.006226661615073681,
-0.016528956592082977,
0.04248260706663132,
0.02410115674138069,
0.002371804788708687,
0.0218... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.013504214584827423,
-0.030881021171808243,
0.030798258259892464,
-0.030476033687591553,
0.05130922794342041,
0.049892351031303406,
0.05572133511304855,
0.003718095365911722,
-0.0063159894198179245,
-0.006987675558775663,
0.03776957094669342,
0.01840907707810402,
0.007886956445872784,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.003335821907967329,
-0.012418213300406933,
0.0330197811126709,
-0.03186802193522453,
0.050854746252298355,
0.040540147572755814,
0.0434253104031086,
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0.04855368286371231,
0.012571891769766808,
0.006931743118911982,
0.004... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.004206215031445026,
-0.014766495674848557,
0.028291013091802597,
-0.02664225362241268,
0.05913345515727997,
0.042675990611314774,
0.049355313181877136,
0.008155286312103271,
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-0.010645367205142975,
0.04236855357885361,
0.013252335600554943,
-0.002822305541485548,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.003958750516176224,
-0.023672757670283318,
0.02670198678970337,
-0.021405447274446487,
0.06248505786061287,
0.04197802394628525,
0.04242338612675667,
0.010376976802945137,
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-0.014501615427434444,
0.03865135833621025,
0.023945128545165062,
-0.001013327855616808,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/23408 | [
"Needs Triage"
] | Bug: Not Multiplying by 100 in Mean Absolute Percentage Error
Hi,
I am using Scikit-learn version 1.1.0`.
As I was looking into the implementation of `mean_absolute_percentate_error`, I found that the implementation is missing multiplication by 100 to convert it into a percentage.
See the code below,
https:... | 23,408 | [
0.0035325782373547554,
-0.006769875064492226,
0.02692488208413124,
-0.02754313312470913,
0.05974014848470688,
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0.03876849636435509,
0.015899742022156715,
-0.0018157955491915345,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23405 | [
"module:linear_model",
"Needs Investigation"
] | LassoLars: improve precision at lower regularization values
[LassoLars](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html?highlight=lassolars#sklearn.linear_model.LassoLars) can be quite imprecise at low regularization values (e.g. alpha=alpha_max/1000).
This can be easily solved b... | 23,405 | [
0.0023470513988286257,
0.06069163978099823,
0.026107175275683403,
0.029346399009227753,
0.08023200929164886,
-0.04722822457551956,
-0.005297106225043535,
0.05684821680188179,
-0.010676751844584942,
0.006607681047171354,
-0.014322350732982159,
0.03713162988424301,
0.00441602012142539,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/23405 | [
"module:linear_model",
"Needs Investigation"
] | LassoLars: improve precision at lower regularization values
[LassoLars](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html?highlight=lassolars#sklearn.linear_model.LassoLars) can be quite imprecise at low regularization values (e.g. alpha=alpha_max/1000).
This can be easily solved b... | 23,405 | [
-0.007658967282623053,
0.057161808013916016,
0.030630456283688545,
0.02671879716217518,
0.07618967443704605,
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-0.022381238639354706,
0.053938355296850204,
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0.01943710632622242,
-0.07446824014186859,
0.034784410148859024,
-0.012889249250292778,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.019319208338856697,
0.09222335368394852,
-0.03592067211866379,
-0.012280789203941822,
-0.025545360520482063,
-0.004291930701583624,
0.03729313984513283,
0.0259995236992836,
-0.11403312534093857,
0.0024390683975070715,
0.05525672808289528,
0.00773991271853447,
-0.014537365175783634,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.01957632414996624,
0.09101126343011856,
-0.03523268550634384,
-0.011982903815805912,
-0.026445813477039337,
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0.0366857573390007,
0.026412134990096092,
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0.003218493890017271,
0.0558466836810112,
0.007480499800294638,
-0.013343681581318378,
0.079... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.024709582328796387,
0.08633659034967422,
-0.049953654408454895,
-0.01553285215049982,
-0.009220007807016373,
-0.018474822863936424,
0.014442012645304203,
0.016307687386870384,
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0.01106527540832758,
0.035309843719005585,
0.013366368599236012,
-0.02345164865255356,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.04371337220072746,
0.09827535599470139,
-0.03176238387823105,
-0.00997153390198946,
-0.014166126027703285,
0.0030760299414396286,
0.015637319535017014,
0.02142547070980072,
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0.01296362653374672,
0.043974269181489944,
0.012153387069702148,
-0.0068858168087899685,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.037538230419158936,
0.09168577194213867,
-0.03047918900847435,
-0.0058836243115365505,
-0.0092995660379529,
0.004049842245876789,
0.013567503541707993,
0.02435150183737278,
-0.11417859047651291,
0.003638738300651312,
0.04005564749240875,
0.01916283741593361,
-0.007086530327796936,
0.081... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.014459071680903435,
0.0863146185874939,
-0.04434599354863167,
-0.0037775395903736353,
-0.028534170240163803,
-0.015528430230915546,
0.030434150248765945,
0.012470477260649204,
-0.10662220418453217,
-0.0014551468193531036,
0.030299723148345947,
0.01754727214574814,
-0.022567234933376312,
... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.016749883070588112,
0.08542943000793457,
-0.048033636063337326,
-0.007638432085514069,
-0.025817466899752617,
-0.016436802223324776,
0.028133200481534004,
0.010338167659938335,
-0.10463793575763702,
-0.0004957486526109278,
0.03403543308377266,
0.018273716792464256,
-0.023983145132660866,
... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.008075752295553684,
0.09809985011816025,
-0.048769231885671616,
-0.005104044917970896,
-0.02462456002831459,
-0.021921124309301376,
0.033404406160116196,
0.0077101388014853,
-0.1079869195818901,
-0.003927865065634251,
0.03552674129605293,
0.01686590164899826,
-0.031892456114292145,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.009802719578146935,
0.10008261352777481,
-0.04694684222340584,
-0.004117477685213089,
-0.025100328028202057,
-0.019407330080866814,
0.03237336501479149,
0.00828615389764309,
-0.10974223166704178,
-0.007261964492499828,
0.032657742500305176,
0.01538208406418562,
-0.02752683125436306,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.0032853519078344107,
0.08352727442979813,
-0.052900370210409164,
-0.011019294150173664,
-0.020265109837055206,
-0.016171829774975777,
0.021473094820976257,
0.011462235823273659,
-0.13609516620635986,
-0.0026904274709522724,
0.03351407125592232,
0.00031536369351670146,
-0.01378872245550155... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.020316533744335175,
0.09686543047428131,
-0.05082109943032265,
-0.015765735879540443,
-0.011549741961061954,
-0.015004252083599567,
0.022128775715827942,
0.012422623112797737,
-0.10890417546033859,
0.001023304183036089,
0.028929520398378372,
0.021318435668945312,
-0.02120632492005825,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
-0.014207901433110237,
0.08386164158582687,
-0.05016709864139557,
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0.027135463431477547,
0.012502462603151798,
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0.003987599164247513,
0.0418093316257,
0.00956745631992817,
-0.01905762031674385,
0.07888... |
https://github.com/scikit-learn/scikit-learn/issues/23401 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Grouping of infrequent categories in 𝗢𝗻𝗲𝗛𝗼𝘁𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Dear all,
This feature is very similar to the one presented in this paper:
https://ieeexplore.ieee.org/document/8851888
which states the following paragraph:
The goal of the PCP transform is to substantially reduce the input memory and processing ... | 23,401 | [
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0.012651341035962105,
-0.02580094337463379,
0.079... |
https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
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https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
-0.036015719175338745,
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0.007081317715346813,
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-0... |
https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
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-0.009... |
https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
-0.03486704081296921,
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0.013374186120927334,
-0.009172090329229832,
-0.010097838938236237,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/23400 | [
"New Feature",
"module:ensemble"
] | Store the OOB Loss for `GradientBoostingClassifier`
### Describe the workflow you want to enable
Currently the only OOB-related performance metric we store on `GradientBoostingClassifier` is `oob_improvement_`, which is an array of OOB loss decreases per iteration. However, it would also be useful to track the *actua... | 23,400 | [
-0.03046332113444805,
0.04986675828695297,
0.04132247716188431,
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0.014218329451978207,
-0.012881256639957428,
-0.007618756964802742,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
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-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23397 | [
"Bug",
"Blocker",
"Regression",
"High Priority"
] | `DecisionTreeClassifier` became slower in v1.1 when fitting encoded variables
### Describe the bug
The evaluation of a pipeline that encodes categorical data with v1.1 takes around 8 times longer than using v1.0.2
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
from time import... | 23,397 | [
-0.01671413518488407,
0.05160403251647949,
0.01182473637163639,
-0.018085788935422897,
0.06921174377202988,
0.01159169152379036,
-0.036178022623062134,
0.06348440051078796,
-0.053410161286592484,
-0.019210685044527054,
0.07784955203533173,
0.0651020035147667,
0.04544825851917267,
0.0235902... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.04308125376701355,
-0.018653014674782753,
0.00882016308605671,
-0.04030201584100723,
0.025170153006911278,
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0.05859335511922836,
-0.030768556520342827,
-0.0335405133664608,
0.0028329419437795877,
0.05289449170231819,
0.03373942896723747,
0.06068352237343788,
0.0335... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.037011388689279556,
-0.016155073419213295,
0.012471671216189861,
-0.04265904799103737,
0.01603846438229084,
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0.06846781075000763,
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0.008220597170293331,
0.0447019599378109,
0.04344078153371811,
0.07555064558982849,
0.0290... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.030509134754538536,
-0.02418825961649418,
-0.0022828085348010063,
-0.05517662689089775,
0.014293071813881397,
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0.05524156242609024,
-0.027623984962701797,
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0.008816546760499477,
0.05541396886110306,
0.06605805456638336,
0.04993920400738716,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.05003219470381737,
-0.016387490555644035,
0.008674409240484238,
-0.044306445866823196,
0.028773872181773186,
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0.05828215926885605,
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0.01031025405973196,
0.048794664442539215,
0.047096285969018936,
0.056732408702373505,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.046496544033288956,
-0.014544757083058357,
0.008246064186096191,
-0.037353988736867905,
0.019458401948213577,
-0.011667686514556408,
0.05842582881450653,
-0.022784678265452385,
-0.029891327023506165,
0.00396556593477726,
0.0534011609852314,
0.058647915720939636,
0.05496314540505409,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.04277617484331131,
-0.04277248680591583,
0.025267979130148888,
-0.05167241394519806,
0.04031818360090256,
-0.014147122390568256,
0.055996768176555634,
-0.018826540559530258,
-0.027283985167741776,
0.006170861888676882,
0.038097918033599854,
0.09837670624256134,
0.04398861899971962,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.026847345754504204,
0.0053279586136341095,
0.003378696972504258,
-0.062373898923397064,
0.011363834142684937,
-0.008925926871597767,
0.04775163531303406,
-0.031482309103012085,
-0.037994951009750366,
0.005081915762275457,
0.07047735154628754,
0.03979973495006561,
0.06330735236406326,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.05636771395802498,
-0.045462045818567276,
0.013985239900648594,
-0.049294110387563705,
0.03153051435947418,
-0.014333987608551979,
0.06261315941810608,
-0.03643576055765152,
-0.04365082085132599,
0.021136930212378502,
0.049570512026548386,
0.04654601588845253,
0.06292474269866943,
0.026... |
https://github.com/scikit-learn/scikit-learn/issues/23394 | [
"Documentation",
"module:feature_selection"
] | VarianceThreshold does not state whether normalisation is required
### Describe the issue linked to the documentation
Is normalisation required? If so, it would be good to state this in the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
The example appl... | 23,394 | [
-0.049192894250154495,
-0.021111169829964638,
-0.0013197376392781734,
-0.05580856651067734,
0.020848127081990242,
-0.016475606709718704,
0.04991336539387703,
-0.027382435277104378,
-0.024409975856542587,
0.012659533880650997,
0.06064502149820328,
0.04053983837366104,
0.0452788807451725,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23393 | [
"Bug",
"Regression",
"Needs Triage"
] | KeyError raised when using pandas DataFrame in SelectFromModel.fit()
### Describe the bug
When passing X to SelectFromModel.fit() where X is a pandas DatafFrame, a keyerror is raised at
https://github.com/scikit-learn/scikit-learn/blob/16625450b58f555dc3955d223f0c3b64a5686984/sklearn/feature_selection/_from_mode... | 23,393 | [
0.008812502026557922,
0.00029058963991701603,
0.02570149675011635,
-0.011476176790893078,
0.08707205951213837,
0.012745318002998829,
0.0500970184803009,
0.02922411821782589,
0.04281267896294594,
-0.031820766627788544,
0.04628180339932442,
0.042263519018888474,
0.0215882807970047,
0.0783503... |
https://github.com/scikit-learn/scikit-learn/issues/23390 | [
"Documentation",
"module:linear_model"
] | Perceptron.t_ appears off by 1
### Describe the bug
The [docs](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html) state that `Perceptron.t_` is the "[n]umber of weight updates performed during training" and that it should be the "[s]ame as `(n_iter_ * n_samples)`." However, the va... | 23,390 | [
-0.03990431874990463,
-0.026060594245791435,
0.01219145581126213,
0.03858531638979912,
0.005964125506579876,
-0.004051409661769867,
0.009664199315011501,
-0.01070704497396946,
0.0007010680856183171,
0.0005283980281092227,
0.0359201543033123,
0.0056571816094219685,
-0.006373926065862179,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23390 | [
"Documentation",
"module:linear_model"
] | Perceptron.t_ appears off by 1
### Describe the bug
The [docs](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html) state that `Perceptron.t_` is the "[n]umber of weight updates performed during training" and that it should be the "[s]ame as `(n_iter_ * n_samples)`." However, the va... | 23,390 | [
-0.03990431874990463,
-0.026060594245791435,
0.01219145581126213,
0.03858531638979912,
0.005964125506579876,
-0.004051409661769867,
0.009664199315011501,
-0.01070704497396946,
0.0007010680856183171,
0.0005283980281092227,
0.0359201543033123,
0.0056571816094219685,
-0.006373926065862179,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23390 | [
"Documentation",
"module:linear_model"
] | Perceptron.t_ appears off by 1
### Describe the bug
The [docs](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html) state that `Perceptron.t_` is the "[n]umber of weight updates performed during training" and that it should be the "[s]ame as `(n_iter_ * n_samples)`." However, the va... | 23,390 | [
-0.03990431874990463,
-0.026060594245791435,
0.01219145581126213,
0.03858531638979912,
0.005964125506579876,
-0.004051409661769867,
0.009664199315011501,
-0.01070704497396946,
0.0007010680856183171,
0.0005283980281092227,
0.0359201543033123,
0.0056571816094219685,
-0.006373926065862179,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23383 | [
"Bug",
"Needs Triage"
] | Unable to import joblib after update to 1.1.0
### Describe the bug
Hi community,
I was very excited after the get_features_names_out fixes in 1.1 and I wanted to incorporate changes in my training code according to that.
However now Im getting and error regarding joblib. Code to reproduce below:
### Steps/... | 23,383 | [
0.013976622372865677,
0.04263146221637726,
0.003910135477781296,
-0.08300279825925827,
0.034561887383461,
0.02398950792849064,
0.024585291743278503,
0.05099406838417053,
0.01856887899339199,
0.013125056400895119,
0.05483737960457802,
0.0849834531545639,
-0.010248917154967785,
0.04485856369... |
https://github.com/scikit-learn/scikit-learn/issues/23383 | [
"Bug",
"Needs Triage"
] | Unable to import joblib after update to 1.1.0
### Describe the bug
Hi community,
I was very excited after the get_features_names_out fixes in 1.1 and I wanted to incorporate changes in my training code according to that.
However now Im getting and error regarding joblib. Code to reproduce below:
### Steps/... | 23,383 | [
0.013976622372865677,
0.04263146221637726,
0.003910135477781296,
-0.08300279825925827,
0.034561887383461,
0.02398950792849064,
0.024585291743278503,
0.05099406838417053,
0.01856887899339199,
0.013125056400895119,
0.05483737960457802,
0.0849834531545639,
-0.010248917154967785,
0.04485856369... |
https://github.com/scikit-learn/scikit-learn/issues/23383 | [
"Bug",
"Needs Triage"
] | Unable to import joblib after update to 1.1.0
### Describe the bug
Hi community,
I was very excited after the get_features_names_out fixes in 1.1 and I wanted to incorporate changes in my training code according to that.
However now Im getting and error regarding joblib. Code to reproduce below:
### Steps/... | 23,383 | [
0.013976622372865677,
0.04263146221637726,
0.003910135477781296,
-0.08300279825925827,
0.034561887383461,
0.02398950792849064,
0.024585291743278503,
0.05099406838417053,
0.01856887899339199,
0.013125056400895119,
0.05483737960457802,
0.0849834531545639,
-0.010248917154967785,
0.04485856369... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23382 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | CV integration for OOB-scoring
### Describe the workflow you want to enable
Out-of-Bag (OOB) scoring provides an estimate of the model generalizability for `RandomForest` without needing to refit the model several times as is demanded by k-fold cross validation (CV). Although `sklearn` provides a mechanism to obtain ... | 23,382 | [
-0.018630757927894592,
-0.02227131277322769,
0.061804480850696564,
-0.012866939418017864,
0.04186616837978363,
-0.006155833601951599,
-0.032199230045080185,
0.008185661397874355,
0.03960144892334938,
-0.030993450433015823,
-0.0032659205608069897,
0.05975736305117607,
-0.013854634948074818,
... |
https://github.com/scikit-learn/scikit-learn/issues/23381 | [
"Bug",
"module:datasets"
] | fetch_openml difference between pandas and liac-arff parser
Seen in a [scipy-dev build](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42132&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf&t=a5a438e1-a911-5517-158f-26a140e5cbbf).
There are additional quotes in the pandas parser case.
cc ... | 23,381 | [
0.07774775475263596,
-0.029940124601125717,
0.013826542533934116,
0.04036647453904152,
0.08006052672863007,
0.01869947649538517,
0.06212995946407318,
0.0037231435999274254,
-0.008833980187773705,
-0.04114920645952225,
-0.028133567422628403,
0.04605485498905182,
0.03977145627140999,
-0.0301... |
https://github.com/scikit-learn/scikit-learn/issues/23381 | [
"Bug",
"module:datasets"
] | fetch_openml difference between pandas and liac-arff parser
Seen in a [scipy-dev build](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42132&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf&t=a5a438e1-a911-5517-158f-26a140e5cbbf).
There are additional quotes in the pandas parser case.
cc ... | 23,381 | [
0.07774775475263596,
-0.029940124601125717,
0.013826542533934116,
0.04036647453904152,
0.08006052672863007,
0.01869947649538517,
0.06212995946407318,
0.0037231435999274254,
-0.008833980187773705,
-0.04114920645952225,
-0.028133567422628403,
0.04605485498905182,
0.03977145627140999,
-0.0301... |
https://github.com/scikit-learn/scikit-learn/issues/23377 | [
"Bug",
"Needs Triage"
] | Scorer in sklearn.linear_model.RidgeCV
### Describe the bug
When changing scoring methods in sklearn.linear_model.RidgeCV the output remains the same
Is there a way to have this work properly?
### S... | 23,377 | [
-0.0005548651679418981,
-0.03738036006689072,
0.039437782019376755,
0.048605967313051224,
0.08550675213336945,
-0.03181198984384537,
0.023792123422026634,
0.03553040698170662,
-0.01006923709064722,
0.006544039119035006,
-0.0023236845154315233,
0.1137121319770813,
0.02417345531284809,
0.043... |
https://github.com/scikit-learn/scikit-learn/issues/23377 | [
"Bug",
"Needs Triage"
] | Scorer in sklearn.linear_model.RidgeCV
### Describe the bug
When changing scoring methods in sklearn.linear_model.RidgeCV the output remains the same
Is there a way to have this work properly?
### S... | 23,377 | [
0.02031026966869831,
-0.0298036877065897,
0.02446056343615055,
0.07157156616449356,
0.06712193787097931,
-0.019997654482722282,
0.031184526160359383,
0.030954377725720406,
-0.04273267090320587,
0.010453709401190281,
0.0015015163226053119,
0.13405741751194,
0.01482888963073492,
0.0259493701... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23376 | [
"Bug",
"module:linear_model",
"float32"
] | The data type of input data for LinearRegression class will affect the results
### Describe the bug
Our team just used the class sklearn.linear_model.LinearRegression to do multi-linear regression. And, we found out that the same data, which means the values are identical for each element, with different data forma... | 23,376 | [
0.006342042703181505,
0.003774345386773348,
0.009260866791009903,
0.0666467621922493,
0.08557234704494476,
0.004863350186496973,
0.07904215902090073,
0.03803336247801781,
0.012981303036212921,
-0.02845483087003231,
-0.012977400794625282,
0.017029263079166412,
0.04479742795228958,
0.0043926... |
https://github.com/scikit-learn/scikit-learn/issues/23375 | [
"Documentation",
"Needs Triage"
] | SGDRegressor documentation refers to non-existent loss metric
### Describe the issue linked to the documentation
The documentation refers to "SGDRegressor(loss='squared_error') in various places throughout, including an example in paragraph 3 and the descriptions of acceptable values in section 1.5.2. However, testi... | 23,375 | [
-0.03780986741185188,
0.0037827808409929276,
0.028507176786661148,
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0.054269030690193176,
0.01245601661503315,
0.06302998960018158,
0.027549007907509804,
0.001291449647396803,
0.004497066605836153,
0.07980401068925858,
-0.019675416871905327,
0.019030628725886345,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/23375 | [
"Documentation",
"Needs Triage"
] | SGDRegressor documentation refers to non-existent loss metric
### Describe the issue linked to the documentation
The documentation refers to "SGDRegressor(loss='squared_error') in various places throughout, including an example in paragraph 3 and the descriptions of acceptable values in section 1.5.2. However, testi... | 23,375 | [
-0.03523208200931549,
-0.011802665889263153,
0.022665176540613174,
-0.02724144235253334,
0.03678210824728012,
0.009617014788091183,
0.05798133835196495,
0.007585413288325071,
-0.005257162731140852,
0.00994072575122118,
0.08245854079723358,
-0.021708864718675613,
0.023963162675499916,
0.003... |
https://github.com/scikit-learn/scikit-learn/issues/23369 | [
"Needs Triage"
] | ImportError: cannot import name '_joblib_parallel_args' from 'sklearn.utils.fixes' (/Users/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py)
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/var/folders... | 23,369 | [
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0.0266036968678236,
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0.02175780199468136,
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0.04549141973257065,
0.03156505897641182,
-0.011352951638400555,
-0.0059558... |
https://github.com/scikit-learn/scikit-learn/issues/23368 | [
"Bug",
"Easy",
"help wanted",
"module:metrics"
] | sklearn.metrics.coverage_error wrong error message for 1D array
### Describe the bug
Let y_true and y_score be numpy arrays of shape (K,),
when you try to run the "sklearn.metrics.coverage_error" as explained in the documentation
it returns "binary type not supported" error, but this is not the case at all, the m... | 23,368 | [
0.0024320560041815042,
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0.016577910631895065,
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0.08526396006345749,
0.02180831879377365,
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0.04212082549929619,
-0.013964908197522163,
0.049169689416885376,
0.04812430590391159,
0.0014972377102822065,
0.051... |
https://github.com/scikit-learn/scikit-learn/issues/23366 | [
"Needs Triage"
] | KMeans processing n_init sequentially!!
Hi,
I was looking into KMeans code and found that the following can be parallelized. For example, each work in `for loop` can be processed independently. I expect this to reduce the runtime. Please check.
https://github.com/scikit-learn/scikit-learn/blob/84f8409dc5c4857296... | 23,366 | [
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-0.008152051828801632,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
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-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
0.02063417248427868,
-0.008109153248369694,
0.020773334428668022,
0.01399327628314495,
0.008674479089677334,
-0.02835298888385296,
-0.04262099787592888,
-0.019837120547890663,
-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
0.02063417248427868,
-0.008109153248369694,
0.020773334428668022,
0.01399327628314495,
0.008674479089677334,
-0.02835298888385296,
-0.04262099787592888,
-0.019837120547890663,
-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
0.02063417248427868,
-0.008109153248369694,
0.020773334428668022,
0.01399327628314495,
0.008674479089677334,
-0.02835298888385296,
-0.04262099787592888,
-0.019837120547890663,
-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
0.02063417248427868,
-0.008109153248369694,
0.020773334428668022,
0.01399327628314495,
0.008674479089677334,
-0.02835298888385296,
-0.04262099787592888,
-0.019837120547890663,
-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23363 | [
"Documentation",
"module:metrics"
] | DOC cross-reference balanced accuracy (unadj.) being identical to macro avg recall and make link for accuracy being identical to weighted avg recall
### Describe the workflow you want to enable
_[Please add label: module:metrics]_
In the output of metrics.classification_report, it should be explicitly indicated th... | 23,363 | [
-0.0475027933716774,
-0.01644485630095005,
0.02063417248427868,
-0.008109153248369694,
0.020773334428668022,
0.01399327628314495,
0.008674479089677334,
-0.02835298888385296,
-0.04262099787592888,
-0.019837120547890663,
-0.0636572539806366,
-0.024195153266191483,
0.05018377676606178,
0.0471... |
https://github.com/scikit-learn/scikit-learn/issues/23357 | [
"Bug"
] | fetch_openml fails on leukemia
### Describe the bug
Downloading leukemia dataset with `fetch_openml` fails
Visiting the link causing the tiemout https://openml.org/api/v1/json/data/list/data_name/leukemia/limit/2/status/active/ redirects me to
https://old.openml.org/api/v1/json/data/list/data_name/leukemia/limit... | 23,357 | [
0.01637493446469307,
0.0016175612108781934,
0.000465076562250033,
-0.006096378900110722,
0.06595730781555176,
0.019827373325824738,
-0.01065507810562849,
0.04495902359485626,
0.01844942569732666,
0.030455762520432472,
-0.04520638659596443,
0.004393664188683033,
0.009709562174975872,
0.0164... |
https://github.com/scikit-learn/scikit-learn/issues/23357 | [
"Bug"
] | fetch_openml fails on leukemia
### Describe the bug
Downloading leukemia dataset with `fetch_openml` fails
Visiting the link causing the tiemout https://openml.org/api/v1/json/data/list/data_name/leukemia/limit/2/status/active/ redirects me to
https://old.openml.org/api/v1/json/data/list/data_name/leukemia/limit... | 23,357 | [
0.01637493446469307,
0.0016175612108781934,
0.000465076562250033,
-0.006096378900110722,
0.06595730781555176,
0.019827373325824738,
-0.01065507810562849,
0.04495902359485626,
0.01844942569732666,
0.030455762520432472,
-0.04520638659596443,
0.004393664188683033,
0.009709562174975872,
0.0164... |
https://github.com/scikit-learn/scikit-learn/issues/23357 | [
"Bug"
] | fetch_openml fails on leukemia
### Describe the bug
Downloading leukemia dataset with `fetch_openml` fails
Visiting the link causing the tiemout https://openml.org/api/v1/json/data/list/data_name/leukemia/limit/2/status/active/ redirects me to
https://old.openml.org/api/v1/json/data/list/data_name/leukemia/limit... | 23,357 | [
0.01637493446469307,
0.0016175612108781934,
0.000465076562250033,
-0.006096378900110722,
0.06595730781555176,
0.019827373325824738,
-0.01065507810562849,
0.04495902359485626,
0.01844942569732666,
0.030455762520432472,
-0.04520638659596443,
0.004393664188683033,
0.009709562174975872,
0.0164... |
https://github.com/scikit-learn/scikit-learn/issues/23357 | [
"Bug"
] | fetch_openml fails on leukemia
### Describe the bug
Downloading leukemia dataset with `fetch_openml` fails
Visiting the link causing the tiemout https://openml.org/api/v1/json/data/list/data_name/leukemia/limit/2/status/active/ redirects me to
https://old.openml.org/api/v1/json/data/list/data_name/leukemia/limit... | 23,357 | [
0.01637493446469307,
0.0016175612108781934,
0.000465076562250033,
-0.006096378900110722,
0.06595730781555176,
0.019827373325824738,
-0.01065507810562849,
0.04495902359485626,
0.01844942569732666,
0.030455762520432472,
-0.04520638659596443,
0.004393664188683033,
0.009709562174975872,
0.0164... |
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:
It... | 23,354 | [
-0.0034711852204054594,
0.0071434746496379375,
-0.048152536153793335,
-0.04959367960691452,
-0.026073534041643143,
-0.00002877541737689171,
0.010164388455450535,
0.07085973769426346,
0.0000644838364678435,
0.04355834797024727,
0.04020190238952637,
0.01865185610949993,
0.0021063098683953285,
... |
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:
##... | 23,354 | [
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0.012006435543298721,
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0.010208102874457836,
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0.023269403725862503,
0.056313663721084595,
0.012199447490274906,
0.02341778576374054,
0.016436249017715454,
0.03362574055790901,
-0.013836679980158806,
0.06... |
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