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/27563 | [
"Bug"
] | sklearn.utils._param_validation.InvalidParameterError: The 'zero_division' parameter of precision_score must be a float among {0.0, 1.0, nan} or a str among {'warn'}. Got nan instead
### Describe the bug
I'm trying to use `precision_score` with `np.nan` for the `zero_division`. It's not working with `cross_val_sco... | 27,563 | [
-0.019750749692320824,
-0.05605041980743408,
0.047183047980070114,
-0.02355799451470375,
0.10070694983005524,
-0.010502196848392487,
0.02345777116715908,
0.02122030220925808,
-0.010306431911885738,
-0.019586768001317978,
-0.00012267987767700106,
0.05593046545982361,
0.028991732746362686,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27563 | [
"Bug"
] | sklearn.utils._param_validation.InvalidParameterError: The 'zero_division' parameter of precision_score must be a float among {0.0, 1.0, nan} or a str among {'warn'}. Got nan instead
### Describe the bug
I'm trying to use `precision_score` with `np.nan` for the `zero_division`. It's not working with `cross_val_sco... | 27,563 | [
-0.019750749692320824,
-0.05605041980743408,
0.047183047980070114,
-0.02355799451470375,
0.10070694983005524,
-0.010502196848392487,
0.02345777116715908,
0.02122030220925808,
-0.010306431911885738,
-0.019586768001317978,
-0.00012267987767700106,
0.05593046545982361,
0.028991732746362686,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27563 | [
"Bug"
] | sklearn.utils._param_validation.InvalidParameterError: The 'zero_division' parameter of precision_score must be a float among {0.0, 1.0, nan} or a str among {'warn'}. Got nan instead
### Describe the bug
I'm trying to use `precision_score` with `np.nan` for the `zero_division`. It's not working with `cross_val_sco... | 27,563 | [
-0.019750749692320824,
-0.05605041980743408,
0.047183047980070114,
-0.02355799451470375,
0.10070694983005524,
-0.010502196848392487,
0.02345777116715908,
0.02122030220925808,
-0.010306431911885738,
-0.019586768001317978,
-0.00012267987767700106,
0.05593046545982361,
0.028991732746362686,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27563 | [
"Bug"
] | sklearn.utils._param_validation.InvalidParameterError: The 'zero_division' parameter of precision_score must be a float among {0.0, 1.0, nan} or a str among {'warn'}. Got nan instead
### Describe the bug
I'm trying to use `precision_score` with `np.nan` for the `zero_division`. It's not working with `cross_val_sco... | 27,563 | [
-0.019750749692320824,
-0.05605041980743408,
0.047183047980070114,
-0.02355799451470375,
0.10070694983005524,
-0.010502196848392487,
0.02345777116715908,
0.02122030220925808,
-0.010306431911885738,
-0.019586768001317978,
-0.00012267987767700106,
0.05593046545982361,
0.028991732746362686,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27561 | [
"Bug",
"Needs Triage"
] | MLPClassifier: Cannot turn off convergence warning without side effects
### Describe the bug
As [described](https://github.com/scikit-learn/scikit-learn/discussions/27062) by @qrdlgit, it can have sense to use MLPClassifier with small `max_iter` that are always reached. In this case, I get
```
ConvergenceWarning:... | 27,561 | [
-0.022614937275648117,
0.03213247284293175,
0.00933736190199852,
0.014688138850033283,
0.042674798518419266,
0.014129414223134518,
0.018478643149137497,
0.024579256772994995,
0.03276273235678673,
0.027597304433584213,
0.06895270943641663,
0.03714209794998169,
-0.035900890827178955,
0.00680... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.04911051318049431,
-0.00723282340914011,
0.007740547880530357,
0.021358618512749672,
0.0036691988352686167,
0.022060513496398926,
0.028072578832507133,
0.04737091436982155,
0.04498845338821411,
-0.030464768409729004,
-0.0013906069798395038,
0.11159659177064896,
0.02160823903977871,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.05122510716319084,
-0.027870777994394302,
0.009840022772550583,
0.021053003147244453,
0.00644431309774518,
0.03504372015595436,
0.02048548124730587,
0.03641173988580704,
0.04246700927615166,
-0.0330563560128212,
0.006624648813158274,
0.10084218531847,
0.028260612860322,
-0.02051631361246... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.041246913373470306,
-0.0033754222095012665,
0.007930618710815907,
0.01588336192071438,
-0.0015354728093370795,
0.023976625874638557,
0.02181517891585827,
0.0597626268863678,
0.040030062198638916,
-0.03142935410141945,
-0.015462454408407211,
0.1229286938905716,
0.013352958485484123,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.03946485370397568,
-0.008207553066313267,
0.004174768924713135,
0.014197323471307755,
0.01031583547592163,
0.028662381693720818,
0.006709372624754906,
0.05612363666296005,
0.028461046516895294,
-0.02273702248930931,
0.008120744489133358,
0.12166853249073029,
0.025275498628616333,
-0.0090... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.04205061122775078,
-0.02581905573606491,
0.008239777758717537,
0.025339847430586815,
0.009355094283819199,
0.02731398120522499,
0.02011159248650074,
0.053359366953372955,
0.04282544553279877,
-0.0394531786441803,
0.006285388953983784,
0.12112948298454285,
0.018094096332788467,
-0.0225312... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.04364017769694328,
-0.016781628131866455,
0.008555657230317593,
0.0178295336663723,
0.012129023671150208,
0.03390676900744438,
0.0009152988204732537,
0.049090757966041565,
0.0430096760392189,
-0.03915678709745407,
0.007823695428669453,
0.10721005499362946,
0.021356454119086266,
-0.005963... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.05031250789761543,
-0.010636714287102222,
0.007046273909509182,
0.027074813842773438,
0.008351343683898449,
0.016599778085947037,
0.028981922194361687,
0.04362446442246437,
0.0533347986638546,
-0.03146321326494217,
-0.0070981369353830814,
0.10846715420484543,
0.019639544188976288,
-0.015... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.04050888866186142,
-0.009151865728199482,
0.01383952796459198,
0.01344828587025404,
0.004237587098032236,
0.021545851603150368,
0.04388465732336044,
0.04970066249370575,
0.07403621822595596,
-0.026437537744641304,
-0.017909033223986626,
0.10988591611385345,
0.016781097277998924,
-0.00134... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.05561578646302223,
-0.0073265195824205875,
0.012229484505951405,
0.017604857683181763,
0.0009353070636279881,
0.04969742149114609,
0.014600513502955437,
0.036591511219739914,
0.031694669276475906,
-0.04476324841380119,
0.0006079159793443978,
0.0970512181520462,
0.02576911821961403,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.029787564650177956,
-0.004737951327115297,
0.011251676827669144,
0.01081183273345232,
0.02099064365029335,
0.02409326657652855,
0.015682430937886238,
0.03340427950024605,
0.04093744605779648,
-0.035784751176834106,
0.016781287267804146,
0.08948914706707001,
0.013376927003264427,
-0.03886... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.0442754365503788,
0.003074859268963337,
0.015539394691586494,
0.009292025119066238,
0.030598502606153488,
0.03498224541544914,
0.01872265338897705,
0.04640261083841324,
0.03538979962468147,
-0.03117205947637558,
-0.0018865243764594197,
0.10858674347400665,
0.012540189549326897,
0.0121643... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.03792159631848335,
0.010069083422422409,
-0.0014619120629504323,
0.022725915536284447,
0.007940742187201977,
0.026625415310263634,
0.029681656509637833,
0.04424501582980156,
0.0239374041557312,
-0.031117113307118416,
0.001564434147439897,
0.10377565771341324,
0.011645212769508362,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.04029802232980728,
0.0003530250396579504,
0.0023426383268088102,
0.01453110296279192,
0.007824226282536983,
0.026378799229860306,
0.024654030799865723,
0.04803212359547615,
0.029727885499596596,
-0.03829821199178696,
0.0027103102765977383,
0.10710041224956512,
0.018282154574990273,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.047284603118896484,
0.0035250606015324593,
0.004329969175159931,
0.01725117303431034,
0.0076659731566905975,
0.024715395644307137,
0.026368198916316032,
0.04367692768573761,
0.03051309660077095,
-0.03735481947660446,
0.004875744227319956,
0.10555616766214371,
0.02116732858121395,
-0.0184... |
https://github.com/scikit-learn/scikit-learn/issues/27559 | [
"Documentation"
] | Correctly document linked libraries
### Describe the issue linked to the documentation
When downloading the current wheel for `scikit-learn==1.3.1`, the metadata tell me that the package is subject to the terms of BSD-3-Clause. Unfortunately, this only applies to the package itself. Skimming through the distributed... | 27,559 | [
0.047697536647319794,
-0.027073105797171593,
0.006081114057451487,
0.023441385477781296,
0.0060888598673045635,
0.02314070425927639,
0.016026146709918976,
0.05335257202386856,
0.04101379215717316,
-0.04062503203749657,
0.010053972713649273,
0.11321970075368881,
0.01939864084124565,
-0.0200... |
https://github.com/scikit-learn/scikit-learn/issues/27555 | [
"Bug",
"Needs Triage"
] | Louvain community detection fails to recognize sparse matrix instance
### Describe the bug
TypeError being thrown by sknetwork/utils/check.py:130, in check_format(input_matrix, allow_empty)
I don't think this should be happening.
### Steps/Code to Reproduce
```python
from sknetwork.clustering import Louvain, ge... | 27,555 | [
0.01362523715943098,
-0.03563874587416649,
0.015596888028085232,
0.04222863167524338,
0.06929293274879456,
-0.013898397795855999,
0.03266880288720131,
0.043052125722169876,
0.025462716817855835,
0.021119028329849243,
-0.010150923393666744,
0.01201170776039362,
0.011537028476595879,
0.05193... |
https://github.com/scikit-learn/scikit-learn/issues/27547 | [
"Documentation"
] | Modified huber - Bug in the formula
### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/sgd.html#mathematical-formulation
1.5.8. Mathematical formulation -> Loss function details -> Modified huber loss
The equation written for huber loss contains a bug. it is written as ... | 27,547 | [
-0.045009441673755646,
-0.040533896535634995,
-0.02997572533786297,
-0.005621979478746653,
0.021149154752492905,
-0.04173995926976204,
0.04467672482132912,
-0.006038907449692488,
0.019726866856217384,
-0.0010406780056655407,
0.0668293759226799,
0.01751452498137951,
0.06644031405448914,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27547 | [
"Documentation"
] | Modified huber - Bug in the formula
### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/sgd.html#mathematical-formulation
1.5.8. Mathematical formulation -> Loss function details -> Modified huber loss
The equation written for huber loss contains a bug. it is written as ... | 27,547 | [
-0.04093156009912491,
-0.04017689824104309,
-0.022164324298501015,
0.0041562700644135475,
0.020251289010047913,
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0.03487758710980415,
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0.014065348543226719,
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0.0692085549235344,
0.016701161861419678,
0.058551836758852005,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27547 | [
"Documentation"
] | Modified huber - Bug in the formula
### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/sgd.html#mathematical-formulation
1.5.8. Mathematical formulation -> Loss function details -> Modified huber loss
The equation written for huber loss contains a bug. it is written as ... | 27,547 | [
-0.0467025488615036,
-0.034431133419275284,
-0.03040947951376438,
-0.010037466883659363,
0.02089449018239975,
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0.04401545599102974,
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0.01714439131319523,
0.0014285905053839087,
0.05913741514086723,
0.016738099977374077,
0.06236117705702782,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/27545 | [
"Needs Triage"
] | ⚠️ CI failed on Ubuntu_Atlas.ubuntu_atlas ⚠️
**CI is still failing on [Ubuntu_Atlas.ubuntu_atlas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=60142&view=logs&j=689a1c8f-ff4e-5689-1a1a-6fa551ae9eba)** (Oct 18, 2023)
- test_logistic_regressioncv_class_weights[65-balanced-weight1]
COMMENT:
## ... | 27,545 | [
0.006603738758713007,
0.05062049254775047,
-0.012545238249003887,
-0.03264296054840088,
0.052975717931985855,
0.03242163732647896,
0.03250289335846901,
0.025240274146199226,
0.0016010903054848313,
0.03893356770277023,
0.08227576315402985,
0.007059440482407808,
-0.009039236232638359,
0.0750... |
https://github.com/scikit-learn/scikit-learn/issues/27545 | [
"Needs Triage"
] | ⚠️ CI failed on Ubuntu_Atlas.ubuntu_atlas ⚠️
**CI is still failing on [Ubuntu_Atlas.ubuntu_atlas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=60142&view=logs&j=689a1c8f-ff4e-5689-1a1a-6fa551ae9eba)** (Oct 18, 2023)
- test_logistic_regressioncv_class_weights[65-balanced-weight1]
COMMENT:
Clo... | 27,545 | [
0.008320062421262264,
0.04847424849867821,
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0.05066298320889473,
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0.044570278376340866,
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0.02906368486583233,
0.06666509807109833,
-0.001443964196369052,
0.003346635727211833,
0.07546... |
https://github.com/scikit-learn/scikit-learn/issues/27543 | [
"Bug"
] | Handling 'category' for LightGBM models
### Describe the bug
We should be able to convert some columns in the type 'category' in a DataFrame and let the LightGBM model handle it by itself.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from lightgbm import LGBMClassifier
from... | 27,543 | [
0.027882151305675507,
0.06066090986132622,
0.026052530854940414,
-0.010861529037356377,
0.09535647928714752,
0.046734608709812164,
0.040059637278318405,
0.0335850790143013,
-0.010054280050098896,
-0.05944550409913063,
-0.0065119704231619835,
0.006391366943717003,
0.0029935792554169893,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27543 | [
"Bug"
] | Handling 'category' for LightGBM models
### Describe the bug
We should be able to convert some columns in the type 'category' in a DataFrame and let the LightGBM model handle it by itself.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from lightgbm import LGBMClassifier
from... | 27,543 | [
0.027882151305675507,
0.06066090986132622,
0.026052530854940414,
-0.010861529037356377,
0.09535647928714752,
0.046734608709812164,
0.040059637278318405,
0.0335850790143013,
-0.010054280050098896,
-0.05944550409913063,
-0.0065119704231619835,
0.006391366943717003,
0.0029935792554169893,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27543 | [
"Bug"
] | Handling 'category' for LightGBM models
### Describe the bug
We should be able to convert some columns in the type 'category' in a DataFrame and let the LightGBM model handle it by itself.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from lightgbm import LGBMClassifier
from... | 27,543 | [
0.027882151305675507,
0.06066090986132622,
0.026052530854940414,
-0.010861529037356377,
0.09535647928714752,
0.046734608709812164,
0.040059637278318405,
0.0335850790143013,
-0.010054280050098896,
-0.05944550409913063,
-0.0065119704231619835,
0.006391366943717003,
0.0029935792554169893,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27543 | [
"Bug"
] | Handling 'category' for LightGBM models
### Describe the bug
We should be able to convert some columns in the type 'category' in a DataFrame and let the LightGBM model handle it by itself.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from lightgbm import LGBMClassifier
from... | 27,543 | [
0.027882151305675507,
0.06066090986132622,
0.026052530854940414,
-0.010861529037356377,
0.09535647928714752,
0.046734608709812164,
0.040059637278318405,
0.0335850790143013,
-0.010054280050098896,
-0.05944550409913063,
-0.0065119704231619835,
0.006391366943717003,
0.0029935792554169893,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27540 | [
"New Feature"
] | SelectKBest shouldn't raise if k > n_samples
### Describe the workflow you want to enable
Let's say I want to build a logistic regression model with at most 50 features. I could do that with something like this:
``make_pipeline(ColumnTransformer(...OneHotEncoder(), remainder="passthrough"), SelectKBest(k=50), Log... | 27,540 | [
-0.00007474135782103986,
0.09418867528438568,
-0.008762415498495102,
-0.023501595482230186,
0.04341598600149155,
0.022281164303421974,
0.04819853603839874,
0.07396911829710007,
0.04536071792244911,
0.03850064426660538,
0.12078642100095749,
-0.01376286055892706,
-0.02394435554742813,
0.0826... |
https://github.com/scikit-learn/scikit-learn/issues/27540 | [
"New Feature"
] | SelectKBest shouldn't raise if k > n_samples
### Describe the workflow you want to enable
Let's say I want to build a logistic regression model with at most 50 features. I could do that with something like this:
``make_pipeline(ColumnTransformer(...OneHotEncoder(), remainder="passthrough"), SelectKBest(k=50), Log... | 27,540 | [
-0.004155118018388748,
0.09276267141103745,
-0.010444184765219688,
-0.024699704721570015,
0.0415182039141655,
0.028346141800284386,
0.04994061961770058,
0.07399310171604156,
0.04371275380253792,
0.038201041519641876,
0.12095430493354797,
-0.00972666684538126,
-0.0238169077783823,
0.0870629... |
https://github.com/scikit-learn/scikit-learn/issues/27540 | [
"New Feature"
] | SelectKBest shouldn't raise if k > n_samples
### Describe the workflow you want to enable
Let's say I want to build a logistic regression model with at most 50 features. I could do that with something like this:
``make_pipeline(ColumnTransformer(...OneHotEncoder(), remainder="passthrough"), SelectKBest(k=50), Log... | 27,540 | [
-0.003213985124602914,
0.0828041359782219,
-0.01023756992071867,
-0.021958045661449432,
0.04632923752069473,
0.029397321864962578,
0.04407493770122528,
0.07659770548343658,
0.0339067168533802,
0.03061358816921711,
0.1130087599158287,
-0.008015069179236889,
-0.005626666359603405,
0.09268123... |
https://github.com/scikit-learn/scikit-learn/issues/27535 | [
"Performance",
"Needs Decision",
"module:tree",
"Breaking Change",
"cython"
] | Use float32_t for tree.threshold
The features `X` in our standard decision trees are float32, so it would make sense for the threshold of features to also be float32, see https://github.com/scikit-learn/scikit-learn/blob/8ae5f186986667bc3042a36f5d23e352acc40154/sklearn/tree/_tree.pxd#L31
Note that the Cython trees ... | 27,535 | [
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-0.0062452657148242,
-0.031141940504312515,
0.02135302871465683,
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-0.037707772105932236,
0.005154732149094343,
0.017732517793774605,
-0.0067230756394565105,... |
https://github.com/scikit-learn/scikit-learn/issues/27535 | [
"Performance",
"Needs Decision",
"module:tree",
"Breaking Change",
"cython"
] | Use float32_t for tree.threshold
The features `X` in our standard decision trees are float32, so it would make sense for the threshold of features to also be float32, see https://github.com/scikit-learn/scikit-learn/blob/8ae5f186986667bc3042a36f5d23e352acc40154/sklearn/tree/_tree.pxd#L31
Note that the Cython trees ... | 27,535 | [
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-0.004351526033133268,
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0.019029268994927406,
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-0.03356335684657097,
0.026566684246063232,
0.0218564011156559,
0.009537785314023495,
... |
https://github.com/scikit-learn/scikit-learn/issues/27535 | [
"Performance",
"Needs Decision",
"module:tree",
"Breaking Change",
"cython"
] | Use float32_t for tree.threshold
The features `X` in our standard decision trees are float32, so it would make sense for the threshold of features to also be float32, see https://github.com/scikit-learn/scikit-learn/blob/8ae5f186986667bc3042a36f5d23e352acc40154/sklearn/tree/_tree.pxd#L31
Note that the Cython trees ... | 27,535 | [
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-0.005202796310186386,
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0.025393234565854073,
0.03036470152437687,
0.016289301216602325,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27535 | [
"Performance",
"Needs Decision",
"module:tree",
"Breaking Change",
"cython"
] | Use float32_t for tree.threshold
The features `X` in our standard decision trees are float32, so it would make sense for the threshold of features to also be float32, see https://github.com/scikit-learn/scikit-learn/blob/8ae5f186986667bc3042a36f5d23e352acc40154/sklearn/tree/_tree.pxd#L31
Note that the Cython trees ... | 27,535 | [
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-0.060195449739694595,
-0.01081357803195715,
0.001996516017243266,
0.016297299414873123,
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0.01365438848733902,
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-0.04964197427034378,
0.027448752894997597,
0.018811993300914764,
0.016177412122488022,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27535 | [
"Performance",
"Needs Decision",
"module:tree",
"Breaking Change",
"cython"
] | Use float32_t for tree.threshold
The features `X` in our standard decision trees are float32, so it would make sense for the threshold of features to also be float32, see https://github.com/scikit-learn/scikit-learn/blob/8ae5f186986667bc3042a36f5d23e352acc40154/sklearn/tree/_tree.pxd#L31
Note that the Cython trees ... | 27,535 | [
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-0.04226206988096237,
-0.014522861689329147,
-0.0025444687344133854,
0.03223980590701103,
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0.0219633337110281,
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-0.041553713381290436,
0.0294750165194273,
0.03427978605031967,
-0.006291786674410105,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27533 | [
"Enhancement"
] | Better inference of the columns remainder dtype in `transformers_` from `ColumnTransformer`
A typical use case is to fit a `ColumnTransfomrer` on a pandas dataframe such as:
```python
# %%
from sklearn.datasets import load_iris
df, y = load_iris(return_X_y=True, as_frame=True)
# %%
from sklearn.preprocessi... | 27,533 | [
0.01251441240310669,
0.03625664487481117,
0.043904729187488556,
0.037628792226314545,
0.029136188328266144,
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0.04890739172697067,
0.03416714444756508,
-0.027938930317759514,
-0.004100410733371973,
0.009653210639953613,
-0.020314421504735947,
0.015017999336123466,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/27533 | [
"Enhancement"
] | Better inference of the columns remainder dtype in `transformers_` from `ColumnTransformer`
A typical use case is to fit a `ColumnTransfomrer` on a pandas dataframe such as:
```python
# %%
from sklearn.datasets import load_iris
df, y = load_iris(return_X_y=True, as_frame=True)
# %%
from sklearn.preprocessi... | 27,533 | [
0.01251441240310669,
0.03625664487481117,
0.043904729187488556,
0.037628792226314545,
0.029136188328266144,
0.026335280388593674,
0.04890739172697067,
0.03416714444756508,
-0.027938930317759514,
-0.004100410733371973,
0.009653210639953613,
-0.020314421504735947,
0.015017999336123466,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/27533 | [
"Enhancement"
] | Better inference of the columns remainder dtype in `transformers_` from `ColumnTransformer`
A typical use case is to fit a `ColumnTransfomrer` on a pandas dataframe such as:
```python
# %%
from sklearn.datasets import load_iris
df, y = load_iris(return_X_y=True, as_frame=True)
# %%
from sklearn.preprocessi... | 27,533 | [
0.01251441240310669,
0.03625664487481117,
0.043904729187488556,
0.037628792226314545,
0.029136188328266144,
0.026335280388593674,
0.04890739172697067,
0.03416714444756508,
-0.027938930317759514,
-0.004100410733371973,
0.009653210639953613,
-0.020314421504735947,
0.015017999336123466,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/27533 | [
"Enhancement"
] | Better inference of the columns remainder dtype in `transformers_` from `ColumnTransformer`
A typical use case is to fit a `ColumnTransfomrer` on a pandas dataframe such as:
```python
# %%
from sklearn.datasets import load_iris
df, y = load_iris(return_X_y=True, as_frame=True)
# %%
from sklearn.preprocessi... | 27,533 | [
0.01251441240310669,
0.03625664487481117,
0.043904729187488556,
0.037628792226314545,
0.029136188328266144,
0.026335280388593674,
0.04890739172697067,
0.03416714444756508,
-0.027938930317759514,
-0.004100410733371973,
0.009653210639953613,
-0.020314421504735947,
0.015017999336123466,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/27531 | [
"Bug",
"Needs Triage"
] | NearestNeighbors.kneighbors returns inaccurate distance
### Describe the bug
Using neighbors.NearestNeighbors I noticed that when finding an exact match, kneighbors _sometimes_ returns a distance > 0. (Although the values I've seen so far have been pretty small ~1e-8 to 1e-9)
At first I thought this was a floati... | 27,531 | [
0.005753643810749054,
-0.019670939072966576,
0.003102676710113883,
0.030402060598134995,
0.0234967153519392,
-0.008877146057784557,
0.02474347874522209,
0.03532754257321358,
0.004776114132255316,
-0.023770585656166077,
-0.005324441008269787,
-0.006944810505956411,
0.001175525481812656,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27531 | [
"Bug",
"Needs Triage"
] | NearestNeighbors.kneighbors returns inaccurate distance
### Describe the bug
Using neighbors.NearestNeighbors I noticed that when finding an exact match, kneighbors _sometimes_ returns a distance > 0. (Although the values I've seen so far have been pretty small ~1e-8 to 1e-9)
At first I thought this was a floati... | 27,531 | [
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0.001175525481812656,
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https://github.com/scikit-learn/scikit-learn/issues/27528 | [
"New Feature"
] | Extra plots in partial dependence plots
### Describe the workflow you want to enable
As discussed in #19410, there has been interest in including additional visualizations along with the partial dependence visualizations. Extra plots would aid in the interpretation of partial dependence plots. It would be low overhea... | 27,528 | [
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0.0554... |
https://github.com/scikit-learn/scikit-learn/issues/27528 | [
"New Feature"
] | Extra plots in partial dependence plots
### Describe the workflow you want to enable
As discussed in #19410, there has been interest in including additional visualizations along with the partial dependence visualizations. Extra plots would aid in the interpretation of partial dependence plots. It would be low overhea... | 27,528 | [
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https://github.com/scikit-learn/scikit-learn/issues/27528 | [
"New Feature"
] | Extra plots in partial dependence plots
### Describe the workflow you want to enable
As discussed in #19410, there has been interest in including additional visualizations along with the partial dependence visualizations. Extra plots would aid in the interpretation of partial dependence plots. It would be low overhea... | 27,528 | [
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https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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0.030288... |
https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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0.03189... |
https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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0.0314... |
https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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0.03381... |
https://github.com/scikit-learn/scikit-learn/issues/27522 | [
"module:test-suite"
] | Add new estimator checks for sparse arrays to ensure compatibility for third-party libraries
In #27090, we make changes in our tests to check that our estimators are compatible with sparse arrays. However, it does not intend to write common tests through new checks available in `estimator_checks.py`.
We should impl... | 27,522 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27518 | [
"Bug"
] | Inconsistent results with same random seed
### Describe the bug
I'm not sure this is a bug and I couldn't find anything in the issues archive, but I'm seeing an inconsistency between consecutive runs of kmeans.fit, even when setting the same random seed via np.random.seed or random_state. I pinned it down to multit... | 27,518 | [
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0.0075421323999762535,
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0.008722336031496525,
0.018408652395009995,
0.0339471772313118,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27518 | [
"Bug"
] | Inconsistent results with same random seed
### Describe the bug
I'm not sure this is a bug and I couldn't find anything in the issues archive, but I'm seeing an inconsistency between consecutive runs of kmeans.fit, even when setting the same random seed via np.random.seed or random_state. I pinned it down to multit... | 27,518 | [
-0.003114373655989766,
-0.054277412593364716,
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0.05217548459768295,
0.02234945073723793,
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0.008722336031496525,
0.018408652395009995,
0.0339471772313118,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27518 | [
"Bug"
] | Inconsistent results with same random seed
### Describe the bug
I'm not sure this is a bug and I couldn't find anything in the issues archive, but I'm seeing an inconsistency between consecutive runs of kmeans.fit, even when setting the same random seed via np.random.seed or random_state. I pinned it down to multit... | 27,518 | [
-0.003114373655989766,
-0.054277412593364716,
-0.025264179334044456,
0.05217548459768295,
0.02234945073723793,
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0.0075421323999762535,
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-0.00832293089479208,
0.008722336031496525,
0.018408652395009995,
0.0339471772313118,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27518 | [
"Bug"
] | Inconsistent results with same random seed
### Describe the bug
I'm not sure this is a bug and I couldn't find anything in the issues archive, but I'm seeing an inconsistency between consecutive runs of kmeans.fit, even when setting the same random seed via np.random.seed or random_state. I pinned it down to multit... | 27,518 | [
-0.003114373655989766,
-0.054277412593364716,
-0.025264179334044456,
0.05217548459768295,
0.02234945073723793,
-0.029313698410987854,
0.0075421323999762535,
-0.004730987828224897,
-0.015913456678390503,
-0.00832293089479208,
0.008722336031496525,
0.018408652395009995,
0.0339471772313118,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27518 | [
"Bug"
] | Inconsistent results with same random seed
### Describe the bug
I'm not sure this is a bug and I couldn't find anything in the issues archive, but I'm seeing an inconsistency between consecutive runs of kmeans.fit, even when setting the same random seed via np.random.seed or random_state. I pinned it down to multit... | 27,518 | [
-0.003114373655989766,
-0.054277412593364716,
-0.025264179334044456,
0.05217548459768295,
0.02234945073723793,
-0.029313698410987854,
0.0075421323999762535,
-0.004730987828224897,
-0.015913456678390503,
-0.00832293089479208,
0.008722336031496525,
0.018408652395009995,
0.0339471772313118,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27514 | [
"Documentation"
] | Model Persistence doc page could provide clearer actionable recommendations
### Describe the issue linked to the documentation
The [Model Persistence page](https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst) currently discusses many options (pickling, `skops`, ONNX and PMML, but it doe... | 27,514 | [
0.027877960354089737,
0.04401331767439842,
0.013341568410396576,
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0.05279761552810669,
0.05529532954096794,
-0.00016927943215705454,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/27514 | [
"Documentation"
] | Model Persistence doc page could provide clearer actionable recommendations
### Describe the issue linked to the documentation
The [Model Persistence page](https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst) currently discusses many options (pickling, `skops`, ONNX and PMML, but it doe... | 27,514 | [
0.027877960354089737,
0.04401331767439842,
0.013341568410396576,
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0.00979161262512207,
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0.011016931384801865,
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0.05279761552810669,
0.05529532954096794,
-0.00016927943215705454,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/27514 | [
"Documentation"
] | Model Persistence doc page could provide clearer actionable recommendations
### Describe the issue linked to the documentation
The [Model Persistence page](https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst) currently discusses many options (pickling, `skops`, ONNX and PMML, but it doe... | 27,514 | [
0.027877960354089737,
0.04401331767439842,
0.013341568410396576,
-0.020655252039432526,
0.00979161262512207,
0.009932621382176876,
0.011016931384801865,
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0.023365527391433716,
-0.0366244800388813,
0.05279761552810669,
0.05529532954096794,
-0.00016927943215705454,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/27514 | [
"Documentation"
] | Model Persistence doc page could provide clearer actionable recommendations
### Describe the issue linked to the documentation
The [Model Persistence page](https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst) currently discusses many options (pickling, `skops`, ONNX and PMML, but it doe... | 27,514 | [
0.027877960354089737,
0.04401331767439842,
0.013341568410396576,
-0.020655252039432526,
0.00979161262512207,
0.009932621382176876,
0.011016931384801865,
-0.026998082175850868,
0.023365527391433716,
-0.0366244800388813,
0.05279761552810669,
0.05529532954096794,
-0.00016927943215705454,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/27514 | [
"Documentation"
] | Model Persistence doc page could provide clearer actionable recommendations
### Describe the issue linked to the documentation
The [Model Persistence page](https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst) currently discusses many options (pickling, `skops`, ONNX and PMML, but it doe... | 27,514 | [
0.027877960354089737,
0.04401331767439842,
0.013341568410396576,
-0.020655252039432526,
0.00979161262512207,
0.009932621382176876,
0.011016931384801865,
-0.026998082175850868,
0.023365527391433716,
-0.0366244800388813,
0.05279761552810669,
0.05529532954096794,
-0.00016927943215705454,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27510 | [
"New Feature",
"Needs Triage"
] | GrideSearchCV() has Issue with LSSVM () classification
### Describe the workflow you want to enable
Hi,
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
The code :
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https:/... | 27,510 | [
-0.008980431593954563,
-0.040430158376693726,
0.022948790341615677,
0.03237403929233551,
0.11583055555820465,
-0.0046123662032186985,
0.015615969896316528,
0.03592834249138832,
0.05706718936562538,
0.004850355908274651,
-0.02115505374968052,
0.022450510412454605,
0.005795169621706009,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27508 | [
"Bug",
"Documentation",
"help wanted"
] | Mention that DBSCAN might modify precomputed sparse distance matrix
### Describe the issue linked to the documentation
[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) provides a parameter called `metric` which can be assigned the value `precomputed` so that a precomputed dist... | 27,508 | [
-0.025098448619246483,
-0.021922284737229347,
0.004588172771036625,
0.011082186363637447,
0.044322527945041656,
0.005802694242447615,
0.013896238058805466,
0.025337236002087593,
0.036079589277505875,
0.011511752381920815,
0.016171583905816078,
0.009868409484624863,
0.03128635138273239,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.019858919084072113,
0.06478303670883179,
0.018212128430604935,
-0.023586275056004524,
0.03429833799600601,
-0.04549354314804077,
-0.04158737137913704,
-0.019099097698926926,
-0.030479418113827705,
0.030480962246656418,
0.04933272674679756,
0.017574647441506386,
-0.02876710332930088,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.01996048167347908,
0.06836467236280441,
0.018473338335752487,
-0.025064846500754356,
0.031903330236673355,
-0.04457367956638336,
-0.04067651182413101,
-0.020784609019756317,
-0.030608393251895905,
0.03197701275348663,
0.05334433913230896,
0.01769154891371727,
-0.03121109865605831,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.014424321241676807,
0.06636954098939896,
0.02267441898584366,
-0.026212982833385468,
0.02849111519753933,
-0.04616537690162659,
-0.04730826988816261,
-0.02512938156723976,
-0.0328865647315979,
0.028294183313846588,
0.04861067980527878,
0.01873045414686203,
-0.028467416763305664,
0.04844... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.018718499690294266,
0.06649649143218994,
0.018902745097875595,
-0.024557217955589294,
0.029937583953142166,
-0.04298558086156845,
-0.04154938831925392,
-0.018023597076535225,
-0.03309622034430504,
0.031650252640247345,
0.04995037615299225,
0.01656869612634182,
-0.027031976729631424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.02943768911063671,
0.06400511413812637,
0.019073504954576492,
-0.03133745118975639,
0.046271324157714844,
-0.03278090059757233,
-0.040744878351688385,
-0.016899732872843742,
-0.004705018829554319,
0.03375529497861862,
0.04812582582235336,
0.0271395705640316,
-0.0433032363653183,
0.03550... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.01735144667327404,
0.07500327378511429,
0.015144090168178082,
-0.021458351984620094,
0.017211081460118294,
-0.05125487968325615,
-0.038263726979494095,
-0.017100609838962555,
-0.04216326028108597,
0.025553273037075996,
0.04673093557357788,
0.02117246575653553,
-0.031764283776283264,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27507 | [
"New Feature",
"Needs Info"
] | adding uncertainty quantifier in the "predict" function for DecisionTreeRegressor
### Describe the workflow you want to enable
The "n_node_samples" in "tree_" attribute tracks the number of samples in the leafs. But there should be a easier way of using this quantity for general users.
I hope the following featur... | 27,507 | [
-0.012281347997486591,
0.05603862553834915,
0.02167944237589836,
-0.014065817929804325,
0.03272413834929466,
-0.04269106686115265,
-0.06718362122774124,
-0.021950239315629005,
-0.04491756111383438,
0.02218259684741497,
0.04798789694905281,
0.025474250316619873,
-0.03269069269299507,
0.0365... |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 | [
-0.007362504955381155,
-0.006869887001812458,
0.003251402173191309,
0.009144497103989124,
0.01872413046658039,
-0.01724863238632679,
0.03927259519696236,
0.1038089394569397,
0.056113120168447495,
0.0023646310437470675,
0.016916552558541298,
0.05116027593612671,
-0.002478386741131544,
0.019... |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 | [
-0.007362504955381155,
-0.006869887001812458,
0.003251402173191309,
0.009144497103989124,
0.01872413046658039,
-0.01724863238632679,
0.03927259519696236,
0.1038089394569397,
0.056113120168447495,
0.0023646310437470675,
0.016916552558541298,
0.05116027593612671,
-0.002478386741131544,
0.019... |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 | [
-0.007362504955381155,
-0.006869887001812458,
0.003251402173191309,
0.009144497103989124,
0.01872413046658039,
-0.01724863238632679,
0.03927259519696236,
0.1038089394569397,
0.056113120168447495,
0.0023646310437470675,
0.016916552558541298,
0.05116027593612671,
-0.002478386741131544,
0.019... |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 | [
-0.007362504955381155,
-0.006869887001812458,
0.003251402173191309,
0.009144497103989124,
0.01872413046658039,
-0.01724863238632679,
0.03927259519696236,
0.1038089394569397,
0.056113120168447495,
0.0023646310437470675,
0.016916552558541298,
0.05116027593612671,
-0.002478386741131544,
0.019... |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 | [
-0.007362504955381155,
-0.006869887001812458,
0.003251402173191309,
0.009144497103989124,
0.01872413046658039,
-0.01724863238632679,
0.03927259519696236,
0.1038089394569397,
0.056113120168447495,
0.0023646310437470675,
0.016916552558541298,
0.05116027593612671,
-0.002478386741131544,
0.019... |
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