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/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24831 | [
"pypy"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 04, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
I merged #24803 with a ... | 24,831 | [
0.044158730655908585,
0.012034796178340912,
-0.009119955822825432,
-0.07491080462932587,
0.022203776985406876,
0.030601482838392258,
0.03710562363266945,
0.04699617624282837,
0.01200021244585514,
0.03030441328883171,
0.02724822424352169,
0.04567340761423111,
-0.013165057636797428,
0.031047... |
https://github.com/scikit-learn/scikit-learn/issues/24831 | [
"pypy"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 04, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no longer fail... | 24,831 | [
0.024221492931246758,
0.008539585396647453,
-0.01928858831524849,
-0.08332347124814987,
0.02065839059650898,
0.02655371092259884,
0.029153548181056976,
0.04615214094519615,
0.015447568148374557,
0.04264361783862114,
0.035564832389354706,
0.0322132371366024,
-0.024300292134284973,
0.0573994... |
https://github.com/scikit-learn/scikit-learn/issues/24830 | [
"Bug",
"Low Priority"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI failed on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Nov 04, 2022)
- test_inverse_transform[50-float32-False-10-expected_mixing_shape5]
COMME... | 24,830 | [
-0.022102242335677147,
-0.03226608783006668,
-0.01722562126815319,
0.008933121338486671,
0.05274554342031479,
0.014328764751553535,
0.01844426989555359,
0.01733211986720562,
0.00007575895870104432,
0.02193441614508629,
0.02495380863547325,
-0.021852364763617516,
0.020868970081210136,
0.043... |
https://github.com/scikit-learn/scikit-learn/issues/24830 | [
"Bug",
"Low Priority"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI failed on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Nov 04, 2022)
- test_inverse_transform[50-float32-False-10-expected_mixing_shape5]
COMME... | 24,830 | [
-0.0362008772790432,
-0.04568377137184143,
-0.031912900507450104,
-0.0371088869869709,
0.040135566145181656,
0.008886602707207203,
0.03204374015331268,
0.03866162151098251,
-0.005393327679485083,
0.036422841250896454,
0.011639992706477642,
0.04111596569418907,
0.007149075623601675,
0.03064... |
https://github.com/scikit-learn/scikit-learn/issues/24830 | [
"Bug",
"Low Priority"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI failed on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Nov 04, 2022)
- test_inverse_transform[50-float32-False-10-expected_mixing_shape5]
COMME... | 24,830 | [
-0.020116368308663368,
-0.03260321542620659,
-0.029691433534026146,
-0.0281358789652586,
0.027405623346567154,
-0.0043766433373093605,
0.01776442863047123,
0.01932585798203945,
0.012208625674247742,
0.02387268841266632,
0.04080706834793091,
0.016239335760474205,
0.02967250719666481,
0.0552... |
https://github.com/scikit-learn/scikit-learn/issues/24830 | [
"Bug",
"Low Priority"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI failed on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Nov 04, 2022)
- test_inverse_transform[50-float32-False-10-expected_mixing_shape5]
COMME... | 24,830 | [
-0.044833604246377945,
-0.050332095474004745,
-0.018779098987579346,
-0.01351372804492712,
0.012507959268987179,
-0.0015361696714535356,
0.044851258397102356,
0.042513225227594376,
0.03322269394993782,
0.03288559243083,
0.02558145858347416,
0.007986275479197502,
0.017538081854581833,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/24830 | [
"Bug",
"Low Priority"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI failed on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48358&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Nov 04, 2022)
- test_inverse_transform[50-float32-False-10-expected_mixing_shape5]
COMME... | 24,830 | [
-0.019908247515559196,
-0.013918106444180012,
-0.027675462886691093,
-0.04159272834658623,
0.032548222690820694,
0.006026779301464558,
0.02206488512456417,
0.02669658325612545,
0.0007349965744651854,
0.03940802440047264,
0.03645758330821991,
0.015821198001503944,
0.009360285475850105,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24829 | [
"Documentation",
"module:preprocessing"
] | `FunctionTransformer.transform()` API Confusion
### Describe the issue linked to the documentation
Documentation in question: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer.transform
I need the FunctionTransformer in orde... | 24,829 | [
-0.012870327569544315,
0.01971127651631832,
0.03201600909233093,
-0.037523169070482254,
0.03239389881491661,
-0.002802458358928561,
0.09193786233663559,
0.0026704787742346525,
-0.006350941490381956,
-0.02580581232905388,
0.027151599526405334,
-0.00905124843120575,
0.009565279819071293,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24829 | [
"Documentation",
"module:preprocessing"
] | `FunctionTransformer.transform()` API Confusion
### Describe the issue linked to the documentation
Documentation in question: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer.transform
I need the FunctionTransformer in orde... | 24,829 | [
-0.010851421393454075,
0.009824822656810284,
0.0141586409881711,
-0.03763001412153244,
0.013008416630327702,
-0.00683814100921154,
0.09266214817762375,
-0.010866339318454266,
0.0039621093310415745,
-0.026061411947011948,
0.02913258783519268,
-0.006178376730531454,
0.006456615868955851,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24829 | [
"Documentation",
"module:preprocessing"
] | `FunctionTransformer.transform()` API Confusion
### Describe the issue linked to the documentation
Documentation in question: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer.transform
I need the FunctionTransformer in orde... | 24,829 | [
-0.012257799506187439,
0.010518552735447884,
0.01427626982331276,
-0.038306817412376404,
0.012858832255005836,
-0.007848020642995834,
0.09097637981176376,
-0.010678619146347046,
0.006132856477051973,
-0.025338636711239815,
0.030131207779049873,
-0.005468788091093302,
0.005841011181473732,
... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24828 | [
"RFC",
"workflow"
] | RFC CLI Tool Proposal
## Introduction
Other OSS projects such as `scipy` and `numpy` have made use of development CLI tools such as `runtests.py` and `dev.py` to provide a collected and singular entry point into development tasks for contributors. I find these tools very helpful personally, and think they add a large... | 24,828 | [
-0.01765202172100544,
0.06564676016569138,
-0.021932685747742653,
-0.09443223476409912,
-0.010512740351259708,
-0.006776963360607624,
0.09326539933681488,
0.01404704712331295,
0.01786542870104313,
0.033669427037239075,
0.04108342155814171,
0.019042368978261948,
-0.03536587581038475,
0.0745... |
https://github.com/scikit-learn/scikit-learn/issues/24819 | [
"help wanted",
"module:linear_model",
"module:test-suite"
] | Reduce warnings in test_logistic.py
### Description
The tests of `LogisticRegression` in `test_logistic.py` produce currently **468 warnings** (run `pytest sklearn/linear_model/tests/test_logistic.py`).
This is quite a lot and should be reduced.
### Remark
To explore the cause for the warnings, run `pytest -Werr... | 24,819 | [
-0.03747417405247688,
0.03040042333304882,
0.020052069798111916,
0.03404286131262779,
0.12602774798870087,
0.02917497232556343,
0.04379303753376007,
0.08691157400608063,
0.0480358824133873,
0.0017075167270377278,
0.047510381788015366,
0.013964593410491943,
-0.07976135611534119,
0.052533723... |
https://github.com/scikit-learn/scikit-learn/issues/24816 | [
"Bug",
"module:feature_extraction"
] | `DictVectorizer` doesn't raise `NotFittedError` when using transform without prior fitting
### Describe the bug
When trying to call the `transform` method of an unfitted DictVectorizer instance an `AttributeError` is raised instead of a `NotFittedError` .
Other transformers, such as StandardScaler, make use of `ch... | 24,816 | [
-0.004214941523969173,
-0.021549612283706665,
0.0462975911796093,
-0.015221776440739632,
0.1115419790148735,
0.020154405385255814,
0.0349804125726223,
0.019358143210411072,
0.00945005938410759,
0.02356605976819992,
0.07141255587339401,
0.00828193873167038,
0.03346480056643486,
0.0102009801... |
https://github.com/scikit-learn/scikit-learn/issues/24816 | [
"Bug",
"module:feature_extraction"
] | `DictVectorizer` doesn't raise `NotFittedError` when using transform without prior fitting
### Describe the bug
When trying to call the `transform` method of an unfitted DictVectorizer instance an `AttributeError` is raised instead of a `NotFittedError` .
Other transformers, such as StandardScaler, make use of `ch... | 24,816 | [
-0.004214941523969173,
-0.021549612283706665,
0.0462975911796093,
-0.015221776440739632,
0.1115419790148735,
0.020154405385255814,
0.0349804125726223,
0.019358143210411072,
0.00945005938410759,
0.02356605976819992,
0.07141255587339401,
0.00828193873167038,
0.03346480056643486,
0.0102009801... |
https://github.com/scikit-learn/scikit-learn/issues/24801 | [
"module:svm"
] | LinearSVC fails when used with Bagging
### Describe the bug
Using the Bagging class with LinearSVC as baseestimator throws
```
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive me... | 24,801 | [
-0.009467147290706635,
0.0011636822018772364,
0.017045997083187103,
0.030116144567728043,
0.12028618901968002,
-0.013348358683288097,
-0.017063584178686142,
0.043272845447063446,
-0.014092973433434963,
-0.004065884742885828,
0.05130687728524208,
0.06572037935256958,
-0.037302613258361816,
... |
https://github.com/scikit-learn/scikit-learn/issues/24801 | [
"module:svm"
] | LinearSVC fails when used with Bagging
### Describe the bug
Using the Bagging class with LinearSVC as baseestimator throws
```
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive me... | 24,801 | [
-0.009467147290706635,
0.0011636822018772364,
0.017045997083187103,
0.030116144567728043,
0.12028618901968002,
-0.013348358683288097,
-0.017063584178686142,
0.043272845447063446,
-0.014092973433434963,
-0.004065884742885828,
0.05130687728524208,
0.06572037935256958,
-0.037302613258361816,
... |
https://github.com/scikit-learn/scikit-learn/issues/24801 | [
"module:svm"
] | LinearSVC fails when used with Bagging
### Describe the bug
Using the Bagging class with LinearSVC as baseestimator throws
```
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive me... | 24,801 | [
-0.009467147290706635,
0.0011636822018772364,
0.017045997083187103,
0.030116144567728043,
0.12028618901968002,
-0.013348358683288097,
-0.017063584178686142,
0.043272845447063446,
-0.014092973433434963,
-0.004065884742885828,
0.05130687728524208,
0.06572037935256958,
-0.037302613258361816,
... |
https://github.com/scikit-learn/scikit-learn/issues/24801 | [
"module:svm"
] | LinearSVC fails when used with Bagging
### Describe the bug
Using the Bagging class with LinearSVC as baseestimator throws
```
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive me... | 24,801 | [
-0.009467147290706635,
0.0011636822018772364,
0.017045997083187103,
0.030116144567728043,
0.12028618901968002,
-0.013348358683288097,
-0.017063584178686142,
0.043272845447063446,
-0.014092973433434963,
-0.004065884742885828,
0.05130687728524208,
0.06572037935256958,
-0.037302613258361816,
... |
https://github.com/scikit-learn/scikit-learn/issues/24801 | [
"module:svm"
] | LinearSVC fails when used with Bagging
### Describe the bug
Using the Bagging class with LinearSVC as baseestimator throws
```
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive me... | 24,801 | [
-0.009467147290706635,
0.0011636822018772364,
0.017045997083187103,
0.030116144567728043,
0.12028618901968002,
-0.013348358683288097,
-0.017063584178686142,
0.043272845447063446,
-0.014092973433434963,
-0.004065884742885828,
0.05130687728524208,
0.06572037935256958,
-0.037302613258361816,
... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24797 | [
"Documentation",
"good first issue",
"Meta-issue"
] | `MatplotlibDeprecationWarnings` in examples
### Describe the issue linked to the documentation
Some `MatplotlibDeprecationWarning`s are still present in the dev documentation and need to be fixed.
Here a list:
- [x] [classification/plot_lda_qda.html](https://scikit-learn.org/dev/auto_examples/classification/plot_... | 24,797 | [
0.002022663364186883,
-0.0026084233541041613,
-0.039473045617341995,
-0.007122740149497986,
0.04072839021682739,
0.0398670993745327,
0.04746348783373833,
0.05576787143945694,
0.009610477834939957,
0.015969133004546165,
0.017165521159768105,
0.04522854834794998,
-0.012041842564940453,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/24795 | [
"Documentation"
] | URLs of citeseer or citeseerx should be updated
### Describe the issue linked to the documentation
Files pointed by the `citeseer` or `citeseerx` URLs are no longer reachable. I think the site has updated its routes to the files.
The good news is that there are only 25 places in 17 files to be modified
![image](... | 24,795 | [
0.027401842176914215,
0.013769456185400486,
-0.011812263168394566,
0.006874946411699057,
-0.024857740849256516,
0.011952647008001804,
0.002532553393393755,
0.028823740780353546,
-0.051439106464385986,
-0.025725657120347023,
-0.014924528077244759,
0.043751247227191925,
0.006616837345063686,
... |
https://github.com/scikit-learn/scikit-learn/issues/24795 | [
"Documentation"
] | URLs of citeseer or citeseerx should be updated
### Describe the issue linked to the documentation
Files pointed by the `citeseer` or `citeseerx` URLs are no longer reachable. I think the site has updated its routes to the files.
The good news is that there are only 25 places in 17 files to be modified
![image](... | 24,795 | [
0.027401842176914215,
0.013769456185400486,
-0.011812263168394566,
0.006874946411699057,
-0.024857740849256516,
0.011952647008001804,
0.002532553393393755,
0.028823740780353546,
-0.051439106464385986,
-0.025725657120347023,
-0.014924528077244759,
0.043751247227191925,
0.006616837345063686,
... |
https://github.com/scikit-learn/scikit-learn/issues/24795 | [
"Documentation"
] | URLs of citeseer or citeseerx should be updated
### Describe the issue linked to the documentation
Files pointed by the `citeseer` or `citeseerx` URLs are no longer reachable. I think the site has updated its routes to the files.
The good news is that there are only 25 places in 17 files to be modified
![image](... | 24,795 | [
0.027401842176914215,
0.013769456185400486,
-0.011812263168394566,
0.006874946411699057,
-0.024857740849256516,
0.011952647008001804,
0.002532553393393755,
0.028823740780353546,
-0.051439106464385986,
-0.025725657120347023,
-0.014924528077244759,
0.043751247227191925,
0.006616837345063686,
... |
https://github.com/scikit-learn/scikit-learn/issues/24792 | [
"Bug",
"Needs Triage"
] | ValueError: The loss log_loss is not supported.
### Describe the bug
I have this error, and I can't find a solution when using SGDClassifier with the loss="log_loss" parameter to take advantage of online learning as LR
### Steps/Code to Reproduce
```
#training
df_training = df.head(int(len(df) * config.traini... | 24,792 | [
0.006509413477033377,
0.0107743414118886,
0.03171458840370178,
-0.014657370746135712,
0.09682433307170868,
0.02964756265282631,
0.030785491690039635,
0.04543809965252876,
-0.0031955598387867212,
-0.03591851145029068,
0.03856422379612923,
0.04120557755231857,
-0.03420330584049225,
0.0635047... |
https://github.com/scikit-learn/scikit-learn/issues/24791 | [
"Documentation"
] | Example change produces warnings in documentation
### Describe the issue linked to the documentation
The tutorial about [Unsupervised learning](https://scikit-learn.org/dev/tutorial/statistical_inference/unsupervised_learning.html) in the development version is missing images from the K-means clustering example. Th... | 24,791 | [
0.013879687525331974,
-0.013955840840935707,
-0.037716832011938095,
-0.012311450205743313,
0.02995608188211918,
0.03955494612455368,
0.06967439502477646,
0.014385690912604332,
0.02994326874613762,
-0.0005734084406867623,
0.021946018561720848,
0.08323903381824493,
0.020306697115302086,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24791 | [
"Documentation"
] | Example change produces warnings in documentation
### Describe the issue linked to the documentation
The tutorial about [Unsupervised learning](https://scikit-learn.org/dev/tutorial/statistical_inference/unsupervised_learning.html) in the development version is missing images from the K-means clustering example. Th... | 24,791 | [
0.015090725384652615,
-0.010149478912353516,
-0.038661371916532516,
-0.017069851979613304,
0.022622782737016678,
0.04028007388114929,
0.06106974184513092,
0.013782257214188576,
0.02215404435992241,
-0.015986546874046326,
0.03431297093629837,
0.08453034609556198,
0.015216901898384094,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24791 | [
"Documentation"
] | Example change produces warnings in documentation
### Describe the issue linked to the documentation
The tutorial about [Unsupervised learning](https://scikit-learn.org/dev/tutorial/statistical_inference/unsupervised_learning.html) in the development version is missing images from the K-means clustering example. Th... | 24,791 | [
0.011021255515515804,
-0.020843075588345528,
-0.041708797216415405,
-0.011977425776422024,
0.02219756506383419,
0.04267524182796478,
0.07172167301177979,
0.011330410838127136,
0.016217295080423355,
-0.007867036387324333,
0.038478098809719086,
0.07935185730457306,
0.019275320693850517,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24786 | [
"Bug",
"module:gaussian_process"
] | Gaussian process `log_marginal_likelihood()` `theta` parameter: pass as `log(theta)`?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24765
I opened this as a discussion, but it is probably more suited for an issue, as it may lead to a documentation fix.
System Info
```
System:
... | 24,786 | [
0.04408891126513481,
0.001043665106408298,
0.05188269913196564,
-0.017321327701210976,
0.020528875291347504,
0.0072257500141859055,
0.052691735327243805,
-0.038418084383010864,
-0.037330713123083115,
-0.0039015449583530426,
0.0017040185630321503,
0.003571116831153631,
0.032566044479608536,
... |
https://github.com/scikit-learn/scikit-learn/issues/24786 | [
"Bug",
"module:gaussian_process"
] | Gaussian process `log_marginal_likelihood()` `theta` parameter: pass as `log(theta)`?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24765
I opened this as a discussion, but it is probably more suited for an issue, as it may lead to a documentation fix.
System Info
```
System:
... | 24,786 | [
0.04408891126513481,
0.001043665106408298,
0.05188269913196564,
-0.017321327701210976,
0.020528875291347504,
0.0072257500141859055,
0.052691735327243805,
-0.038418084383010864,
-0.037330713123083115,
-0.0039015449583530426,
0.0017040185630321503,
0.003571116831153631,
0.032566044479608536,
... |
https://github.com/scikit-learn/scikit-learn/issues/24786 | [
"Bug",
"module:gaussian_process"
] | Gaussian process `log_marginal_likelihood()` `theta` parameter: pass as `log(theta)`?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24765
I opened this as a discussion, but it is probably more suited for an issue, as it may lead to a documentation fix.
System Info
```
System:
... | 24,786 | [
0.04408891126513481,
0.001043665106408298,
0.05188269913196564,
-0.017321327701210976,
0.020528875291347504,
0.0072257500141859055,
0.052691735327243805,
-0.038418084383010864,
-0.037330713123083115,
-0.0039015449583530426,
0.0017040185630321503,
0.003571116831153631,
0.032566044479608536,
... |
https://github.com/scikit-learn/scikit-learn/issues/24782 | [
"Needs Triage"
] | Machine learning
COMMENT:
it is | 24,782 | [
0.002795866457745433,
0.054833296686410904,
-0.039536625146865845,
0.02925189584493637,
-0.02301202528178692,
0.021676918491721153,
-0.0005193669348955154,
0.0203144121915102,
-0.041167858988046646,
-0.03943459689617157,
0.04834995046257973,
0.0664488896727562,
-0.010204493068158627,
0.043... |
https://github.com/scikit-learn/scikit-learn/issues/24780 | [
"New Feature"
] | Graph metaestimators with a builder API
### Describe the workflow you want to enable
I recently added a new project to scikit-learn-contrib called [skdag](https://github.com/scikit-learn-contrib/skdag) , which allows the construction of workflows as DAGs (directed acyclic graphs), wrapped up in the interface of a met... | 24,780 | [
-0.054741404950618744,
0.06075134500861168,
-0.004420265089720488,
-0.044456977397203445,
-0.015600993297994137,
-0.0228081326931715,
0.0657489150762558,
-0.04739823564887047,
0.020542606711387634,
0.022959472611546516,
0.033062782138586044,
0.09031793475151062,
-0.041876766830682755,
0.11... |
https://github.com/scikit-learn/scikit-learn/issues/24780 | [
"New Feature"
] | Graph metaestimators with a builder API
### Describe the workflow you want to enable
I recently added a new project to scikit-learn-contrib called [skdag](https://github.com/scikit-learn-contrib/skdag) , which allows the construction of workflows as DAGs (directed acyclic graphs), wrapped up in the interface of a met... | 24,780 | [
-0.0548887737095356,
0.07661853730678558,
-0.004139415919780731,
-0.04559393972158432,
-0.019427580758929253,
-0.016276471316814423,
0.07538189738988876,
-0.03256179764866829,
0.012001816183328629,
0.016268784180283546,
0.028676774352788925,
0.08102540671825409,
-0.036849554628133774,
0.12... |
https://github.com/scikit-learn/scikit-learn/issues/24780 | [
"New Feature"
] | Graph metaestimators with a builder API
### Describe the workflow you want to enable
I recently added a new project to scikit-learn-contrib called [skdag](https://github.com/scikit-learn-contrib/skdag) , which allows the construction of workflows as DAGs (directed acyclic graphs), wrapped up in the interface of a met... | 24,780 | [
-0.05629630386829376,
0.07962138950824738,
-0.0020499606616795063,
-0.04449450597167015,
-0.013301578350365162,
-0.020385535433888435,
0.07434845715761185,
-0.042617253959178925,
0.007037371397018433,
0.019901182502508163,
0.033333566039800644,
0.08268968760967255,
-0.045004703104496,
0.12... |
https://github.com/scikit-learn/scikit-learn/issues/24780 | [
"New Feature"
] | Graph metaestimators with a builder API
### Describe the workflow you want to enable
I recently added a new project to scikit-learn-contrib called [skdag](https://github.com/scikit-learn-contrib/skdag) , which allows the construction of workflows as DAGs (directed acyclic graphs), wrapped up in the interface of a met... | 24,780 | [
-0.0524735264480114,
0.071385458111763,
-0.004464603960514069,
-0.042454157024621964,
-0.016048021614551544,
-0.028672020882368088,
0.06807567924261093,
-0.04194045811891556,
0.01375257596373558,
0.01899585872888565,
0.030605340376496315,
0.08948148041963577,
-0.04492245987057686,
0.125023... |
https://github.com/scikit-learn/scikit-learn/issues/24777 | [
"Documentation"
] | error message improvement
### Describe the workflow you want to enable
Hi,
I think that this error message is misleading
https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/model_selection/_search_successive_halving.py#L186
I am basically following the example he... | 24,777 | [
0.03722996637225151,
-0.01094088889658451,
0.002315891906619072,
-0.015162626281380653,
0.028947560116648674,
-0.00785656925290823,
0.009870116598904133,
0.03084482066333294,
-0.03512484207749367,
-0.017062971368432045,
0.06252916157245636,
0.05263517051935196,
-0.06087956577539444,
-0.012... |
https://github.com/scikit-learn/scikit-learn/issues/24777 | [
"Documentation"
] | error message improvement
### Describe the workflow you want to enable
Hi,
I think that this error message is misleading
https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/model_selection/_search_successive_halving.py#L186
I am basically following the example he... | 24,777 | [
0.042670875787734985,
0.0037195212207734585,
0.0006664202082902193,
-0.018012018874287605,
0.042281005531549454,
-0.003660650458186865,
0.03431747481226921,
0.024558736011385918,
-0.021265463903546333,
-0.01329273171722889,
0.06341227144002914,
0.0630972683429718,
-0.04807059094309807,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24777 | [
"Documentation"
] | error message improvement
### Describe the workflow you want to enable
Hi,
I think that this error message is misleading
https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/model_selection/_search_successive_halving.py#L186
I am basically following the example he... | 24,777 | [
0.04703710228204727,
0.010881065391004086,
-0.005896859336644411,
-0.01418844610452652,
0.02719506062567234,
-0.007313782814890146,
-0.011412116698920727,
0.023080337792634964,
-0.04910821095108986,
-0.017406273633241653,
0.07423233985900879,
0.048000164330005646,
-0.06364832073450089,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24777 | [
"Documentation"
] | error message improvement
### Describe the workflow you want to enable
Hi,
I think that this error message is misleading
https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/model_selection/_search_successive_halving.py#L186
I am basically following the example he... | 24,777 | [
0.040821027010679245,
-0.000025979024940170348,
-0.009102299809455872,
-0.01719246804714203,
0.028282754123210907,
-0.00737543310970068,
0.01029236987233162,
0.027937648817896843,
-0.042967334389686584,
-0.015906061977148056,
0.06083676218986511,
0.05928729102015495,
-0.0635358989238739,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24777 | [
"Documentation"
] | error message improvement
### Describe the workflow you want to enable
Hi,
I think that this error message is misleading
https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/model_selection/_search_successive_halving.py#L186
I am basically following the example he... | 24,777 | [
0.041853196918964386,
-0.00031064485665410757,
-0.008070811629295349,
-0.01713012158870697,
0.030989238992333412,
-0.006081871222704649,
0.012020585127174854,
0.0319245383143425,
-0.04341444373130798,
-0.017071187496185303,
0.05997684970498085,
0.058792512863874435,
-0.06191309913992882,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24773 | [
"Documentation"
] | DOC Adjusted_rand_score not obvious in the graph
### Describe the issue linked to the documentation
In the example "Adjustment for chance in clustering performance evaluation", there are four different scores in the graph legend, but the graph shows only three lines. The blue line `adjusted_rand_score` seems to be ve... | 24,773 | [
-0.054912347346544266,
-0.07831627130508423,
-0.04845016449689865,
0.05330535024404526,
-0.005390207748860121,
-0.02467305026948452,
-0.0022871883120387793,
0.010862897150218487,
0.03718578815460205,
0.026083864271640778,
0.024176158010959625,
0.04143408313393593,
0.042232196778059006,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24773 | [
"Documentation"
] | DOC Adjusted_rand_score not obvious in the graph
### Describe the issue linked to the documentation
In the example "Adjustment for chance in clustering performance evaluation", there are four different scores in the graph legend, but the graph shows only three lines. The blue line `adjusted_rand_score` seems to be ve... | 24,773 | [
-0.05982111021876335,
-0.08042404800653458,
-0.039559558033943176,
0.05034960061311722,
0.002113679889589548,
-0.030756955966353416,
-0.002114326460286975,
0.01815279945731163,
0.040666136890649796,
0.026921849697828293,
0.020264700055122375,
0.03954436257481575,
0.030913149937987328,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24772 | [
"Bug",
"Needs Triage"
] | IterativeImputer strange implementation of fit_transform
### Describe the bug
A common scenario might be to subclass `IterativeImputer` to wrap some functionality around fit/transform/fit_transform methods.
it might look like this:
```
class Derived(IterativeImputer):
def fit(self, X, y=None):
... | 24,772 | [
-0.012122385203838348,
0.028566211462020874,
0.02202678471803665,
0.05154608562588692,
0.02570587582886219,
-0.016467776149511337,
0.026834804564714432,
0.01435851864516735,
-0.0035941433161497116,
0.01102583296597004,
0.014605876989662647,
0.03890066593885422,
0.026413723826408386,
0.0029... |
https://github.com/scikit-learn/scikit-learn/issues/24764 | [
"Bug",
"module:neural_network"
] | MLPEstimator do not report the proper `n_iter_` after successive call of `fit`
### Describe the bug
It seems that `MLPRegressor` and `MLPClassifier` does not take into account `max_iter` with `warm_start`.
### Steps/Code to Reproduce
```python
model = MLPRegressor(warm_start=True, early_stopping=True, max_... | 24,764 | [
-0.03292516991496086,
-0.030939342454075813,
0.033129747956991196,
0.02291465550661087,
0.0362563356757164,
-0.0207761712372303,
0.04598488658666611,
0.03647596761584282,
0.034229815006256104,
-0.016128607094287872,
0.023594584316015244,
0.01950531080365181,
0.010580458678305149,
0.0156073... |
https://github.com/scikit-learn/scikit-learn/issues/24764 | [
"Bug",
"module:neural_network"
] | MLPEstimator do not report the proper `n_iter_` after successive call of `fit`
### Describe the bug
It seems that `MLPRegressor` and `MLPClassifier` does not take into account `max_iter` with `warm_start`.
### Steps/Code to Reproduce
```python
model = MLPRegressor(warm_start=True, early_stopping=True, max_... | 24,764 | [
-0.03292516991496086,
-0.030939342454075813,
0.033129747956991196,
0.02291465550661087,
0.0362563356757164,
-0.0207761712372303,
0.04598488658666611,
0.03647596761584282,
0.034229815006256104,
-0.016128607094287872,
0.023594584316015244,
0.01950531080365181,
0.010580458678305149,
0.0156073... |
https://github.com/scikit-learn/scikit-learn/issues/24764 | [
"Bug",
"module:neural_network"
] | MLPEstimator do not report the proper `n_iter_` after successive call of `fit`
### Describe the bug
It seems that `MLPRegressor` and `MLPClassifier` does not take into account `max_iter` with `warm_start`.
### Steps/Code to Reproduce
```python
model = MLPRegressor(warm_start=True, early_stopping=True, max_... | 24,764 | [
-0.03292516991496086,
-0.030939342454075813,
0.033129747956991196,
0.02291465550661087,
0.0362563356757164,
-0.0207761712372303,
0.04598488658666611,
0.03647596761584282,
0.034229815006256104,
-0.016128607094287872,
0.023594584316015244,
0.01950531080365181,
0.010580458678305149,
0.0156073... |
https://github.com/scikit-learn/scikit-learn/issues/24764 | [
"Bug",
"module:neural_network"
] | MLPEstimator do not report the proper `n_iter_` after successive call of `fit`
### Describe the bug
It seems that `MLPRegressor` and `MLPClassifier` does not take into account `max_iter` with `warm_start`.
### Steps/Code to Reproduce
```python
model = MLPRegressor(warm_start=True, early_stopping=True, max_... | 24,764 | [
-0.03292516991496086,
-0.030939342454075813,
0.033129747956991196,
0.02291465550661087,
0.0362563356757164,
-0.0207761712372303,
0.04598488658666611,
0.03647596761584282,
0.034229815006256104,
-0.016128607094287872,
0.023594584316015244,
0.01950531080365181,
0.010580458678305149,
0.0156073... |
https://github.com/scikit-learn/scikit-learn/issues/24764 | [
"Bug",
"module:neural_network"
] | MLPEstimator do not report the proper `n_iter_` after successive call of `fit`
### Describe the bug
It seems that `MLPRegressor` and `MLPClassifier` does not take into account `max_iter` with `warm_start`.
### Steps/Code to Reproduce
```python
model = MLPRegressor(warm_start=True, early_stopping=True, max_... | 24,764 | [
-0.03292516991496086,
-0.030939342454075813,
0.033129747956991196,
0.02291465550661087,
0.0362563356757164,
-0.0207761712372303,
0.04598488658666611,
0.03647596761584282,
0.034229815006256104,
-0.016128607094287872,
0.023594584316015244,
0.01950531080365181,
0.010580458678305149,
0.0156073... |
https://github.com/scikit-learn/scikit-learn/issues/24760 | [
"Bug",
"Needs Triage"
] | Install fails on python 3.11
### Describe the bug
While installing scikit-learn, installation fails with setuptools error. If setuptools version is downgraded to setuptools==58.2.0 from setuptools==65.5.0(current stable), installation works fine.
### Steps/Code to Reproduce
```
pip install -U scikit-learn
``... | 24,760 | [
0.03221924230456352,
-0.006855700630694628,
0.016987407580018044,
-0.06495916098356247,
0.035541459918022156,
0.020047662779688835,
0.026612717658281326,
0.057756178081035614,
-0.016028719022870064,
-0.007109679747372866,
0.022304585203528404,
0.0921517014503479,
0.009252076968550682,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24760 | [
"Bug",
"Needs Triage"
] | Install fails on python 3.11
### Describe the bug
While installing scikit-learn, installation fails with setuptools error. If setuptools version is downgraded to setuptools==58.2.0 from setuptools==65.5.0(current stable), installation works fine.
### Steps/Code to Reproduce
```
pip install -U scikit-learn
``... | 24,760 | [
0.03221924230456352,
-0.006855700630694628,
0.016987407580018044,
-0.06495916098356247,
0.035541459918022156,
0.020047662779688835,
0.026612717658281326,
0.057756178081035614,
-0.016028719022870064,
-0.007109679747372866,
0.022304585203528404,
0.0921517014503479,
0.009252076968550682,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24757 | [
"Bug",
"module:decomposition"
] | PCA crashes when fit to large array
### Describe the bug
When trying to fit a `PCA` model to a large dataset with `shape=(30000, 28000)`, the model fits for a bit more than 30 minutes using nearly all cores and then crashes out of python. I've confirmed that a dataset with `shape=(20000, 28000)` succeeds without is... | 24,757 | [
-0.009605132043361664,
0.04490404203534126,
0.005981420632451773,
0.024134591221809387,
0.09605447202920914,
-0.009182583540678024,
-0.049262985587120056,
0.004025926813483238,
0.005944334901869297,
0.00864673126488924,
0.024474795907735825,
0.035886842757463455,
-0.031124858185648918,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24757 | [
"Bug",
"module:decomposition"
] | PCA crashes when fit to large array
### Describe the bug
When trying to fit a `PCA` model to a large dataset with `shape=(30000, 28000)`, the model fits for a bit more than 30 minutes using nearly all cores and then crashes out of python. I've confirmed that a dataset with `shape=(20000, 28000)` succeeds without is... | 24,757 | [
-0.009605132043361664,
0.04490404203534126,
0.005981420632451773,
0.024134591221809387,
0.09605447202920914,
-0.009182583540678024,
-0.049262985587120056,
0.004025926813483238,
0.005944334901869297,
0.00864673126488924,
0.024474795907735825,
0.035886842757463455,
-0.031124858185648918,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24757 | [
"Bug",
"module:decomposition"
] | PCA crashes when fit to large array
### Describe the bug
When trying to fit a `PCA` model to a large dataset with `shape=(30000, 28000)`, the model fits for a bit more than 30 minutes using nearly all cores and then crashes out of python. I've confirmed that a dataset with `shape=(20000, 28000)` succeeds without is... | 24,757 | [
-0.009605132043361664,
0.04490404203534126,
0.005981420632451773,
0.024134591221809387,
0.09605447202920914,
-0.009182583540678024,
-0.049262985587120056,
0.004025926813483238,
0.005944334901869297,
0.00864673126488924,
0.024474795907735825,
0.035886842757463455,
-0.031124858185648918,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24757 | [
"Bug",
"module:decomposition"
] | PCA crashes when fit to large array
### Describe the bug
When trying to fit a `PCA` model to a large dataset with `shape=(30000, 28000)`, the model fits for a bit more than 30 minutes using nearly all cores and then crashes out of python. I've confirmed that a dataset with `shape=(20000, 28000)` succeeds without is... | 24,757 | [
-0.009605132043361664,
0.04490404203534126,
0.005981420632451773,
0.024134591221809387,
0.09605447202920914,
-0.009182583540678024,
-0.049262985587120056,
0.004025926813483238,
0.005944334901869297,
0.00864673126488924,
0.024474795907735825,
0.035886842757463455,
-0.031124858185648918,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24757 | [
"Bug",
"module:decomposition"
] | PCA crashes when fit to large array
### Describe the bug
When trying to fit a `PCA` model to a large dataset with `shape=(30000, 28000)`, the model fits for a bit more than 30 minutes using nearly all cores and then crashes out of python. I've confirmed that a dataset with `shape=(20000, 28000)` succeeds without is... | 24,757 | [
-0.009605132043361664,
0.04490404203534126,
0.005981420632451773,
0.024134591221809387,
0.09605447202920914,
-0.009182583540678024,
-0.049262985587120056,
0.004025926813483238,
0.005944334901869297,
0.00864673126488924,
0.024474795907735825,
0.035886842757463455,
-0.031124858185648918,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24754 | [
"New Feature",
"module:pipeline"
] | add `feature_names_in_` attribute to `FeatureUnion`
### Describe the workflow you want to enable
The docs for `feature_names_in_` in the `sklearn.pipeline.Pipeline` say:
> feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
under... | 24,754 | [
-0.025627311319112778,
0.025134887546300888,
0.007878325879573822,
-0.00404167827218771,
0.044356875121593475,
0.006191458087414503,
0.07261747121810913,
-0.050363559275865555,
0.02171691693365574,
0.00014731376722920686,
0.006751534063369036,
0.011487209238111973,
0.009002845734357834,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24754 | [
"New Feature",
"module:pipeline"
] | add `feature_names_in_` attribute to `FeatureUnion`
### Describe the workflow you want to enable
The docs for `feature_names_in_` in the `sklearn.pipeline.Pipeline` say:
> feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
under... | 24,754 | [
-0.025627311319112778,
0.025134887546300888,
0.007878325879573822,
-0.00404167827218771,
0.044356875121593475,
0.006191458087414503,
0.07261747121810913,
-0.050363559275865555,
0.02171691693365574,
0.00014731376722920686,
0.006751534063369036,
0.011487209238111973,
0.009002845734357834,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24754 | [
"New Feature",
"module:pipeline"
] | add `feature_names_in_` attribute to `FeatureUnion`
### Describe the workflow you want to enable
The docs for `feature_names_in_` in the `sklearn.pipeline.Pipeline` say:
> feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
under... | 24,754 | [
-0.025627311319112778,
0.025134887546300888,
0.007878325879573822,
-0.00404167827218771,
0.044356875121593475,
0.006191458087414503,
0.07261747121810913,
-0.050363559275865555,
0.02171691693365574,
0.00014731376722920686,
0.006751534063369036,
0.011487209238111973,
0.009002845734357834,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24754 | [
"New Feature",
"module:pipeline"
] | add `feature_names_in_` attribute to `FeatureUnion`
### Describe the workflow you want to enable
The docs for `feature_names_in_` in the `sklearn.pipeline.Pipeline` say:
> feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
under... | 24,754 | [
-0.025627311319112778,
0.025134887546300888,
0.007878325879573822,
-0.00404167827218771,
0.044356875121593475,
0.006191458087414503,
0.07261747121810913,
-0.050363559275865555,
0.02171691693365574,
0.00014731376722920686,
0.006751534063369036,
0.011487209238111973,
0.009002845734357834,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
https://github.com/scikit-learn/scikit-learn/issues/24752 | [
"Enhancement",
"Performance",
"module:linear_model"
] | Fix scaling of LogisticRegression objective for LBFGS
## Description
The objective function of `LogisticRegression` is `C * sum(log_loss) + penalty`. For LBFGS, the reformulation (having the same argmin) is much more favorable: `1/N * sum(log_loss) + 1/(N*C)*penalty`.
Note that the division by 1/C is already done ... | 24,752 | [
0.009110091254115105,
0.05537891015410423,
0.037248507142066956,
0.01656213216483593,
0.05806247889995575,
-0.003428543685004115,
0.02662598341703415,
0.05804997682571411,
-0.016817141324281693,
0.01910202018916607,
0.03268979489803314,
0.006768106948584318,
-0.04260968416929245,
0.0217338... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.