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/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... |
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... |
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/27505 | [
"Documentation"
] | Impact of class weights in LogisticRegression
### Describe the issue linked to the documentation
The impact of class weights and the exact objective function with (all kinds of) weights for `LogisticRegression` should be mentioned in the user guide. Importantly, the scale of weights interact with the (anti-) penalty ... | 27,505 | [
0.004754035267978907,
0.04876076057553291,
0.008167310617864132,
0.01853366568684578,
0.058867551386356354,
0.017824159935116768,
0.016504930332303047,
-0.008088135160505772,
-0.028872961178421974,
-0.020836153998970985,
0.09384902566671371,
0.005082052666693926,
-0.01312771812081337,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 | [
-0.020093344151973724,
0.0026574349030852318,
0.013502474874258041,
0.003984882961958647,
0.012427972629666328,
-0.05455762892961502,
-0.04560872167348862,
-0.019542083144187927,
-0.03284651041030884,
0.0261902566999197,
0.03255468234419823,
0.06421326845884323,
-0.0424906387925148,
0.0023... |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 | [
-0.022007450461387634,
0.005508993752300739,
0.011313819326460361,
0.002458475064486265,
0.009852399118244648,
-0.05669178068637848,
-0.04361850768327713,
-0.020542362704873085,
-0.030511489138007164,
0.028041349723935127,
0.0316505990922451,
0.0700678676366806,
-0.044981151819229126,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 | [
-0.014214104041457176,
0.005291755776852369,
0.004991729743778706,
0.004913453012704849,
0.01232832670211792,
-0.04877694696187973,
-0.038185738027095795,
-0.017698846757411957,
-0.027532391250133514,
0.026993978768587112,
0.034339889883995056,
0.06691556423902512,
-0.03376861289143562,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 | [
-0.0203118696808815,
0.0018579348688945174,
0.012433848343789577,
0.002859106520190835,
0.012755203992128372,
-0.05712826922535896,
-0.04311542958021164,
-0.01713140308856964,
-0.028387922793626785,
0.025782158598303795,
0.03450649604201317,
0.06815133988857269,
-0.0424678772687912,
0.0021... |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 | [
-0.020160555839538574,
0.002926632296293974,
0.01183310430496931,
0.0015596901066601276,
0.009581077843904495,
-0.05757414177060127,
-0.042263999581336975,
-0.016461512073874474,
-0.028381938114762306,
0.02604801580309868,
0.034190427511930466,
0.06800314038991928,
-0.04311230033636093,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 | [
0.025555090978741646,
0.026986168697476387,
0.01759205386042595,
-0.01375932153314352,
0.06936440616846085,
0.02794947475194931,
0.02425490692257881,
-0.002472649561241269,
0.024181297048926353,
-0.04696816951036453,
0.013656660914421082,
0.059630852192640305,
0.007017955649644136,
0.04454... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 | [
0.026263443753123283,
0.011848894879221916,
0.023666078224778175,
-0.0304354690015316,
0.10859882086515427,
0.041310105472803116,
0.035619642585515976,
0.01870831288397312,
-0.04415448009967804,
-0.003115550149232149,
0.07122045755386353,
-0.020970173180103302,
0.04296203330159187,
-0.0174... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 | [
-0.009991235099732876,
0.013857933692634106,
0.02842380665242672,
-0.02527626045048237,
0.07615552097558975,
0.024992959573864937,
0.09884679317474365,
0.014566147699952126,
0.02395036444067955,
0.012816745787858963,
0.07133080065250397,
0.005761626176536083,
0.053821440786123276,
0.034313... |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 | [
0.007148613687604666,
0.05226504057645798,
-0.018086664378643036,
-0.032725464552640915,
-0.004132503177970648,
0.009802998043596745,
0.022226251661777496,
-0.006139859557151794,
-0.014641677029430866,
-0.026645243167877197,
0.06518351286649704,
0.04585425555706024,
0.0023148530162870884,
... |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 | [
0.009890500456094742,
0.0532325804233551,
-0.021234333515167236,
-0.029927397146821022,
-0.008546511642634869,
0.01328140776604414,
0.026254305616021156,
-0.006613994482904673,
-0.017787523567676544,
-0.0287519209086895,
0.06430967152118683,
0.03729730471968651,
0.0034882372710853815,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 | [
0.02157347835600376,
0.05413487181067467,
-0.024719001725316048,
-0.013018140569329262,
-0.01519367191940546,
0.018309293314814568,
0.04129499942064285,
-0.003524016821756959,
-0.008913535624742508,
-0.03349780663847923,
0.06415997445583344,
0.03846343234181404,
-0.0003289888263680041,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 | [
0.006536427419632673,
0.044769272208213806,
-0.015869159251451492,
-0.035717885941267014,
-0.00023483068798668683,
0.00587479816749692,
0.019582834094762802,
-0.007553524803370237,
-0.013467158190906048,
-0.025777576491236687,
0.06398140639066696,
0.050898272544145584,
0.004064034204930067,
... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 | [
-0.01615702174603939,
0.04559508338570595,
0.03486890345811844,
0.004141129087656736,
0.06648841500282288,
0.013914555311203003,
0.03907133266329765,
0.03717228025197983,
0.017764756456017494,
0.0044282833114266396,
0.02487395517528057,
0.001604873570613563,
-0.06898348033428192,
0.0597116... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
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-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 | [
-0.0012309059966355562,
0.05208149924874306,
0.0037667592987418175,
0.004913021344691515,
0.023934684693813324,
-0.0038743168115615845,
-0.021306972950696945,
-0.0066163563169538975,
-0.00578098651021719,
0.015714170411229134,
0.029337378218770027,
0.0046284194104373455,
0.003198764985427260... |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 | [
-0.013652607798576355,
0.027500856667757034,
0.05401567742228508,
-0.0202629491686821,
0.10014145076274872,
0.013667682185769081,
0.04681610316038132,
0.025638608261942863,
-0.02793053537607193,
-0.0007854845025576651,
0.017467668280005455,
0.009061011485755444,
0.03262493759393692,
0.0079... |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 | [
-0.013652607798576355,
0.027500856667757034,
0.05401567742228508,
-0.0202629491686821,
0.10014145076274872,
0.013667682185769081,
0.04681610316038132,
0.025638608261942863,
-0.02793053537607193,
-0.0007854845025576651,
0.017467668280005455,
0.009061011485755444,
0.03262493759393692,
0.0079... |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 | [
-0.013652607798576355,
0.027500856667757034,
0.05401567742228508,
-0.0202629491686821,
0.10014145076274872,
0.013667682185769081,
0.04681610316038132,
0.025638608261942863,
-0.02793053537607193,
-0.0007854845025576651,
0.017467668280005455,
0.009061011485755444,
0.03262493759393692,
0.0079... |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 | [
-0.013652607798576355,
0.027500856667757034,
0.05401567742228508,
-0.0202629491686821,
0.10014145076274872,
0.013667682185769081,
0.04681610316038132,
0.025638608261942863,
-0.02793053537607193,
-0.0007854845025576651,
0.017467668280005455,
0.009061011485755444,
0.03262493759393692,
0.0079... |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 | [
0.0008887461153790355,
-0.07789367437362671,
0.017929134890437126,
0.023992760106921196,
0.046843696385622025,
-0.01160474494099617,
0.04826151207089424,
-0.01773238554596901,
0.07959482818841934,
-0.00821318756788969,
0.009872742928564548,
0.02514716610312462,
0.026265893131494522,
0.0196... |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 | [
0.0008887461153790355,
-0.07789367437362671,
0.017929134890437126,
0.023992760106921196,
0.046843696385622025,
-0.01160474494099617,
0.04826151207089424,
-0.01773238554596901,
0.07959482818841934,
-0.00821318756788969,
0.009872742928564548,
0.02514716610312462,
0.026265893131494522,
0.0196... |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 | [
0.0008887461153790355,
-0.07789367437362671,
0.017929134890437126,
0.023992760106921196,
0.046843696385622025,
-0.01160474494099617,
0.04826151207089424,
-0.01773238554596901,
0.07959482818841934,
-0.00821318756788969,
0.009872742928564548,
0.02514716610312462,
0.026265893131494522,
0.0196... |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 | [
0.0008887461153790355,
-0.07789367437362671,
0.017929134890437126,
0.023992760106921196,
0.046843696385622025,
-0.01160474494099617,
0.04826151207089424,
-0.01773238554596901,
0.07959482818841934,
-0.00821318756788969,
0.009872742928564548,
0.02514716610312462,
0.026265893131494522,
0.0196... |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 | [
0.0008887461153790355,
-0.07789367437362671,
0.017929134890437126,
0.023992760106921196,
0.046843696385622025,
-0.01160474494099617,
0.04826151207089424,
-0.01773238554596901,
0.07959482818841934,
-0.00821318756788969,
0.009872742928564548,
0.02514716610312462,
0.026265893131494522,
0.0196... |
https://github.com/scikit-learn/scikit-learn/issues/27473 | [
"Bug",
"Needs Triage"
] | check_estimator is broken
### Describe the bug
Since the version 1.3.0, the check_estimator function is broken for all our custom estimators, but for native estimators as well.
An exception is raised for the test `check_estimators_pickle`: `ValueError: When creating aligned memmap-backed arrays, input must be a ... | 27,473 | [
-0.007902789860963821,
0.020117439329624176,
0.008031322620809078,
-0.008817525580525398,
0.08176592737436295,
-0.01364454347640276,
0.03463422879576683,
0.032520171254873276,
0.05807454138994217,
0.0009577887831255794,
0.048509325832128525,
0.10247037559747696,
-0.0020654331892728806,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27467 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI is still failing on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59597&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Sep 28, 2023)
- test_kneighbors_brute_backend[float32-manhattan]
COMMENT... | 27,467 | [
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0.02365492656826973,
0.02684665657579899,
0.06108284741640091,
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0.015517846681177616,
0.035631246864795685,
0.04148305207490921,
-0.0007922970689833164,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
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0.03425668552517891,
-0.03275107219815254,
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0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
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0.03425668552517891,
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0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
-0.016539422795176506,
0.03425668552517891,
-0.03275107219815254,
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0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
-0.016539422795176506,
0.03425668552517891,
-0.03275107219815254,
-0.005757103208452463,
0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
-0.016539422795176506,
0.03425668552517891,
-0.03275107219815254,
-0.005757103208452463,
0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
-0.016539422795176506,
0.03425668552517891,
-0.03275107219815254,
-0.005757103208452463,
0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 | [
0.0027694962918758392,
0.0915185883641243,
-0.037768445909023285,
0.003344873432070017,
-0.011242029257118702,
-0.016539422795176506,
0.03425668552517891,
-0.03275107219815254,
-0.005757103208452463,
0.00254644057713449,
0.01721235178411007,
-0.029528392478823662,
-0.006603193003684282,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27460 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59484&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Sep 25, 2023)
- test_pairwise_distances_argkmin[45-csr_matrix-float32-parallel_on_X-cityblo... | 27,460 | [
-0.02298765443265438,
-0.013585586100816727,
-0.03499121591448784,
-0.0072175017558038235,
0.0296584852039814,
0.02086975984275341,
0.07417215406894684,
0.048171717673540115,
0.016744639724493027,
0.011669735424220562,
0.031213510781526566,
0.03926723077893257,
0.012706258334219456,
0.0330... |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 | [
0.00842147134244442,
-0.04004660248756409,
0.03584006428718567,
0.05134347453713417,
0.09141650050878525,
-0.030228519812226295,
0.037917349487543106,
0.04903942719101906,
0.02638218179345131,
0.00842322502285242,
-0.0010060305939987302,
-0.0014501778641715646,
0.009788958355784416,
0.0222... |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 | [
0.00842147134244442,
-0.04004660248756409,
0.03584006428718567,
0.05134347453713417,
0.09141650050878525,
-0.030228519812226295,
0.037917349487543106,
0.04903942719101906,
0.02638218179345131,
0.00842322502285242,
-0.0010060305939987302,
-0.0014501778641715646,
0.009788958355784416,
0.0222... |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 | [
0.00842147134244442,
-0.04004660248756409,
0.03584006428718567,
0.05134347453713417,
0.09141650050878525,
-0.030228519812226295,
0.037917349487543106,
0.04903942719101906,
0.02638218179345131,
0.00842322502285242,
-0.0010060305939987302,
-0.0014501778641715646,
0.009788958355784416,
0.0222... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.008354858495295048,
0.013498470187187195,
-0.017401492223143578,
0.010736623778939247,
0.02407253161072731,
-0.038665350526571274,
0.007569548208266497,
-0.031172553077340126,
0.03212051838636398,
0.007768210489302874,
-0.03810560330748558,
0.08009838312864304,
-0.024565335363149643,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.020867902785539627,
0.0208478644490242,
-0.0011485678842291236,
0.00874379277229309,
0.041528791189193726,
-0.020511586219072342,
0.039526887238025665,
-0.02121516689658165,
0.06635696440935135,
-0.005353876855224371,
-0.01830417476594448,
0.10413319617509842,
-0.020785920321941376,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.0050935628823935986,
0.008201775141060352,
-0.005679459311068058,
0.011357946321368217,
0.035531703382730484,
-0.04172486811876297,
0.0023935106582939625,
-0.012709366157650948,
0.023974765092134476,
0.011094143614172935,
-0.037492696195840836,
0.06292551755905151,
-0.017759954556822777,
... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.013296493329107761,
0.03210008144378662,
-0.007018177304416895,
0.016371069476008415,
0.011503840796649456,
-0.04973047226667404,
0.01809011399745941,
-0.035640038549900055,
0.03189918026328087,
0.012171928770840168,
-0.04884563386440277,
0.0699198767542839,
-0.02986026369035244,
-0.0011... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.011449368670582771,
0.017123088240623474,
-0.023169642314314842,
0.005176458042114973,
0.01707015000283718,
-0.040921054780483246,
0.001776189194060862,
-0.026572009548544884,
0.01334555447101593,
0.003989524208009243,
-0.041690532118082047,
0.0899348184466362,
-0.03829796612262726,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.013029515743255615,
0.018058177083730698,
-0.0184224471449852,
0.02276596985757351,
0.021190853789448738,
-0.03794297203421593,
0.0010727790649980307,
-0.028458720073103905,
0.02951986901462078,
0.004245952237397432,
-0.04761597141623497,
0.07500754296779633,
-0.024743176996707916,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.012654202058911324,
0.02459617517888546,
-0.018152829259634018,
0.017894167453050613,
0.02137291245162487,
-0.04005396366119385,
0.025397149845957756,
-0.0438576377928257,
0.028863150626420975,
0.010264808312058449,
-0.044151779264211655,
0.07613139599561691,
-0.01654878631234169,
-0.003... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.0071127405390143394,
0.01657012850046158,
-0.02194881998002529,
0.018853966146707535,
0.02238251455128193,
-0.03768482431769371,
0.000059038862673332915,
-0.02898973971605301,
0.031499359756708145,
0.004994833376258612,
-0.04656071960926056,
0.08205945789813995,
-0.02335667982697487,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 | [
0.010001951828598976,
0.014359015971422195,
-0.022314008325338364,
0.02032746560871601,
0.021074257791042328,
-0.03764248266816139,
-0.00017044527339749038,
-0.03086688183248043,
0.031516559422016144,
0.004619592800736427,
-0.04416096583008766,
0.08105457574129105,
-0.023045677691698074,
-... |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 | [
0.036890625953674316,
0.04384483024477959,
0.023926984518766403,
0.0081482557579875,
0.02996889129281044,
-0.03063390403985977,
-0.049434397369623184,
-0.021602783352136612,
-0.005318968091160059,
-0.015071753412485123,
0.04954918101429939,
0.05400829389691353,
0.01793990284204483,
-0.0327... |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 | [
0.036890625953674316,
0.04384483024477959,
0.023926984518766403,
0.0081482557579875,
0.02996889129281044,
-0.03063390403985977,
-0.049434397369623184,
-0.021602783352136612,
-0.005318968091160059,
-0.015071753412485123,
0.04954918101429939,
0.05400829389691353,
0.01793990284204483,
-0.0327... |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 | [
0.036890625953674316,
0.04384483024477959,
0.023926984518766403,
0.0081482557579875,
0.02996889129281044,
-0.03063390403985977,
-0.049434397369623184,
-0.021602783352136612,
-0.005318968091160059,
-0.015071753412485123,
0.04954918101429939,
0.05400829389691353,
0.01793990284204483,
-0.0327... |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 | [
0.036890625953674316,
0.04384483024477959,
0.023926984518766403,
0.0081482557579875,
0.02996889129281044,
-0.03063390403985977,
-0.049434397369623184,
-0.021602783352136612,
-0.005318968091160059,
-0.015071753412485123,
0.04954918101429939,
0.05400829389691353,
0.01793990284204483,
-0.0327... |
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