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/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28297 | [
"Bug"
] | Getting HTTPError: HTTP Error 403: Forbidden when trying to load California Housing dataset
### Describe the bug
When trying to load the dataset I get an error.
### Steps/Code to Reproduce
```
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn... | 28,297 | [
-0.0194381196051836,
0.03637128323316574,
-0.0016146287089213729,
-0.004470166750252247,
0.06787991523742676,
0.034410033375024796,
0.049701329320669174,
0.04878684878349304,
0.027765139937400818,
-0.006652847398072481,
-0.0786249190568924,
0.001784064806997776,
0.003957824781537056,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/28296 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7683195940)** (Jan 28, 2024)
COMMENT:
It should be something transient due to network. | 28,296 | [
-0.0505213737487793,
0.003034101566299796,
-0.007499588653445244,
-0.025537338107824326,
0.02772492542862892,
0.02326763980090618,
0.004920033272355795,
0.03572908788919449,
-0.046171411871910095,
0.02361406944692135,
0.08442151546478271,
0.03392929956316948,
-0.016780443489551544,
0.06381... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28293 | [
"Bug"
] | NeighborhoodComponentsAnalysis (NCA) sets incorrect `_n_features_out` value which makes `.transform()` fail if `transform_output="pandas"`.
### Describe the bug
`NeighborhoodComponentsAnalysis.transform()` fails with the following error whenever `transform_output` is set to "pandas":
```python-traceback
ValueErro... | 28,293 | [
0.020708361640572548,
0.04167647287249565,
0.0073199146427214146,
0.011214477941393852,
0.05668819323182106,
0.0032389324624091387,
0.07974282652139664,
-0.03547963127493858,
0.013637302443385124,
-0.018841931596398354,
-0.00938587635755539,
-0.006630973890423775,
0.05381251871585846,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28280 | [
"Bug"
] | Tests failing when cuda installed but no GPU is present
after doing `conda install pytorch cupy`, my tests fail with:
```
FAILED sklearn/metrics/tests/test_common.py::test_array_api_compliance[
accuracy_score-check_array_api_binary_classification_metric-cupy-None-None]
- cupy_backends.cuda.api.runtime.CUDARunti... | 28,280 | [
-0.036066874861717224,
0.008488812483847141,
0.0014264279743656516,
-0.004311912693083286,
0.043585896492004395,
0.02101227454841137,
0.10249429941177368,
0.033044811338186264,
0.059103839099407196,
0.012733095325529575,
0.06340261548757553,
0.0012672175653278828,
-0.0038570419419556856,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28280 | [
"Bug"
] | Tests failing when cuda installed but no GPU is present
after doing `conda install pytorch cupy`, my tests fail with:
```
FAILED sklearn/metrics/tests/test_common.py::test_array_api_compliance[
accuracy_score-check_array_api_binary_classification_metric-cupy-None-None]
- cupy_backends.cuda.api.runtime.CUDARunti... | 28,280 | [
-0.04092516750097275,
0.028999555855989456,
0.003375641768798232,
-0.003078161971643567,
0.035464853048324585,
0.037894777953624725,
0.09320878982543945,
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0.0789915919303894,
0.020597899332642555,
0.07468102872371674,
0.010651965625584126,
0.004976178053766489,
0.02682... |
https://github.com/scikit-learn/scikit-learn/issues/28280 | [
"Bug"
] | Tests failing when cuda installed but no GPU is present
after doing `conda install pytorch cupy`, my tests fail with:
```
FAILED sklearn/metrics/tests/test_common.py::test_array_api_compliance[
accuracy_score-check_array_api_binary_classification_metric-cupy-None-None]
- cupy_backends.cuda.api.runtime.CUDARunti... | 28,280 | [
-0.03342094644904137,
0.01768406853079796,
-0.000029602984795928933,
-0.0046423957683146,
0.03446432203054428,
0.007098449859768152,
0.10349907726049423,
0.03634357079863548,
0.05126408860087395,
0.007288179360330105,
0.052819836884737015,
-0.01100936159491539,
0.017137810587882996,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/28280 | [
"Bug"
] | Tests failing when cuda installed but no GPU is present
after doing `conda install pytorch cupy`, my tests fail with:
```
FAILED sklearn/metrics/tests/test_common.py::test_array_api_compliance[
accuracy_score-check_array_api_binary_classification_metric-cupy-None-None]
- cupy_backends.cuda.api.runtime.CUDARunti... | 28,280 | [
-0.03519878536462784,
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0.08894454687833786,
0.03834432363510132,
0.06573113799095154,
0.021405169740319252,
0.08233006298542023,
0.003957134205847979,
-0.0027277215849608183,
0.028... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
0.022712159901857376,
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-0.024289702996611595,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
0.01739555411040783,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
0.01702113449573517,
0.043280333280563354,
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0.03294891491532326,
-0.035381268709897995,
... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
0.017281394451856613,
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0.004... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
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0.0211... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
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-0.023125866428017616,
... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
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-... |
https://github.com/scikit-learn/scikit-learn/issues/28274 | [
"Build / CI"
] | Trigger lockfile update with a comment
I've never been able to run the script to update the lock files w/o errors. My last attempt resulted in https://github.com/scikit-learn/scikit-learn/pull/28258#issuecomment-1910627538 which also didn't work, and that's after I had to install conda on my env, which I don't usually... | 28,274 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/28260 | [
"Bug"
] | ColumnTransformer output unexpected prefixed feature names from FunctionTransformer() step
### Describe the bug
The following code demonstrates that when `FunctionTransformer` is present as a step in `ColumnTransformer`, the feature names output are all prefixed with the name from the last step '**C__**'. For exam... | 28,260 | [
0.044781193137168884,
-0.00838790275156498,
0.018760208040475845,
-0.03060709312558174,
0.03732500597834587,
0.018545065075159073,
0.08526632934808731,
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0.008767453953623772,
0.038464467972517014,
-0.007059918250888586,
0.08537720888853073,
0.0459... |
https://github.com/scikit-learn/scikit-learn/issues/28260 | [
"Bug"
] | ColumnTransformer output unexpected prefixed feature names from FunctionTransformer() step
### Describe the bug
The following code demonstrates that when `FunctionTransformer` is present as a step in `ColumnTransformer`, the feature names output are all prefixed with the name from the last step '**C__**'. For exam... | 28,260 | [
0.044781193137168884,
-0.00838790275156498,
0.018760208040475845,
-0.03060709312558174,
0.03732500597834587,
0.018545065075159073,
0.08526632934808731,
0.01188263762742281,
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0.008767453953623772,
0.038464467972517014,
-0.007059918250888586,
0.08537720888853073,
0.0459... |
https://github.com/scikit-learn/scikit-learn/issues/28260 | [
"Bug"
] | ColumnTransformer output unexpected prefixed feature names from FunctionTransformer() step
### Describe the bug
The following code demonstrates that when `FunctionTransformer` is present as a step in `ColumnTransformer`, the feature names output are all prefixed with the name from the last step '**C__**'. For exam... | 28,260 | [
0.044781193137168884,
-0.00838790275156498,
0.018760208040475845,
-0.03060709312558174,
0.03732500597834587,
0.018545065075159073,
0.08526632934808731,
0.01188263762742281,
-0.031558431684970856,
0.008767453953623772,
0.038464467972517014,
-0.007059918250888586,
0.08537720888853073,
0.0459... |
https://github.com/scikit-learn/scikit-learn/issues/28260 | [
"Bug"
] | ColumnTransformer output unexpected prefixed feature names from FunctionTransformer() step
### Describe the bug
The following code demonstrates that when `FunctionTransformer` is present as a step in `ColumnTransformer`, the feature names output are all prefixed with the name from the last step '**C__**'. For exam... | 28,260 | [
0.044781193137168884,
-0.00838790275156498,
0.018760208040475845,
-0.03060709312558174,
0.03732500597834587,
0.018545065075159073,
0.08526632934808731,
0.01188263762742281,
-0.031558431684970856,
0.008767453953623772,
0.038464467972517014,
-0.007059918250888586,
0.08537720888853073,
0.0459... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.007471554912626743,
0.049155209213495255,
-0.014106087386608124,
-0.05591343343257904,
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0.006200907751917839,
0.030177513137459755,
0.008425588719546795,
0.019031571224331856,
-0.05040814355015755,
0.08280900120735168,
0.02779121696949005,
-0.029570788145065308,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.006563825067132711,
0.047617677599191666,
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0.020693354308605194,
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0.09388444572687149,
0.028425103053450584,
-0.029100598767399788,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.010209232568740845,
0.05205073580145836,
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0.022163819521665573,
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0.09272635728120804,
0.024439195170998573,
-0.024451972916722298,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.008586644195020199,
0.032866861671209335,
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-0.02254480868577957,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.003295825095847249,
0.031709812581539154,
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0.08751080930233002,
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-0.025479698553681374,
... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.0116206593811512,
0.04605875164270401,
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0.014796142466366291,
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0.060759734362363815,
0.00012654731108341366,
-0.003880316624417901,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.012151074595749378,
0.04988223314285278,
-0.0330258272588253,
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0.00478812912479043,
0.012736656703054905,
0.0051845223642885685,
0.0016142083331942558,
-0.05244391784071922,
0.07922322303056717,
0.005272706504911184,
-0.025707663968205452,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.009800408966839314,
0.034095000475645065,
-0.021832985803484917,
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0.011304852552711964,
0.028333773836493492,
0.014888180419802666,
0.013701965101063251,
-0.056495390832424164,
0.08245962858200073,
0.020169325172901154,
-0.026297271251678467,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.012599818408489227,
0.04341534152626991,
-0.01979886181652546,
-0.06065256521105766,
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0.026903120800852776,
0.008501033298671246,
0.009966312907636166,
-0.046756256371736526,
0.07115286588668823,
0.021984973922371864,
-0.030647659674286842,
0.01429... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.01591138355433941,
0.050792206078767776,
-0.019192446023225784,
-0.05836072936654091,
-0.02966877445578575,
0.03216291591525078,
0.018689826130867004,
-0.002047364367172122,
0.0000014947225963624078,
-0.04383106902241707,
0.06540260463953018,
0.012629767879843712,
-0.03009323962032795,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.00904689822345972,
0.05368480086326599,
-0.017601095139980316,
-0.04901942238211632,
-0.029739035293459892,
0.037519846111536026,
0.027780668810009956,
-0.004406764637678862,
0.015894880518317223,
-0.034153759479522705,
0.0781664028763771,
0.010116423480212688,
-0.031995344907045364,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28259 | [
"Build / CI"
] | RFC bump Cython minimum supported version to 3.0.8
Currently we still have Cython 0.29.33 as our minimum Cython version. We may want to decide to bump our Cython requirement to Cython >= 3.0.8. I am +1 for this given that:
- https://github.com/scikit-learn/scikit-learn/issues/27682 needs Cython >= 3
- https://gith... | 28,259 | [
0.0061822207644581795,
0.03826659917831421,
-0.017145799472928047,
-0.05869608744978905,
-0.029750941321253777,
0.009177611209452152,
0.031468767672777176,
0.012224296107888222,
0.019610634073615074,
-0.047128379344940186,
0.08649395406246185,
0.027095390483736992,
-0.02934720367193222,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28254 | [
"Bug",
"High Priority"
] | DecisionTree does not handle properly missing values in criterion partitioning
### Describe the bug
I tried using `RFECV` with `RandomForestClassifier` in version 1.4.0 on data containing NaNs and got the following error:
```
ValueError: Input contains NaN.
```
This is my first time opening an issue to an open-... | 28,254 | [
0.012000512331724167,
-0.011117406189441681,
0.012437361292541027,
0.012938267551362514,
0.05069561302661896,
-0.02081608772277832,
-0.006015613675117493,
0.034372568130493164,
-0.006904910318553448,
-0.002104775747284293,
0.044485997408628464,
0.006357651203870773,
0.034977592527866364,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28253 | [
"New Feature",
"Needs Triage"
] | ridge regression, objective function
### Describe the workflow you want to enable
add another option to define the objective function in another way. currently, the objective function is defined as ||y - Xw||^2_2 + alpha * ||w||^2_2, but when the number of observations, n, is huge. The l2 penalty plays a tiny role, s... | 28,253 | [
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0.13434621691703796,
0.05307689309120178,
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-0.0341046117246151,
0.024966666474938393,
0.096011221408844,
-0.006325089372694492,
-0.005237186793237925,
0.005531029310077429,
0.04937661811709404,
-0.009375681169331074,
0.013... |
https://github.com/scikit-learn/scikit-learn/issues/28246 | [
"API",
"Needs Decision"
] | Metadata routing prevents usage of `IterativeImputer` with `ColumnTransformer`
### Describe the bug
Enabling metadata makes `IterativeImputer` fail when inside a meta-estimator like `ColumnTransformer`, even when there is no metadata requested nor passed.
### Steps/Code to Reproduce
```python
import sklearn
from ... | 28,246 | [
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0.08914210647344589,
0.02517540194094181,
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-0.009501968510448933,
0.007435272913426161,
0.04749534651637077,
0.014573981054127216,
-0.018... |
https://github.com/scikit-learn/scikit-learn/issues/28245 | [
"Bug",
"module:calibration"
] | CalibratedClassifierCV in 1.4 broke the compatibility with custom estimators that outputs float32.
### Describe the bug
Hi, this is an issue from xgboost forwarded here https://github.com/dmlc/xgboost/issues/10004 with copied code and backtrace.
XGBoost outputs float32 in its inference procedure, it seems the late... | 28,245 | [
-0.01888800784945488,
-0.005435611587017775,
0.04036170616745949,
-0.024983903393149376,
0.0476568378508091,
0.002652317751199007,
0.017396463081240654,
0.05791560187935829,
0.001858046744018793,
-0.005390196107327938,
-0.0005647175712510943,
0.04448242485523224,
0.027285555377602577,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28245 | [
"Bug",
"module:calibration"
] | CalibratedClassifierCV in 1.4 broke the compatibility with custom estimators that outputs float32.
### Describe the bug
Hi, this is an issue from xgboost forwarded here https://github.com/dmlc/xgboost/issues/10004 with copied code and backtrace.
XGBoost outputs float32 in its inference procedure, it seems the late... | 28,245 | [
-0.01888800784945488,
-0.005435611587017775,
0.04036170616745949,
-0.024983903393149376,
0.0476568378508091,
0.002652317751199007,
0.017396463081240654,
0.05791560187935829,
0.001858046744018793,
-0.005390196107327938,
-0.0005647175712510943,
0.04448242485523224,
0.027285555377602577,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28245 | [
"Bug",
"module:calibration"
] | CalibratedClassifierCV in 1.4 broke the compatibility with custom estimators that outputs float32.
### Describe the bug
Hi, this is an issue from xgboost forwarded here https://github.com/dmlc/xgboost/issues/10004 with copied code and backtrace.
XGBoost outputs float32 in its inference procedure, it seems the late... | 28,245 | [
-0.01888800784945488,
-0.005435611587017775,
0.04036170616745949,
-0.024983903393149376,
0.0476568378508091,
0.002652317751199007,
0.017396463081240654,
0.05791560187935829,
0.001858046744018793,
-0.005390196107327938,
-0.0005647175712510943,
0.04448242485523224,
0.027285555377602577,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28243 | [
"Needs Triage"
] | RFC: Introduce DBCV cluster validity metric
Dear scikit-learn core developers/maintainers,
I am opening this issue to make the case for the inclusion of DBCV as a scikit-learn cluster metric. I went ahead and tried to address possible concerns (according to your contribution guidelines) upfront (see below). The eff... | 28,243 | [
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-0.013679458759725094,
0.011928176507353783,
-0.04120589420199394,
-0.02773052267730236,
-0.003382293274626136,
0.024350613355636597,
0.018346983939409256,
-0.006421210709959269,
0.019282571971416473,
0.06860122084617615,
-0.033303435891866684,
0.005921855568885803,
... |
https://github.com/scikit-learn/scikit-learn/issues/28243 | [
"Needs Triage"
] | RFC: Introduce DBCV cluster validity metric
Dear scikit-learn core developers/maintainers,
I am opening this issue to make the case for the inclusion of DBCV as a scikit-learn cluster metric. I went ahead and tried to address possible concerns (according to your contribution guidelines) upfront (see below). The eff... | 28,243 | [
-0.010863266885280609,
-0.013679458759725094,
0.011928176507353783,
-0.04120589420199394,
-0.02773052267730236,
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0.024350613355636597,
0.018346983939409256,
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0.019282571971416473,
0.06860122084617615,
-0.033303435891866684,
0.005921855568885803,
... |
https://github.com/scikit-learn/scikit-learn/issues/28243 | [
"Needs Triage"
] | RFC: Introduce DBCV cluster validity metric
Dear scikit-learn core developers/maintainers,
I am opening this issue to make the case for the inclusion of DBCV as a scikit-learn cluster metric. I went ahead and tried to address possible concerns (according to your contribution guidelines) upfront (see below). The eff... | 28,243 | [
-0.010863266885280609,
-0.013679458759725094,
0.011928176507353783,
-0.04120589420199394,
-0.02773052267730236,
-0.003382293274626136,
0.024350613355636597,
0.018346983939409256,
-0.006421210709959269,
0.019282571971416473,
0.06860122084617615,
-0.033303435891866684,
0.005921855568885803,
... |
https://github.com/scikit-learn/scikit-learn/issues/28239 | [
"Bug"
] | Metadata routing breaks `MultioutputClassifier` with estimator that doesn't support `sample_weight` in fit.
### Describe the bug
When combining `MultioutputClassifier` with an estimator that doesn't have sample_weight as metadata in the `fit` method, such as `LinearDiscriminantAnalysis`, it fails to fit.
### Steps/C... | 28,239 | [
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0.036629725247621536,
0.030232664197683334,
0.017666935920715332,
0.08757438510656357,
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0.03625603765249252,
0.013978506438434124,
-0.0013217773521319032,
-0.03409169614315033,
0.02144627645611763,
0.08173306286334991,
-0.008325985632836819,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/28234 | [
"Bug",
"Needs Triage"
] | AUC of the ROC is based on class labels (predict()) instead of scores (decision_function() or predict_proba()) during call to cross_validate
### Describe the bug
Related to #27977
Also applies to pr_auc metric.
When defining multi-metric scoring as a dictionary and passing to `cross_validate()`:
```
scori... | 28,234 | [
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0.016331005841493607,
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-0.0005139184067957103,
0.02298182249069214,
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0.0026534446515142918,
-0.005706832744181156,
0.01816706173121929,
0.00047036370960995555,
... |
https://github.com/scikit-learn/scikit-learn/issues/28232 | [
"Bug"
] | Regression in `ColumnTransformer` due to internal `FunctionTransformer`
In https://github.com/scikit-learn/scikit-learn/pull/27801, we make sure that the output of `func` and the `get_feature_names_out` are consistent.
However, it seems that we have a side effect when the `FunctionTransformer` is created inside a `... | 28,232 | [
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0.09046050161123276,
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0.042350564152002335,
0.025749025866389275,
-0.010545327328145504,
0.07528388500213623,
0.0344638... |
https://github.com/scikit-learn/scikit-learn/issues/28232 | [
"Bug"
] | Regression in `ColumnTransformer` due to internal `FunctionTransformer`
In https://github.com/scikit-learn/scikit-learn/pull/27801, we make sure that the output of `func` and the `get_feature_names_out` are consistent.
However, it seems that we have a side effect when the `FunctionTransformer` is created inside a `... | 28,232 | [
0.040633007884025574,
0.04431310296058655,
0.03922604024410248,
0.001179603859782219,
0.0656353011727333,
0.005792966112494469,
0.09046050161123276,
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0.042350564152002335,
0.025749025866389275,
-0.010545327328145504,
0.07528388500213623,
0.0344638... |
https://github.com/scikit-learn/scikit-learn/issues/28229 | [
"Bug"
] | Trees fitted in 1.3.2 produce different outcome when evaluated in 1.4
### Describe the bug
We have a number of ensemble tree models in production fitted using 1.3.2 or older. Many of these models produce different outcomes when evaluated on the same data in sklearn 1.3.2 and 1.4.
Analysis led me to the change in `... | 28,229 | [
-0.005102391354739666,
0.03577263280749321,
0.011797391809523106,
-0.018554003909230232,
-0.014220135286450386,
-0.05805690959095955,
-0.05577798932790756,
0.02427951619029045,
-0.048972588032484055,
-0.024347906932234764,
0.06627675145864487,
0.004001851659268141,
0.012689126655459404,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28229 | [
"Bug"
] | Trees fitted in 1.3.2 produce different outcome when evaluated in 1.4
### Describe the bug
We have a number of ensemble tree models in production fitted using 1.3.2 or older. Many of these models produce different outcomes when evaluated on the same data in sklearn 1.3.2 and 1.4.
Analysis led me to the change in `... | 28,229 | [
-0.005102391354739666,
0.03577263280749321,
0.011797391809523106,
-0.018554003909230232,
-0.014220135286450386,
-0.05805690959095955,
-0.05577798932790756,
0.02427951619029045,
-0.048972588032484055,
-0.024347906932234764,
0.06627675145864487,
0.004001851659268141,
0.012689126655459404,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28229 | [
"Bug"
] | Trees fitted in 1.3.2 produce different outcome when evaluated in 1.4
### Describe the bug
We have a number of ensemble tree models in production fitted using 1.3.2 or older. Many of these models produce different outcomes when evaluated on the same data in sklearn 1.3.2 and 1.4.
Analysis led me to the change in `... | 28,229 | [
-0.005102391354739666,
0.03577263280749321,
0.011797391809523106,
-0.018554003909230232,
-0.014220135286450386,
-0.05805690959095955,
-0.05577798932790756,
0.02427951619029045,
-0.048972588032484055,
-0.024347906932234764,
0.06627675145864487,
0.004001851659268141,
0.012689126655459404,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28218 | [
"Bug",
"Needs Triage"
] | StratifiedGroupKFold not ensuring Stratified splits
### Describe the bug
The existing implementation of the "StratifiedGroupKFold" class does not consistently achieve accurate stratified splits when dividing datasets into subsets, particularly when the dataset contains a relatively small number of samples.
None o... | 28,218 | [
-0.024933675304055214,
0.029294202104210854,
-0.0007598093943670392,
0.05611252412199974,
0.028472643345594406,
-0.028864407911896706,
0.0815243273973465,
0.05124858766794205,
0.00939901452511549,
-0.04840251803398132,
0.03507958725094795,
-0.015974799171090126,
0.00010494913294678554,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28204 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7598773815)** (Jan 21, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/7606191759) on Jan 22, 2024 | 28,204 | [
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0.03669045865535736,
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-0.011270873248577118,
0.00955085176974535,
0.013113472610712051,
0.0170503631234169,
0.041145361959934235,
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0.028900684788823128,
0.07935474812984467,
0.038421906530857086,
-0.014124053530395031,
0.076... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
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0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28191 | [
"Enhancement"
] | ENH: use the sparse-sparse backend for computing pairwise distance
First reported in: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1056
We have a regression in `kneighbors` with sparse matrix from 1.1.X to 1.3.X.
A code sample to reproduce:
```python
# %%
import sklearn
sklearn.__version__... | 28,191 | [
0.0025657243095338345,
-0.012679604813456535,
0.02654603309929371,
0.023420890793204308,
0.019062921404838562,
-0.015796227380633354,
0.030964793637394905,
0.04503070190548897,
0.012992924079298973,
-0.01887943409383297,
-0.014833458699285984,
0.04369870573282242,
0.004348699003458023,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28186 | [
"Bug"
] | Metadata Routing breaks ColumnTransformer
### Describe the bug
When enable_metadata_routing is set to True, fitting a ColumnTransformer gets AttributeError: 'ColumnTransformer' object has no attribute '_columns'.
### Steps/Code to Reproduce
```python
from numpy.random import default_rng
import pandas as pd
... | 28,186 | [
0.004225343931466341,
0.05671823397278786,
0.041510868817567825,
-0.027765315026044846,
0.11362502723932266,
0.0030591883696615696,
0.027339747175574303,
0.010620084591209888,
-0.05991299822926521,
-0.01225332822650671,
0.04707362502813339,
0.05085316300392151,
-0.0027881592977792025,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/28180 | [
"Needs Triage"
] | Isolation Forest Contamination Rate has no effect on AUC
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28172
<div type='discussions-op-text'>
<sup>Originally posted by **robertken** January 18, 2024</sup>
I am experiencing some unexpected behavior with the Isolation Forest. I'm using... | 28,180 | [
0.0017654324183240533,
-0.003542202990502119,
0.02129811979830265,
0.013633695431053638,
0.05485175549983978,
-0.026655811816453934,
-0.04358239471912384,
-0.0199093297123909,
-0.027197841554880142,
0.003591496730223298,
0.008932599797844887,
0.010304505005478859,
0.004260974004864693,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28178 | [
"Bug"
] | Exception in LogisticRegressionCV
### Describe the bug
The code provided below raises ValueError. I guess that the problem is that minor classes may not be included in **train** or **val** sets for some folds during internal cross-validation, even with stratified split. This produces errors with some metrics other ... | 28,178 | [
0.01765560917556286,
-0.027168534696102142,
0.044892825186252594,
0.01967792585492134,
0.11731485277414322,
-0.0005083542200736701,
0.0390859954059124,
0.027944205328822136,
0.02187955379486084,
-0.028916049748659134,
0.06220400333404541,
0.022468125447630882,
-0.01202184334397316,
0.02078... |
https://github.com/scikit-learn/scikit-learn/issues/28175 | [
"Bug"
] | Inconsistency in `DecisionTreeClassifier` Threshold Behavior
### Describe the bug
I've encountered an unexpected behavior in `DecisionTreeClassifier` when using a decision stump (a tree with one root node and two leaf children). My assumption is based on the standard decision tree logic where a feature value `x` is c... | 28,175 | [
-0.003309008665382862,
-0.07082530111074448,
0.007521139457821846,
0.007898353971540928,
0.02237156219780445,
-0.019872896373271942,
-0.007241061422973871,
0.007810936775058508,
-0.04048934951424599,
-0.04348441958427429,
0.03626140207052231,
0.03609145060181618,
0.031256500631570816,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28175 | [
"Bug"
] | Inconsistency in `DecisionTreeClassifier` Threshold Behavior
### Describe the bug
I've encountered an unexpected behavior in `DecisionTreeClassifier` when using a decision stump (a tree with one root node and two leaf children). My assumption is based on the standard decision tree logic where a feature value `x` is c... | 28,175 | [
-0.003309008665382862,
-0.07082530111074448,
0.007521139457821846,
0.007898353971540928,
0.02237156219780445,
-0.019872896373271942,
-0.007241061422973871,
0.007810936775058508,
-0.04048934951424599,
-0.04348441958427429,
0.03626140207052231,
0.03609145060181618,
0.031256500631570816,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28175 | [
"Bug"
] | Inconsistency in `DecisionTreeClassifier` Threshold Behavior
### Describe the bug
I've encountered an unexpected behavior in `DecisionTreeClassifier` when using a decision stump (a tree with one root node and two leaf children). My assumption is based on the standard decision tree logic where a feature value `x` is c... | 28,175 | [
-0.003309008665382862,
-0.07082530111074448,
0.007521139457821846,
0.007898353971540928,
0.02237156219780445,
-0.019872896373271942,
-0.007241061422973871,
0.007810936775058508,
-0.04048934951424599,
-0.04348441958427429,
0.03626140207052231,
0.03609145060181618,
0.031256500631570816,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28175 | [
"Bug"
] | Inconsistency in `DecisionTreeClassifier` Threshold Behavior
### Describe the bug
I've encountered an unexpected behavior in `DecisionTreeClassifier` when using a decision stump (a tree with one root node and two leaf children). My assumption is based on the standard decision tree logic where a feature value `x` is c... | 28,175 | [
-0.003309008665382862,
-0.07082530111074448,
0.007521139457821846,
0.007898353971540928,
0.02237156219780445,
-0.019872896373271942,
-0.007241061422973871,
0.007810936775058508,
-0.04048934951424599,
-0.04348441958427429,
0.03626140207052231,
0.03609145060181618,
0.031256500631570816,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28175 | [
"Bug"
] | Inconsistency in `DecisionTreeClassifier` Threshold Behavior
### Describe the bug
I've encountered an unexpected behavior in `DecisionTreeClassifier` when using a decision stump (a tree with one root node and two leaf children). My assumption is based on the standard decision tree logic where a feature value `x` is c... | 28,175 | [
-0.003309008665382862,
-0.07082530111074448,
0.007521139457821846,
0.007898353971540928,
0.02237156219780445,
-0.019872896373271942,
-0.007241061422973871,
0.007810936775058508,
-0.04048934951424599,
-0.04348441958427429,
0.03626140207052231,
0.03609145060181618,
0.031256500631570816,
-0.0... |
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