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/27306 | [
"Bug"
] | ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN
### Describe the bug
In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more ... | 27,306 | [
0.006584491580724716,
-0.023408569395542145,
0.037300825119018555,
0.02155185304582119,
0.04046814516186714,
-0.020097216591238976,
0.017587754875421524,
0.03695567697286606,
-0.0037455030251294374,
-0.02139442041516304,
0.010866465978324413,
0.00810164026916027,
0.027918698266148567,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/27306 | [
"Bug"
] | ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN
### Describe the bug
In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more ... | 27,306 | [
0.006584491580724716,
-0.023408569395542145,
0.037300825119018555,
0.02155185304582119,
0.04046814516186714,
-0.020097216591238976,
0.017587754875421524,
0.03695567697286606,
-0.0037455030251294374,
-0.02139442041516304,
0.010866465978324413,
0.00810164026916027,
0.027918698266148567,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/27305 | [
"New Feature",
"Moderate",
"module:ensemble"
] | Monotonicity constraints for GradientBoostingClassifier and GradientBoostingRegressor
### Describe the workflow you want to enable
As a follow-up of #13649, I'd like to use
```python
GradientBoostingClassifier(monotonic_cst=...)
```
same as in `HistGradientBoostingClassifier` and int `RandomForestClassifier`.
##... | 27,305 | [
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0.05044294893741608,
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0.000... |
https://github.com/scikit-learn/scikit-learn/issues/27302 | [
"Needs Triage"
] | ⚠️ CI failed on macOS.pylatest_conda_mkl_no_openmp ⚠️
**CI failed on [macOS.pylatest_conda_mkl_no_openmp](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58670&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2)** (Sep 06, 2023)
- test_pickle_version_warning_is_issued_when_no_version_info_in_pickl... | 27,302 | [
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0.074... |
https://github.com/scikit-learn/scikit-learn/issues/27302 | [
"Needs Triage"
] | ⚠️ CI failed on macOS.pylatest_conda_mkl_no_openmp ⚠️
**CI failed on [macOS.pylatest_conda_mkl_no_openmp](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58670&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2)** (Sep 06, 2023)
- test_pickle_version_warning_is_issued_when_no_version_info_in_pickl... | 27,302 | [
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0.06... |
https://github.com/scikit-learn/scikit-learn/issues/27294 | [
"New Feature",
"Needs Triage"
] | Incremental F-regression
### Describe the workflow you want to enable
For situations with many variables and low memory, for example lags taken from a high frequency time series and corresponding exogenous variables, it's possible to go though each column one by one or in batches, in an ordered manner, select the m... | 27,294 | [
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0.009362975135445595,
0.10806947... |
https://github.com/scikit-learn/scikit-learn/issues/27294 | [
"New Feature",
"Needs Triage"
] | Incremental F-regression
### Describe the workflow you want to enable
For situations with many variables and low memory, for example lags taken from a high frequency time series and corresponding exogenous variables, it's possible to go though each column one by one or in batches, in an ordered manner, select the m... | 27,294 | [
-0.04222620651125908,
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0.0067530907690525055,
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0.009455731138586998,
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0.0830... |
https://github.com/scikit-learn/scikit-learn/issues/27285 | [
"Documentation"
] | Weighted ridge regression regularization variable is dependent on sample weight magnitude
### Describe the issue linked to the documentation
When doing weighted ridge regression, the value of the regularization parameter for a particular solution is dependent on the sample weight vector due to scaling in the implemen... | 27,285 | [
0.0019962205551564693,
0.016172979027032852,
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https://github.com/scikit-learn/scikit-learn/issues/27285 | [
"Documentation"
] | Weighted ridge regression regularization variable is dependent on sample weight magnitude
### Describe the issue linked to the documentation
When doing weighted ridge regression, the value of the regularization parameter for a particular solution is dependent on the sample weight vector due to scaling in the implemen... | 27,285 | [
-0.0025363434106111526,
0.009293535724282265,
0.018410595133900642,
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0.02011123113334179,
-0.0105... |
https://github.com/scikit-learn/scikit-learn/issues/27272 | [
"Bug",
"Needs Triage"
] | MultiOutputRegressor _ BUG
### Describe the bug
```pytb
from sklearn.multioutput import MultiOutputRegressor
File "../lib/python3.10/site-packages/sklearn/multioutput.py", line 45, in <module>
from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
ImportError: cannot import n... | 27,272 | [
0.021599959582090378,
-0.0393630713224411,
0.026902485638856888,
-0.041028887033462524,
0.07562478631734848,
0.016029134392738342,
0.053347740322351456,
0.03235035017132759,
0.03092752769589424,
-0.006710466928780079,
0.016450250521302223,
0.05835110321640968,
0.022744134068489075,
0.03967... |
https://github.com/scikit-learn/scikit-learn/issues/27272 | [
"Bug",
"Needs Triage"
] | MultiOutputRegressor _ BUG
### Describe the bug
```pytb
from sklearn.multioutput import MultiOutputRegressor
File "../lib/python3.10/site-packages/sklearn/multioutput.py", line 45, in <module>
from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
ImportError: cannot import n... | 27,272 | [
0.010750263929367065,
-0.031416893005371094,
0.021229764446616173,
-0.04345812648534775,
0.09262491017580032,
-0.004604463465511799,
0.04970667138695717,
0.036987561732530594,
0.017024273052811623,
-0.0008806240512058139,
0.025953147560358047,
0.05937342345714569,
0.03264729678630829,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27272 | [
"Bug",
"Needs Triage"
] | MultiOutputRegressor _ BUG
### Describe the bug
```pytb
from sklearn.multioutput import MultiOutputRegressor
File "../lib/python3.10/site-packages/sklearn/multioutput.py", line 45, in <module>
from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
ImportError: cannot import n... | 27,272 | [
0.01839139312505722,
-0.018887685611844063,
0.020666854456067085,
-0.0550321526825428,
0.08454301953315735,
-0.002099966863170266,
0.050585757941007614,
0.03484274074435234,
0.020936841145157814,
-0.007738054729998112,
0.02690912038087845,
0.06115617975592613,
0.02855563722550869,
0.024733... |
https://github.com/scikit-learn/scikit-learn/issues/27272 | [
"Bug",
"Needs Triage"
] | MultiOutputRegressor _ BUG
### Describe the bug
```pytb
from sklearn.multioutput import MultiOutputRegressor
File "../lib/python3.10/site-packages/sklearn/multioutput.py", line 45, in <module>
from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
ImportError: cannot import n... | 27,272 | [
0.014901460148394108,
-0.03785504028201103,
0.023755835369229317,
-0.048102233558893204,
0.087979756295681,
0.0037482657935470343,
0.05461759865283966,
0.03234398365020752,
0.024626506492495537,
0.00003116859443252906,
0.02475210279226303,
0.05069296061992645,
0.02588292956352234,
0.039151... |
https://github.com/scikit-learn/scikit-learn/issues/27271 | [
"Bug",
"Needs Triage"
] | feature_names returned by load_breast_cancer() is np.array, not list.
### Describe the bug
According to the online documentation(https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html), `feature_names` and `target_names` should be a list, but the value returned by the `load_breast_... | 27,271 | [
0.060727912932634354,
-0.03591197729110718,
-0.0012205103412270546,
-0.020121177658438683,
0.05919088050723076,
0.04184228926897049,
0.0344335176050663,
0.01820339635014534,
0.01174071617424488,
0.010763011872768402,
0.0025307994801551104,
0.03291700780391693,
0.014921939000487328,
0.02557... |
https://github.com/scikit-learn/scikit-learn/issues/27271 | [
"Bug",
"Needs Triage"
] | feature_names returned by load_breast_cancer() is np.array, not list.
### Describe the bug
According to the online documentation(https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html), `feature_names` and `target_names` should be a list, but the value returned by the `load_breast_... | 27,271 | [
0.060727912932634354,
-0.03591197729110718,
-0.0012205103412270546,
-0.020121177658438683,
0.05919088050723076,
0.04184228926897049,
0.0344335176050663,
0.01820339635014534,
0.01174071617424488,
0.010763011872768402,
0.0025307994801551104,
0.03291700780391693,
0.014921939000487328,
0.02557... |
https://github.com/scikit-learn/scikit-learn/issues/27271 | [
"Bug",
"Needs Triage"
] | feature_names returned by load_breast_cancer() is np.array, not list.
### Describe the bug
According to the online documentation(https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html), `feature_names` and `target_names` should be a list, but the value returned by the `load_breast_... | 27,271 | [
0.060727912932634354,
-0.03591197729110718,
-0.0012205103412270546,
-0.020121177658438683,
0.05919088050723076,
0.04184228926897049,
0.0344335176050663,
0.01820339635014534,
0.01174071617424488,
0.010763011872768402,
0.0025307994801551104,
0.03291700780391693,
0.014921939000487328,
0.02557... |
https://github.com/scikit-learn/scikit-learn/issues/27268 | [
"Build / CI"
] | macOS.pylatest_conda_forge_mkl sometimes fails pickling test
This test has failed in https://github.com/scikit-learn/scikit-learn/pull/27266 with [those logs](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58609&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742d... | 27,268 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/27268 | [
"Build / CI"
] | macOS.pylatest_conda_forge_mkl sometimes fails pickling test
This test has failed in https://github.com/scikit-learn/scikit-learn/pull/27266 with [those logs](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58609&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742d... | 27,268 | [
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https://github.com/scikit-learn/scikit-learn/issues/27268 | [
"Build / CI"
] | macOS.pylatest_conda_forge_mkl sometimes fails pickling test
This test has failed in https://github.com/scikit-learn/scikit-learn/pull/27266 with [those logs](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58609&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742d... | 27,268 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/27268 | [
"Build / CI"
] | macOS.pylatest_conda_forge_mkl sometimes fails pickling test
This test has failed in https://github.com/scikit-learn/scikit-learn/pull/27266 with [those logs](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58609&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742d... | 27,268 | [
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https://github.com/scikit-learn/scikit-learn/issues/27260 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58565&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Sep 01, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-... | 27,260 | [
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0.023669296875596046,
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0.015497331507503986,
0.0805... |
https://github.com/scikit-learn/scikit-learn/issues/27260 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58565&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Sep 01, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-... | 27,260 | [
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0.032542500644922256,
0.020976997911930084,
0.02570643275976181,
0.074... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
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0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
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0.024158816784620285,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27259 | [
"New Feature",
"Needs Decision - Include Feature"
] | New clustering metrics
### Describe the workflow you want to enable
Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html... | 27,259 | [
-0.019781267270445824,
0.010339717380702496,
0.04255415499210358,
-0.04441465064883232,
0.0037235000636428595,
-0.0026590595953166485,
0.05699837580323219,
0.03058210201561451,
0.07146791368722916,
0.016577964648604393,
-0.022315992042422295,
0.024158816784620285,
-0.007740338332951069,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27256 | [
"Bug",
"Needs Triage"
] | Lasso incompatible with scipy=1.11 with sparse X
### Describe the bug
The Lasso() regressor seems to be incompatible with the newest release of scipy=1.11.0 with sparse X input.
### Steps/Code to Reproduce
```
import numpy as np
from scipy.sparse import csc_array
from sklearn.linear_model import Lasso
if _... | 27,256 | [
0.025210928171873093,
0.025626488029956818,
0.03307928517460823,
-0.0046945479698479176,
0.10546359419822693,
-0.014237870462238789,
0.030610375106334686,
0.06096630170941353,
0.06022179126739502,
-0.002138117328286171,
0.037971675395965576,
0.05482395738363266,
-0.005531121976673603,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27256 | [
"Bug",
"Needs Triage"
] | Lasso incompatible with scipy=1.11 with sparse X
### Describe the bug
The Lasso() regressor seems to be incompatible with the newest release of scipy=1.11.0 with sparse X input.
### Steps/Code to Reproduce
```
import numpy as np
from scipy.sparse import csc_array
from sklearn.linear_model import Lasso
if _... | 27,256 | [
0.025210928171873093,
0.025626488029956818,
0.03307928517460823,
-0.0046945479698479176,
0.10546359419822693,
-0.014237870462238789,
0.030610375106334686,
0.06096630170941353,
0.06022179126739502,
-0.002138117328286171,
0.037971675395965576,
0.05482395738363266,
-0.005531121976673603,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27255 | [
"Documentation",
"Needs Triage"
] | Issue in copy to clipboard while Installing
### Describe the issue linked to the documentation
While I was installing scikit-learn I found a small issue when I do select Copy to Clipboard it is not copying the actual text which is "pip install -U scikit-learn"
instead it is copying like
"python3 -m venv sklearn-v... | 27,255 | [
0.05513516813516617,
-0.03243425115942955,
-0.02404589019715786,
-0.004536233376711607,
0.02612505480647087,
0.011419408023357391,
-0.03634299710392952,
0.015032592229545116,
-0.012183183804154396,
-0.02034296654164791,
-0.004656570963561535,
0.0791659727692604,
0.0417511910200119,
0.01847... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.03073214367032051,
0.019262513145804405,
0.023373838514089584,
-0.023361405357718468,
0.04178686812520027,
-0.009006225503981113,
0.027521254494786263,
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-0.0055884672328829765,
-0.030163416638970375,
0.008955838158726692,
-0.0379444919526577,
-0.05011773854494095,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.040067099034786224,
0.01658620871603489,
0.03560677915811539,
-0.047134168446063995,
0.013495254330337048,
-0.014064174145460129,
0.022159850224852562,
-0.05465354770421982,
0.013843819499015808,
-0.012966587208211422,
0.03360166773200035,
-0.02444937452673912,
-0.05743793770670891,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.03565286472439766,
0.016784189268946648,
0.03231014683842659,
-0.05171217769384384,
0.003240575548261404,
-0.009991921484470367,
0.023768853396177292,
-0.05430813506245613,
0.009361619129776955,
-0.013533937744796276,
0.04362818971276283,
-0.031979989260435104,
-0.04706357419490814,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.03914522007107735,
-0.01522833202034235,
0.03792554512619972,
-0.048505570739507675,
0.011489667929708958,
-0.02132549323141575,
0.02782192826271057,
-0.0529942512512207,
0.009304934181272984,
-0.008777010254561901,
0.02111709490418434,
-0.02468431368470192,
-0.0339292548596859,
0.06845... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.015352853573858738,
-0.0025119369383901358,
0.02116532437503338,
-0.05951258912682533,
-0.00261470559053123,
0.010822837240993977,
0.051727067679166794,
-0.016653776168823242,
0.03982623293995857,
-0.0021738209761679173,
0.04195655882358551,
-0.05716458708047867,
-0.05019451677799225,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.015500354580581188,
-0.000281696324236691,
0.029210878536105156,
-0.02467970922589302,
0.0045685237273573875,
0.001984967151656747,
0.02069382183253765,
-0.06850997358560562,
-0.0052854386158287525,
-0.01507440023124218,
0.0301473680883646,
-0.0744280070066452,
-0.03205254673957825,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.037690188735723495,
0.0343351736664772,
0.03518477454781532,
-0.024730240926146507,
0.012374237179756165,
-0.0035929768346250057,
0.008198539726436138,
-0.0484512560069561,
-0.0012069287477061152,
-0.007968607358634472,
0.024752330034971237,
-0.022385671734809875,
-0.04177521541714668,
... |
https://github.com/scikit-learn/scikit-learn/issues/27249 | [
"New Feature",
"module:metrics",
"Needs Triage"
] | sklearn.metrics.logAUC
### Describe the workflow you want to enable
Computing logAUC values.
### Describe your proposed solution
$LogAUC_\lambda=\frac{\sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}$
### Describe alternatives you've considered... | 27,249 | [
-0.042508553713560104,
0.019257964566349983,
0.0323944091796875,
-0.040856000036001205,
0.007070554886013269,
-0.0121699757874012,
0.023600732907652855,
-0.04146896302700043,
-0.0019413841655477881,
-0.008278231136500835,
0.03922262042760849,
-0.030344324186444283,
-0.04195494204759598,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27236 | [
"Bug"
] | BisectingKmeans - intertia per cluster
### Describe the bug
Hi,
I have been using the sklearn package recently for some simple clustering. It appears to me that there is a typo in the BisectingKMeans class. In the `_inertia_per_cluster` method, we need to compute the inertia per-cluster. However, in the current ve... | 27,236 | [
0.00730755552649498,
-0.06912122666835785,
-0.01778113655745983,
0.03393999859690666,
0.02979315258562565,
0.009610461071133614,
0.056531570851802826,
0.015637854114174843,
0.02359337918460369,
-0.01253274641931057,
0.05117928609251976,
0.032095156610012054,
0.027394743636250496,
-0.046085... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27200 | [
"New Feature",
"Needs Decision",
"Needs Decision - Include Feature"
] | Implementation of Robust Random Cut Forest (RRCF) Algorithm
### Describe the workflow you want to enable
Enable users to perform robust anomaly detection using the Robust Random Cut Forest (RRCF) algorithm within the scikit-learn library.
### Describe your proposed solution
## Proposed Solution
I suggest t... | 27,200 | [
0.03507627174258232,
-0.023809049278497696,
0.019432704895734787,
0.015536810271441936,
-0.043289441615343094,
-0.0034669809974730015,
-0.02796720340847969,
0.013482781127095222,
-0.0060478271916508675,
-0.04055088758468628,
0.10239741951227188,
0.01693074405193329,
-0.05807159096002579,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27197 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/6007650858)** (Aug 29, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/6020290075) on Aug 30, 2023 | 27,197 | [
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0.03191758319735527,
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0.028803640976548195,
0.07911856472492218,
0.04164186492562294,
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0.0773... |
https://github.com/scikit-learn/scikit-learn/issues/27195 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58415&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Aug 29, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-... | 27,195 | [
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0.02659578248858452,
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0.035284481942653656,
0.043108902871608734,
0.007411926984786987,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/27195 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58415&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Aug 29, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-... | 27,195 | [
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0.007605879567563534,
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0.04292329400777817,
0.01899752765893936,
0.05164... |
https://github.com/scikit-learn/scikit-learn/issues/27193 | [
"Documentation"
] | Better documentation for `RFECV`
There is almost no description in the documentation of how `RFECV` actually works. The [user guide](https://scikit-learn.org/stable/modules/feature_selection.html#rfe) simply says
> [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklear... | 27,193 | [
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0.03973161801695824,
0.053405966609716415,
0.060496196150779724,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27193 | [
"Documentation"
] | Better documentation for `RFECV`
There is almost no description in the documentation of how `RFECV` actually works. The [user guide](https://scikit-learn.org/stable/modules/feature_selection.html#rfe) simply says
> [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklear... | 27,193 | [
0.03439446911215782,
-0.08880844712257385,
-0.016460899263620377,
-0.016528960317373276,
-0.041237495839595795,
0.04704507440328598,
0.0004923141095787287,
-0.03565923497080803,
-0.007791521959006786,
-0.009142395108938217,
0.03973161801695824,
0.053405966609716415,
0.060496196150779724,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27193 | [
"Documentation"
] | Better documentation for `RFECV`
There is almost no description in the documentation of how `RFECV` actually works. The [user guide](https://scikit-learn.org/stable/modules/feature_selection.html#rfe) simply says
> [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklear... | 27,193 | [
0.03439446911215782,
-0.08880844712257385,
-0.016460899263620377,
-0.016528960317373276,
-0.041237495839595795,
0.04704507440328598,
0.0004923141095787287,
-0.03565923497080803,
-0.007791521959006786,
-0.009142395108938217,
0.03973161801695824,
0.053405966609716415,
0.060496196150779724,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27193 | [
"Documentation"
] | Better documentation for `RFECV`
There is almost no description in the documentation of how `RFECV` actually works. The [user guide](https://scikit-learn.org/stable/modules/feature_selection.html#rfe) simply says
> [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklear... | 27,193 | [
0.03439446911215782,
-0.08880844712257385,
-0.016460899263620377,
-0.016528960317373276,
-0.041237495839595795,
0.04704507440328598,
0.0004923141095787287,
-0.03565923497080803,
-0.007791521959006786,
-0.009142395108938217,
0.03973161801695824,
0.053405966609716415,
0.060496196150779724,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27193 | [
"Documentation"
] | Better documentation for `RFECV`
There is almost no description in the documentation of how `RFECV` actually works. The [user guide](https://scikit-learn.org/stable/modules/feature_selection.html#rfe) simply says
> [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklear... | 27,193 | [
0.03439446911215782,
-0.08880844712257385,
-0.016460899263620377,
-0.016528960317373276,
-0.041237495839595795,
0.04704507440328598,
0.0004923141095787287,
-0.03565923497080803,
-0.007791521959006786,
-0.009142395108938217,
0.03973161801695824,
0.053405966609716415,
0.060496196150779724,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27192 | [
"New Feature",
"Needs Decision"
] | Update the Ledoit-Wolf covariance shrinkage methodology to include modern methods
### Describe the workflow you want to enable
I've been working on implementing partial correlation with basis shrinkage in Python. in particular, I've been porting R code to Python.
The relevant partial correlation publications a... | 27,192 | [
-0.010231568478047848,
0.06869911402463913,
0.007404872681945562,
-0.01199409831315279,
-0.022673621773719788,
-0.036372505128383636,
0.009920867159962654,
0.014491577632725239,
-0.03275316208600998,
0.025425512343645096,
-0.015447478741407394,
-0.014914263039827347,
0.01863233372569084,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27189 | [
"Bug"
] | F1 score not calculated properly
### Describe the bug
According to the [definition](https://en.wikipedia.org/wiki/F-score) of the F1 score for two classes, it can be calculated as
$$
2 \frac{2tp}{2tp + fp + fn}
$$
or
$$
2 \frac{precision * recall}{precision + recall}
$$
From what I can see, scikit... | 27,189 | [
-0.0013725621392950416,
-0.06702359765768051,
0.033048082143068314,
0.021475249901413918,
0.04198620095849037,
-0.022870931774377823,
-0.004658345133066177,
-0.011957594193518162,
-0.01934393122792244,
-0.04579493775963783,
0.02527390792965889,
-0.039638224989175797,
0.07824698835611343,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27186 | [
"Needs Investigation"
] | BUG (maybe) wrong node bound spread in KernelDensity
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/a5620f45614ac3f849c430f53146a66319e4908b/sklearn/neighbors/_binary_tree.pxi.tp#L2114-L2116
https://github.com/scikit-learn/scikit-learn/blob/a5620f45614ac3f849c430f53146a66319e4908b/sklearn... | 27,186 | [
0.01989292912185192,
-0.05678034573793411,
-0.0002898851817008108,
-0.015062338672578335,
-0.008150788024067879,
-0.05179411172866821,
0.01910592056810856,
-0.015018856152892113,
0.002310842741280794,
-0.04839335381984711,
0.024285560473799706,
-0.008381965570151806,
0.02302476204931736,
-... |
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