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/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 | [
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https://github.com/scikit-learn/scikit-learn/issues/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 | [
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0.003357582725584507,
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https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 | [
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0.09401... |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 | [
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0.09401... |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 | [
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0.02377360686659813,
0.02310515008866787,
0.09401... |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 | [
0.018939822912216187,
0.0009034881368279457,
0.007776120211929083,
-0.0254230834543705,
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0.013994384557008743,
0.06598011404275894,
0.02377360686659813,
0.02310515008866787,
0.09401... |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 | [
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https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 | [
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https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 | [
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https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 | [
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https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 | [
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https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
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-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
0.07316571474075317,
0.004305625334382057,
0.05377032235264778,
0.04740116000175476,
-0.0102904187515378,
-0.044132690876722336,
-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
0.07316571474075317,
0.004305625334382057,
0.05377032235264778,
0.04740116000175476,
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-0.044132690876722336,
-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
0.07316571474075317,
0.004305625334382057,
0.05377032235264778,
0.04740116000175476,
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-0.044132690876722336,
-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
0.07316571474075317,
0.004305625334382057,
0.05377032235264778,
0.04740116000175476,
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-0.044132690876722336,
-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 | [
-0.027279822155833244,
-0.024403303861618042,
0.03536687418818474,
-0.007271234877407551,
0.07316571474075317,
0.004305625334382057,
0.05377032235264778,
0.04740116000175476,
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-0.044132690876722336,
-0.009036904200911522,
0.015529898926615715,
0.04982668533921242,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
-0.012607271783053875,
0.05841612443327904,
0.033135462552309036,
-0.013083776459097862,
0.02602040208876133,
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0.011207936331629753,
0.038466159254312515,
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0.005942504853010178,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
0.018178747966885567,
0.09346307814121246,
0.015485397540032864,
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0.03361455723643303,
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0.014110183343291283,
0.017850279808044434,
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0.021481221541762352,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
0.002361477818340063,
0.0536537729203701,
0.025570297613739967,
-0.0015947314677760005,
0.03014577180147171,
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0.02880442515015602,
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0.025549229234457016,
0.004672614391893148,
0.018377549946308136,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
0.006552243139594793,
0.04866645485162735,
0.020232204347848892,
0.011793171986937523,
0.07619647681713104,
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0.06485943496227264,
0.01728900521993637,
0.012124542146921158,
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0.001691159326583147,
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0.00005416537533164956,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
-0.02023300901055336,
0.040255509316921234,
0.018657658249139786,
0.012632466852664948,
0.03835197910666466,
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0.033204179257154465,
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0.017744677141308784,
0.031813401728868484,
0.0057243723422288895,
0.0076917377300560474,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
-0.022914152592420578,
0.047162286937236786,
0.012146570719778538,
0.010397438891232014,
0.027102287858724594,
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0.018050681799650192,
0.030685169622302055,
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0.012700359337031841,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
-0.019533991813659668,
0.04893874749541283,
0.006333404686301947,
0.003682512789964676,
0.019995473325252533,
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0.049345873296260834,
0.03870098292827606,
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0.014271710999310017,
0.03871292248368263,
-0.008059249259531498,
0.006978217978030443,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 | [
-0.0027283551171422005,
0.09752656519412994,
0.014859721064567566,
-0.01879505254328251,
0.009693576022982597,
0.003525934647768736,
0.02271847426891327,
0.02327539585530758,
0.019295647740364075,
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0.017268316820263863,
-0.005370823200792074,
0.01375676691532135,
0.10... |
https://github.com/scikit-learn/scikit-learn/issues/25550 | [
"Bug",
"module:preprocessing"
] | OneHotEncoder `drop_idx_` attribute description in presence of infrequent categories
### Describe the issue linked to the documentation
### Issue summary
In the OneHotEncoder documentation both for [v1.2](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preproces... | 25,550 | [
0.007494242396205664,
0.051115550100803375,
-0.01094179879873991,
-0.0022414212580770254,
0.027770468965172768,
0.048514749854803085,
0.03967934846878052,
0.035409968346357346,
-0.041271813213825226,
-0.01932131126523018,
0.05138679966330528,
0.04686221852898598,
0.01617109589278698,
0.032... |
https://github.com/scikit-learn/scikit-learn/issues/25550 | [
"Bug",
"module:preprocessing"
] | OneHotEncoder `drop_idx_` attribute description in presence of infrequent categories
### Describe the issue linked to the documentation
### Issue summary
In the OneHotEncoder documentation both for [v1.2](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preproces... | 25,550 | [
0.007494242396205664,
0.051115550100803375,
-0.01094179879873991,
-0.0022414212580770254,
0.027770468965172768,
0.048514749854803085,
0.03967934846878052,
0.035409968346357346,
-0.041271813213825226,
-0.01932131126523018,
0.05138679966330528,
0.04686221852898598,
0.01617109589278698,
0.032... |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 | [
-0.020890701562166214,
-0.07227298617362976,
-0.022346362471580505,
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0.010777994059026241,
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0.04564749449491501,
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0.005899777170270681,
0.09224024415016174,
0.06811006367206573,
0.007438197731971741,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 | [
-0.02163936011493206,
-0.07978251576423645,
-0.01950650103390217,
-0.02924191951751709,
0.012323586270213127,
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0.04484407231211662,
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0.002385380445048213,
0.01009117066860199,
0.09711118042469025,
0.07332911342382431,
0.004828073084354401,
0.0231... |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 | [
-0.01674579083919525,
-0.07703939825296402,
-0.020602615550160408,
-0.02822551317512989,
0.010417912155389786,
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0.04482528939843178,
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0.008462400175631046,
0.09031481295824051,
0.07213055342435837,
0.007535260170698166,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25534 | [
"Bug",
"Needs Triage"
] | `_check_unknown` returns error for `np.isnan(known_values)` with int64 arrays
### Describe the bug
When `precision_score` is called with two numpy int64 arrays as y_true and y_pred, an error is thrown in the `_check_unknown` function in [sklearn](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn)/[utils]... | 25,534 | [
-0.007356140296906233,
-0.016253992915153503,
0.029346182942390442,
-0.010240251198410988,
0.0878627821803093,
-0.005060948897153139,
0.032907649874687195,
0.007458082865923643,
-0.007710592355579138,
-0.020351307466626167,
-0.0075044226832687855,
0.001970436656847596,
0.009356401860713959,
... |
https://github.com/scikit-learn/scikit-learn/issues/25534 | [
"Bug",
"Needs Triage"
] | `_check_unknown` returns error for `np.isnan(known_values)` with int64 arrays
### Describe the bug
When `precision_score` is called with two numpy int64 arrays as y_true and y_pred, an error is thrown in the `_check_unknown` function in [sklearn](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn)/[utils]... | 25,534 | [
-0.007356140296906233,
-0.016253992915153503,
0.029346182942390442,
-0.010240251198410988,
0.0878627821803093,
-0.005060948897153139,
0.032907649874687195,
0.007458082865923643,
-0.007710592355579138,
-0.020351307466626167,
-0.0075044226832687855,
0.001970436656847596,
0.009356401860713959,
... |
https://github.com/scikit-learn/scikit-learn/issues/25533 | [
"Bug",
"Needs Triage"
] | Error while installing DeepLabCut: Collecting scikit-learn>=1.0
### Describe the bug
I'm trying to install DeeplLabCut and was encountering an error.
The devs guided me over here to as it seems to be an error while installing scikit-learn.
Issue for reference: https://github.com/DeepLabCut/DeepLabCut/issues/2139
... | 25,533 | [
0.014659682288765907,
0.003894265741109848,
-0.0038599511608481407,
-0.007155978586524725,
0.016378415748476982,
-0.0053560249507427216,
-0.013276775367558002,
0.019545776769518852,
-0.021260008215904236,
0.027882792055606842,
-0.00028361359727568924,
0.017073217779397964,
0.0078359693288803... |
https://github.com/scikit-learn/scikit-learn/issues/25533 | [
"Bug",
"Needs Triage"
] | Error while installing DeepLabCut: Collecting scikit-learn>=1.0
### Describe the bug
I'm trying to install DeeplLabCut and was encountering an error.
The devs guided me over here to as it seems to be an error while installing scikit-learn.
Issue for reference: https://github.com/DeepLabCut/DeepLabCut/issues/2139
... | 25,533 | [
0.014659682288765907,
0.003894265741109848,
-0.0038599511608481407,
-0.007155978586524725,
0.016378415748476982,
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-0.013276775367558002,
0.019545776769518852,
-0.021260008215904236,
0.027882792055606842,
-0.00028361359727568924,
0.017073217779397964,
0.0078359693288803... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 | [
-0.003321858588606119,
0.03584377467632294,
0.015125168487429619,
-0.007296181749552488,
0.00979015976190567,
-0.011586233042180538,
0.07812494039535522,
0.027399184182286263,
0.03862765431404114,
-0.04474275931715965,
-0.0032712307292968035,
0.0242366511374712,
0.014588386751711369,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25529 | [
"New Feature",
"Needs Triage"
] | quantum kernel with scikit -learn
### Describe the workflow you want to enable
I have designed a quantum kernel function with Pennylane quantum simulator. When i want to use Gaussian process for classification in combination with the quantum kernel i encountered this problem:
```py
AttributeError: 'function' o... | 25,529 | [
-0.0003682982351165265,
0.016130726784467697,
0.007272007409483194,
0.0038196267560124397,
0.047122783958911896,
-0.020959947258234024,
0.02435097098350525,
-0.007233594078570604,
-0.012042677029967308,
0.0051947529427707195,
0.019397621974349022,
0.06800569593906403,
0.020377030596137047,
... |
https://github.com/scikit-learn/scikit-learn/issues/25529 | [
"New Feature",
"Needs Triage"
] | quantum kernel with scikit -learn
### Describe the workflow you want to enable
I have designed a quantum kernel function with Pennylane quantum simulator. When i want to use Gaussian process for classification in combination with the quantum kernel i encountered this problem:
```py
AttributeError: 'function' o... | 25,529 | [
-0.0003682982351165265,
0.016130726784467697,
0.007272007409483194,
0.0038196267560124397,
0.047122783958911896,
-0.020959947258234024,
0.02435097098350525,
-0.007233594078570604,
-0.012042677029967308,
0.0051947529427707195,
0.019397621974349022,
0.06800569593906403,
0.020377030596137047,
... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 | [
-0.014912660233676434,
-0.07150337845087051,
0.0031560591887682676,
0.012017653323709965,
0.07665127515792847,
-0.029321201145648956,
0.020797090604901314,
0.0003468962968327105,
0.02956637553870678,
0.010984743013978004,
0.04219542071223259,
0.06718599796295166,
0.004095142241567373,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 | [
0.005942752584815025,
0.011723518371582031,
0.0048584118485450745,
-0.06877551227807999,
0.03998938575387001,
0.03337418660521507,
0.034094229340553284,
0.016431689262390137,
0.030701173469424248,
0.03801920264959335,
0.054108649492263794,
-0.0015506476629525423,
0.0033580847084522247,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25522 | [
"RFC"
] | Behaviour of `warm_start=True` and `max_iter` (and `n_estimators`)
This issue is an RFC to clarify the expected behavior `max_iter` and `n_iter_` (or `estimators` and `len(estimators_)` equivalently) when used with `warm_start=True`.
### Estimators to be considered
The estimators to be considered can be found in... | 25,522 | [
0.006278230808675289,
-0.04672724008560181,
0.04980166628956795,
0.000966462423093617,
0.03553122654557228,
0.022964296862483025,
0.04589354246854782,
-0.0018894884269684553,
0.017195502296090126,
-0.038842231035232544,
0.025940125808119774,
0.01072633545845747,
0.041667450219392776,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/25522 | [
"RFC"
] | Behaviour of `warm_start=True` and `max_iter` (and `n_estimators`)
This issue is an RFC to clarify the expected behavior `max_iter` and `n_iter_` (or `estimators` and `len(estimators_)` equivalently) when used with `warm_start=True`.
### Estimators to be considered
The estimators to be considered can be found in... | 25,522 | [
0.006278230808675289,
-0.04672724008560181,
0.04980166628956795,
0.000966462423093617,
0.03553122654557228,
0.022964296862483025,
0.04589354246854782,
-0.0018894884269684553,
0.017195502296090126,
-0.038842231035232544,
0.025940125808119774,
0.01072633545845747,
0.041667450219392776,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 | [
-0.015333740040659904,
0.020274627953767776,
0.0395301878452301,
-0.0037951243575662374,
0.054616108536720276,
0.022654959931969643,
0.019129114225506783,
-0.012973316013813019,
0.016966670751571655,
0.015597512945532799,
0.005317413713783026,
-0.013636504299938679,
0.037770915776491165,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 | [
-0.015333740040659904,
0.020274627953767776,
0.0395301878452301,
-0.0037951243575662374,
0.054616108536720276,
0.022654959931969643,
0.019129114225506783,
-0.012973316013813019,
0.016966670751571655,
0.015597512945532799,
0.005317413713783026,
-0.013636504299938679,
0.037770915776491165,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 | [
-0.015333740040659904,
0.020274627953767776,
0.0395301878452301,
-0.0037951243575662374,
0.054616108536720276,
0.022654959931969643,
0.019129114225506783,
-0.012973316013813019,
0.016966670751571655,
0.015597512945532799,
0.005317413713783026,
-0.013636504299938679,
0.037770915776491165,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 | [
-0.015333740040659904,
0.020274627953767776,
0.0395301878452301,
-0.0037951243575662374,
0.054616108536720276,
0.022654959931969643,
0.019129114225506783,
-0.012973316013813019,
0.016966670751571655,
0.015597512945532799,
0.005317413713783026,
-0.013636504299938679,
0.037770915776491165,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 | [
-0.015333740040659904,
0.020274627953767776,
0.0395301878452301,
-0.0037951243575662374,
0.054616108536720276,
0.022654959931969643,
0.019129114225506783,
-0.012973316013813019,
0.016966670751571655,
0.015597512945532799,
0.005317413713783026,
-0.013636504299938679,
0.037770915776491165,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 | [
0.023928239941596985,
-0.060638345777988434,
-0.011212710291147232,
0.026663554832339287,
0.058239974081516266,
-0.02872801013290882,
0.0313744954764843,
0.029769249260425568,
0.006470620632171631,
-0.015024994499981403,
0.052052561193704605,
0.05534844845533371,
-0.0049301632679998875,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 | [
0.007746492046862841,
0.030289368703961372,
0.027969488874077797,
-0.008694635704159737,
0.09166623651981354,
0.008732072077691555,
0.048349156975746155,
0.04725996032357216,
-0.029551193118095398,
-0.023716770112514496,
0.006162474397569895,
0.05176450312137604,
0.004501441027969122,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25497 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073)** (Jan 27, 2023)
COMMENT:
It looks like the failure was spurious. I reran the failing job. Let's see. | 25,497 | [
-0.045188505202531815,
0.02437913976609707,
-0.00734325684607029,
-0.0196752417832613,
0.01571657881140709,
0.015745200216770172,
0.019532496109604836,
0.022868448868393898,
-0.05507173016667366,
0.025659950450062752,
0.08267533034086227,
0.039212800562381744,
-0.013610812835395336,
0.0501... |
https://github.com/scikit-learn/scikit-learn/issues/25497 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073)** (Jan 27, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073) on Jan 27, 2023 | 25,497 | [
-0.040171220898628235,
0.03354118764400482,
-0.02275877073407173,
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0.012051425874233246,
0.013548360206186771,
0.01604325883090496,
0.041042279452085495,
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0.02789877913892269,
0.0774511843919754,
0.04064244404435158,
-0.01347645279020071,
0.07328... |
https://github.com/scikit-learn/scikit-learn/issues/25496 | [
"Bug",
"Needs Triage"
] | Partial Dependence Plot orients differently compared to Partial Dependence values
### Describe the bug
The issue is that the 2D partial dependence plot from scikit-learn orients in a different way that what you would get using raw pdp values from sklearn as well.
### Steps/Code to Reproduce
```python
imp... | 25,496 | [
0.004328357521444559,
0.007868646644055843,
0.04219180718064308,
0.027495497837662697,
0.010325800627470016,
-0.0036062849685549736,
0.005246995948255062,
0.006359612103551626,
0.008514394983649254,
0.013431552797555923,
-0.014015460386872292,
0.011704430915415287,
0.02347571589052677,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25496 | [
"Bug",
"Needs Triage"
] | Partial Dependence Plot orients differently compared to Partial Dependence values
### Describe the bug
The issue is that the 2D partial dependence plot from scikit-learn orients in a different way that what you would get using raw pdp values from sklearn as well.
### Steps/Code to Reproduce
```python
imp... | 25,496 | [
0.004328357521444559,
0.007868646644055843,
0.04219180718064308,
0.027495497837662697,
0.010325800627470016,
-0.0036062849685549736,
0.005246995948255062,
0.006359612103551626,
0.008514394983649254,
0.013431552797555923,
-0.014015460386872292,
0.011704430915415287,
0.02347571589052677,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 | [
0.008895357139408588,
-0.06279284507036209,
0.005090250633656979,
-0.03653084486722946,
0.04301321133971214,
-0.0070788199082016945,
0.004494709428399801,
0.006441106554120779,
-0.05743463337421417,
-0.000961107958573848,
0.013912299647927284,
-0.0035960539244115353,
0.0749364122748375,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 | [
0.008895357139408588,
-0.06279284507036209,
0.005090250633656979,
-0.03653084486722946,
0.04301321133971214,
-0.0070788199082016945,
0.004494709428399801,
0.006441106554120779,
-0.05743463337421417,
-0.000961107958573848,
0.013912299647927284,
-0.0035960539244115353,
0.0749364122748375,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 | [
0.008895357139408588,
-0.06279284507036209,
0.005090250633656979,
-0.03653084486722946,
0.04301321133971214,
-0.0070788199082016945,
0.004494709428399801,
0.006441106554120779,
-0.05743463337421417,
-0.000961107958573848,
0.013912299647927284,
-0.0035960539244115353,
0.0749364122748375,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 | [
0.006762305740267038,
0.09134646505117416,
0.008567025884985924,
-0.049394842237234116,
0.05360466614365578,
0.013694540597498417,
0.09898069500923157,
-0.002881724154576659,
0.03567340970039368,
-0.013141285628080368,
0.0036144410260021687,
0.05509554222226143,
0.04218967631459236,
0.0408... |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 | [
0.006762305740267038,
0.09134646505117416,
0.008567025884985924,
-0.049394842237234116,
0.05360466614365578,
0.013694540597498417,
0.09898069500923157,
-0.002881724154576659,
0.03567340970039368,
-0.013141285628080368,
0.0036144410260021687,
0.05509554222226143,
0.04218967631459236,
0.0408... |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 | [
0.006762305740267038,
0.09134646505117416,
0.008567025884985924,
-0.049394842237234116,
0.05360466614365578,
0.013694540597498417,
0.09898069500923157,
-0.002881724154576659,
0.03567340970039368,
-0.013141285628080368,
0.0036144410260021687,
0.05509554222226143,
0.04218967631459236,
0.0408... |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 | [
0.006762305740267038,
0.09134646505117416,
0.008567025884985924,
-0.049394842237234116,
0.05360466614365578,
0.013694540597498417,
0.09898069500923157,
-0.002881724154576659,
0.03567340970039368,
-0.013141285628080368,
0.0036144410260021687,
0.05509554222226143,
0.04218967631459236,
0.0408... |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 | [
0.006762305740267038,
0.09134646505117416,
0.008567025884985924,
-0.049394842237234116,
0.05360466614365578,
0.013694540597498417,
0.09898069500923157,
-0.002881724154576659,
0.03567340970039368,
-0.013141285628080368,
0.0036144410260021687,
0.05509554222226143,
0.04218967631459236,
0.0408... |
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