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/24686 | [
"New Feature",
"module:cluster"
] | Path to HDBSCAN Inclusion
## Introduction
The HDBSCAN estimator implementation from [`scikit-learn-contrib/hdbscan`](https://github.com/scikit-learn-contrib/hdbscan) has been adopted, modified and refactored to conform to scikit-learn API and is now merged into the [`hdbscan`](https://github.com/scikit-learn/scikit-l... | 24,686 | [
-0.05293574929237366,
0.017985418438911438,
0.006291832774877548,
-0.04690643772482872,
-0.04184217005968094,
-0.0010622190311551094,
0.047855496406555176,
0.018633799627423286,
0.025177814066410065,
0.013751072809100151,
0.040152981877326965,
-0.02064886875450611,
0.020939737558364868,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24686 | [
"New Feature",
"module:cluster"
] | Path to HDBSCAN Inclusion
## Introduction
The HDBSCAN estimator implementation from [`scikit-learn-contrib/hdbscan`](https://github.com/scikit-learn-contrib/hdbscan) has been adopted, modified and refactored to conform to scikit-learn API and is now merged into the [`hdbscan`](https://github.com/scikit-learn/scikit-l... | 24,686 | [
-0.05293574929237366,
0.017985418438911438,
0.006291832774877548,
-0.04690643772482872,
-0.04184217005968094,
-0.0010622190311551094,
0.047855496406555176,
0.018633799627423286,
0.025177814066410065,
0.013751072809100151,
0.040152981877326965,
-0.02064886875450611,
0.020939737558364868,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24686 | [
"New Feature",
"module:cluster"
] | Path to HDBSCAN Inclusion
## Introduction
The HDBSCAN estimator implementation from [`scikit-learn-contrib/hdbscan`](https://github.com/scikit-learn-contrib/hdbscan) has been adopted, modified and refactored to conform to scikit-learn API and is now merged into the [`hdbscan`](https://github.com/scikit-learn/scikit-l... | 24,686 | [
-0.05293574929237366,
0.017985418438911438,
0.006291832774877548,
-0.04690643772482872,
-0.04184217005968094,
-0.0010622190311551094,
0.047855496406555176,
0.018633799627423286,
0.025177814066410065,
0.013751072809100151,
0.040152981877326965,
-0.02064886875450611,
0.020939737558364868,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24686 | [
"New Feature",
"module:cluster"
] | Path to HDBSCAN Inclusion
## Introduction
The HDBSCAN estimator implementation from [`scikit-learn-contrib/hdbscan`](https://github.com/scikit-learn-contrib/hdbscan) has been adopted, modified and refactored to conform to scikit-learn API and is now merged into the [`hdbscan`](https://github.com/scikit-learn/scikit-l... | 24,686 | [
-0.05293574929237366,
0.017985418438911438,
0.006291832774877548,
-0.04690643772482872,
-0.04184217005968094,
-0.0010622190311551094,
0.047855496406555176,
0.018633799627423286,
0.025177814066410065,
0.013751072809100151,
0.040152981877326965,
-0.02064886875450611,
0.020939737558364868,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.032940641045570374,
0.021135015413165092,
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0.013269309885799885,
0.035159632563591,
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0.012631162069737911,
0.03552818298339844,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.03485478088259697,
0.027255671098828316,
-0.005735383369028568,
0.012688454240560532,
0.0365169495344162,
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0.023318275809288025,
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0.03283850476145744,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.03530413657426834,
0.015658501535654068,
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0.014643313363194466,
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0.03999948501586914,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.02803340181708336,
0.025342345237731934,
-0.006040383130311966,
-0.0054071517661213875,
0.01975424587726593,
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0.03275011107325554,
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0.003250898327678442,
0.012333515100181103,
-0.005439438857138157,
0.059082549065351486,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.03431866317987442,
0.026419881731271744,
-0.0014762812061235309,
0.012355600483715534,
0.037722788751125336,
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0.023570958524942398,
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-0.00893868412822485,
-0.0035040995571762323,
0.01083064079284668,
0.03533858060836792,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24679 | [
"help wanted"
] | correct SAGA weight update with prox
It seems that saga weights are being updated here with saga bit after the prox operator has been applied to SAG update
https://github.com/scikit-learn/scikit-learn/blob/8610e14f8a9acd488253444b8e551fb3e0d60ef7/sklearn/linear_model/_sag_fast.pyx.tp#L430
Should the prox operator ... | 24,679 | [
0.033721569925546646,
0.027772491797804832,
-0.004503175616264343,
0.015561525709927082,
0.035434190183877945,
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0.022774314507842064,
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-0.011257292702794075,
0.004180574323982,
0.014234762638807297,
0.034412868320941925,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
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0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24675 | [
"Documentation",
"Needs Decision"
] | suggestion to move (copy?) binder button for example to top or right side bar
### Describe the issue linked to the documentation
Firstly, thanks for having binder links for examples!
In the examples such as https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr... | 24,675 | [
0.0040909782983362675,
-0.026449013501405716,
0.0036184131167829037,
0.03195563703775406,
-0.04920542985200882,
0.0067606596276164055,
0.08342809230089188,
0.05321400240063667,
0.030638987198472023,
-0.02919198013842106,
-0.03348802775144577,
0.04877510294318199,
-0.043230511248111725,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24669 | [
"Bug"
] | Missing DLL with SciPy 1.9.2
### Describe the bug
scikit-learn crashes /w current scipy==1.9.2 on Win (AMD64). This combination causes fail of joblib subprocess using scikit-learn. (Tested in Windows containers / docker images python:3.9.13 AND python:3.9.10 .)
Not replicated on Linux and MacOS even with the same ... | 24,669 | [
-0.01142089907079935,
0.02514353208243847,
0.00003929562444682233,
-0.03273677080869675,
0.026285476982593536,
0.018069133162498474,
0.05044142156839371,
0.0076407636515796185,
0.06624341756105423,
-0.014867163263261318,
0.0496753454208374,
0.09523823112249374,
-0.0063148257322609425,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24663 | [
"Bug",
"Needs Triage"
] | Decision function for kernel SVM not reproducible.
### Describe the bug
I want to calculate the decision function through the trained svm model, but the decision function I implement based on the [mathematical expression](https://scikit-learn.org/stable/modules/svm.html#svm-mathematical-formulation) of the SVM is n... | 24,663 | [
0.01110905036330223,
-0.04524705559015274,
0.008016122505068779,
0.027167724445462227,
0.07237596809864044,
-0.04655773192644119,
-0.013201802037656307,
0.015472900122404099,
-0.017374513670802116,
0.033891718834638596,
0.03141947090625763,
0.09940823167562485,
0.08248480409383774,
0.02688... |
https://github.com/scikit-learn/scikit-learn/issues/24663 | [
"Bug",
"Needs Triage"
] | Decision function for kernel SVM not reproducible.
### Describe the bug
I want to calculate the decision function through the trained svm model, but the decision function I implement based on the [mathematical expression](https://scikit-learn.org/stable/modules/svm.html#svm-mathematical-formulation) of the SVM is n... | 24,663 | [
0.01110905036330223,
-0.04524705559015274,
0.008016122505068779,
0.027167724445462227,
0.07237596809864044,
-0.04655773192644119,
-0.013201802037656307,
0.015472900122404099,
-0.017374513670802116,
0.033891718834638596,
0.03141947090625763,
0.09940823167562485,
0.08248480409383774,
0.02688... |
https://github.com/scikit-learn/scikit-learn/issues/24662 | [
"New Feature",
"Needs Triage"
] | Can I show the chart in the browser ?
### Describe the workflow you want to enable
<img width="350" alt="image" src="https://user-images.githubusercontent.com/14190605/195821119-5117dd2e-cc12-4024-8f9b-e87f49cd34f8.png">
Hi! Can I show the chart in the browser ? Thanks
### Describe your proposed solution
LOL
##... | 24,662 | [
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0.002308035735040903,
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0.015177976340055466,
0.0648278146982193,
0.017468011006712914,
0.06863340735435486,
0.01157001219689846,
0.0012798119569197297,
0.08329963684082031,
-0.03060377575457096,
0.1357... |
https://github.com/scikit-learn/scikit-learn/issues/24659 | [
"Bug",
"module:utils",
"module:test-suite"
] | check_classifiers_train fails when tag requires_positive_X=True
### Describe the bug
The check `check_classifiers_train` fails for classifiers that need positive X, even when the tag `requires_positive_X` is set to `True`.
In the example below, I copy/paste the template from [skltemplate](https://github.com/scikit... | 24,659 | [
-0.036208078265190125,
-0.057588059455156326,
0.01943770982325077,
-0.030104387551546097,
0.061163440346717834,
0.019086511805653572,
0.000013764287359663285,
0.02891669049859047,
0.0461294986307621,
-0.0162804052233696,
0.021047037094831467,
0.055262453854084015,
0.016065334901213646,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24659 | [
"Bug",
"module:utils",
"module:test-suite"
] | check_classifiers_train fails when tag requires_positive_X=True
### Describe the bug
The check `check_classifiers_train` fails for classifiers that need positive X, even when the tag `requires_positive_X` is set to `True`.
In the example below, I copy/paste the template from [skltemplate](https://github.com/scikit... | 24,659 | [
-0.036208078265190125,
-0.057588059455156326,
0.01943770982325077,
-0.030104387551546097,
0.061163440346717834,
0.019086511805653572,
0.000013764287359663285,
0.02891669049859047,
0.0461294986307621,
-0.0162804052233696,
0.021047037094831467,
0.055262453854084015,
0.016065334901213646,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24659 | [
"Bug",
"module:utils",
"module:test-suite"
] | check_classifiers_train fails when tag requires_positive_X=True
### Describe the bug
The check `check_classifiers_train` fails for classifiers that need positive X, even when the tag `requires_positive_X` is set to `True`.
In the example below, I copy/paste the template from [skltemplate](https://github.com/scikit... | 24,659 | [
-0.036208078265190125,
-0.057588059455156326,
0.01943770982325077,
-0.030104387551546097,
0.061163440346717834,
0.019086511805653572,
0.000013764287359663285,
0.02891669049859047,
0.0461294986307621,
-0.0162804052233696,
0.021047037094831467,
0.055262453854084015,
0.016065334901213646,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24658 | [
"New Feature",
"Needs Triage"
] | Update min joblib to 1.2.0
### Describe the workflow you want to enable
As of 3 days ago, Joblib 1.2.0 has [significant improvements](https://github.com/joblib/joblib/blob/master/CHANGES.rst) over the Joblib 1.1.1 that sklearn currently supports
I suggest that sklearn should increase joblib version in required dep... | 24,658 | [
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0.10454132407903671,
0.006866403855383396,
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0.0052022188901901245,
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0.06306130439043045,
0.03643413260579109,
0.032798051834106445,
0.0008988980553112924,
0.05930425971746445,
0.0822162851691246,
-0.0685521811246872,
0.0666... |
https://github.com/scikit-learn/scikit-learn/issues/24656 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Improving stratification in StratifiedGroupKFold
### Describe the workflow you want to enable
I think I found two types of issues in StratifiedGroupKFold. First, StratifiedGroupKFold sometimes doesn't give ideally stratified splits when shuffle=True. Second, I think I found a general bug where the code just isn't doi... | 24,656 | [
-0.04424411803483963,
0.02408827841281891,
0.0017194916727021337,
0.03259905055165291,
0.013522387482225895,
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0.07954593747854233,
0.028949366882443428,
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-0.04804489016532898,
0.00826356466859579,
-0.007271775975823402,
-0.0029906455893069506,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24656 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Improving stratification in StratifiedGroupKFold
### Describe the workflow you want to enable
I think I found two types of issues in StratifiedGroupKFold. First, StratifiedGroupKFold sometimes doesn't give ideally stratified splits when shuffle=True. Second, I think I found a general bug where the code just isn't doi... | 24,656 | [
-0.04424411803483963,
0.02408827841281891,
0.0017194916727021337,
0.03259905055165291,
0.013522387482225895,
-0.05173657462000847,
0.07954593747854233,
0.028949366882443428,
-0.005772935692220926,
-0.04804489016532898,
0.00826356466859579,
-0.007271775975823402,
-0.0029906455893069506,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24656 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Improving stratification in StratifiedGroupKFold
### Describe the workflow you want to enable
I think I found two types of issues in StratifiedGroupKFold. First, StratifiedGroupKFold sometimes doesn't give ideally stratified splits when shuffle=True. Second, I think I found a general bug where the code just isn't doi... | 24,656 | [
-0.04424411803483963,
0.02408827841281891,
0.0017194916727021337,
0.03259905055165291,
0.013522387482225895,
-0.05173657462000847,
0.07954593747854233,
0.028949366882443428,
-0.005772935692220926,
-0.04804489016532898,
0.00826356466859579,
-0.007271775975823402,
-0.0029906455893069506,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24656 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Improving stratification in StratifiedGroupKFold
### Describe the workflow you want to enable
I think I found two types of issues in StratifiedGroupKFold. First, StratifiedGroupKFold sometimes doesn't give ideally stratified splits when shuffle=True. Second, I think I found a general bug where the code just isn't doi... | 24,656 | [
-0.04424411803483963,
0.02408827841281891,
0.0017194916727021337,
0.03259905055165291,
0.013522387482225895,
-0.05173657462000847,
0.07954593747854233,
0.028949366882443428,
-0.005772935692220926,
-0.04804489016532898,
0.00826356466859579,
-0.007271775975823402,
-0.0029906455893069506,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.02852340042591095,
0.005726539995521307,
-0.019879985600709915,
-0.027365075424313545,
0.038525406271219254,
-0.034802693873643875,
-0.008357401005923748,
-0.01564415544271469,
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-0.008608183823525906,
0.10285269469022751,
0.007329791318625212,
-0.03875371813774109,
... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03725587576627731,
-0.0018578741000965238,
-0.004069387447088957,
-0.01200465764850378,
0.036651235073804855,
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-0.02287083864212036,
0.005834508221596479,
0.00882533099502325,
-0.003700800472870469,
0.10234145820140839,
-0.003996837418526411,
-0.040632378309965134,
... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.04690735042095184,
0.00899521540850401,
-0.008321153931319714,
-0.0022787333000451326,
0.04039284586906433,
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-0.0072859711945056915,
0.00698896124958992,
0.01413235254585743,
0.0005655259010381997,
0.10329791903495789,
-0.00510551268234849,
-0.054114848375320435,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.037990178912878036,
-0.0037849037908017635,
-0.003963070921599865,
-0.015414849855005741,
0.041444841772317886,
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0.0033919934649020433,
-0.0007771234959363937,
0.10199818760156631,
-0.0003890021180268377,
-0.0358096323907... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.04402630403637886,
0.00656637828797102,
-0.018796097487211227,
-0.019458314403891563,
0.042758818715810776,
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0.02134082280099392,
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0.023388851433992386,
0.08072441071271896,
0.015986546874046326,
-0.02737007848918438,
0.12... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03787175565958023,
-0.007700452581048012,
-0.007335585076361895,
-0.01373036578297615,
0.044879790395498276,
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0.000606870511546731,
0.00584869971498847,
0.003752094926312566,
0.09379655867815018,
0.004253442864865065,
-0.03626939654350281,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03969421982765198,
0.0017365686362609267,
-0.010610274039208889,
-0.007345081306993961,
0.029410231858491898,
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0.005626273341476917,
0.007539431564509869,
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0.10560089349746704,
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-0.03990282490849495,... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.04267504811286926,
0.005081544630229473,
-0.006996395066380501,
-0.012347242794930935,
0.04009895399212837,
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0.004687172826379538,
0.09379054605960846,
0.006952080875635147,
-0.039735060185194016,
... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03607148677110672,
-0.008061987347900867,
-0.006571251433342695,
-0.01881839893758297,
0.038878433406353,
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0.10553370416164398,
0.0009698939393274486,
-0.038681406527757645,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.032565273344516754,
-0.008165236562490463,
-0.005762514192610979,
-0.01728646270930767,
0.04493700712919235,
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0.0076002394780516624,
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0.10726278275251389,
0.001543740974739194,
-0.03937981277704239,... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03694218024611473,
-0.0031535951420664787,
-0.004420148674398661,
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0.03762589767575264,
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0.11104849725961685,
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0.1... |
https://github.com/scikit-learn/scikit-learn/issues/24651 | [
"Enhancement",
"module:svm"
] | Default value for solving an SVM in primal and dual should be determined automatically
### Describe the workflow you want to enable
Currently the default value for LinearSVC and LinearSVR with regards to solving the problem in dual is True. This means that for users that are unfamiliar with support-vector machines ... | 24,651 | [
-0.03891764208674431,
-0.008224393241107464,
-0.013034570962190628,
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0.04102964699268341,
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0.0020586003083735704,
-0.0025549510028213263,
0.10196540504693985,
0.006887675728648901,
-0.03992755338549614,
... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.03132864460349083,
0.030971331521868706,
-0.008410937152802944,
-0.05729173123836517,
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0.013074264861643314,
0.06367781013250351,
-0.012798655778169632,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.030405933037400246,
0.033117618411779404,
-0.010709942318499088,
-0.05683789402246475,
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0.05945888161659241,
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0.026902394369244576,
0.0161061380058527,
0.06764938682317734,
-0.018497148528695107,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.029989317059516907,
0.017691168934106827,
-0.003877809038385749,
-0.05146036669611931,
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0.03224242851138115,
0.023632405325770378,
0.061115413904190063,
-0.02058645896613598,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.031241057440638542,
0.035085082054138184,
-0.01007838360965252,
-0.05371769517660141,
-0.014930345118045807,
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0.05354025587439537,
0.0018800899852067232,
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0.030962128192186356,
0.018148399889469147,
0.0693780854344368,
-0.01860872283577919,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.03269311413168907,
0.03176174685359001,
-0.008351798169314861,
-0.054659631103277206,
-0.01191196870058775,
0.0048887706361711025,
0.05713387951254845,
0.0027654569130390882,
0.050742946565151215,
0.03130096569657326,
0.011239690706133842,
0.06504116207361221,
-0.01755673624575138,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.02558623068034649,
0.06107955798506737,
-0.0056656175293028355,
-0.0716453343629837,
-0.019496668130159378,
0.010367302224040031,
0.04383532702922821,
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0.02769906260073185,
0.03318606689572334,
0.02196357771754265,
0.048253536224365234,
-0.007161705754697323,
0.056... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.016862928867340088,
0.05588101968169212,
0.0009295987547375262,
-0.05080679431557655,
-0.004216142930090427,
0.0013330886140465736,
0.05434831976890564,
-0.0029254681430757046,
0.05054251849651337,
0.030736451968550682,
0.010264385491609573,
0.06260818988084793,
-0.02351844310760498,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.021673789247870445,
0.039868392050266266,
-0.013621291145682335,
-0.042078375816345215,
-0.0031612117309123278,
0.014657589606940746,
0.036675989627838135,
-0.00799779687076807,
0.03150471672415733,
0.04119020700454712,
0.014562499709427357,
0.04950864985585213,
-0.006318306550383568,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24642 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | New algorithm: DP-Means
### Describe the workflow you want to enable
DP-Means is a nonparametric version of K-Means, which is rooted in the Dirichlet Process Mixture Model.
Proposed by[ Kulis and Jordan in 2011](https://arxiv.org/pdf/1111.0352.pdf), it is already a well cited and established algorithm.
Recently, ... | 24,642 | [
-0.03689155727624893,
0.03880039602518082,
-0.01049517560750246,
-0.05518725514411926,
-0.010419965721666813,
0.003634826745837927,
0.05374378338456154,
-0.003688233206048608,
0.03698359429836273,
0.035788826644420624,
0.019416093826293945,
0.06281678378582001,
-0.012852511368691921,
0.058... |
https://github.com/scikit-learn/scikit-learn/issues/24638 | [
"Enhancement",
"module:compose"
] | TransformedTargetRegressor is incompatible with GaussianProcessRegressor when using return_std=True
### Describe the bug
`TransformedTargetRegressor` is incompatible with `GaussianProcessRegressor` when using `return_std=True` due to the fact that the model returns a tuple rather than an array.
### Steps/Code to... | 24,638 | [
-0.008046490140259266,
0.03686538711190224,
0.019994977861642838,
0.007714786566793919,
0.09174077957868576,
-0.005964282434433699,
0.045622117817401886,
-0.015871139243245125,
-0.03390980884432793,
0.001885807141661644,
0.02100127935409546,
0.03846333920955658,
0.0442882776260376,
0.08005... |
https://github.com/scikit-learn/scikit-learn/issues/24638 | [
"Enhancement",
"module:compose"
] | TransformedTargetRegressor is incompatible with GaussianProcessRegressor when using return_std=True
### Describe the bug
`TransformedTargetRegressor` is incompatible with `GaussianProcessRegressor` when using `return_std=True` due to the fact that the model returns a tuple rather than an array.
### Steps/Code to... | 24,638 | [
-0.008046490140259266,
0.03686538711190224,
0.019994977861642838,
0.007714786566793919,
0.09174077957868576,
-0.005964282434433699,
0.045622117817401886,
-0.015871139243245125,
-0.03390980884432793,
0.001885807141661644,
0.02100127935409546,
0.03846333920955658,
0.0442882776260376,
0.08005... |
https://github.com/scikit-learn/scikit-learn/issues/24638 | [
"Enhancement",
"module:compose"
] | TransformedTargetRegressor is incompatible with GaussianProcessRegressor when using return_std=True
### Describe the bug
`TransformedTargetRegressor` is incompatible with `GaussianProcessRegressor` when using `return_std=True` due to the fact that the model returns a tuple rather than an array.
### Steps/Code to... | 24,638 | [
-0.008046490140259266,
0.03686538711190224,
0.019994977861642838,
0.007714786566793919,
0.09174077957868576,
-0.005964282434433699,
0.045622117817401886,
-0.015871139243245125,
-0.03390980884432793,
0.001885807141661644,
0.02100127935409546,
0.03846333920955658,
0.0442882776260376,
0.08005... |
https://github.com/scikit-learn/scikit-learn/issues/24638 | [
"Enhancement",
"module:compose"
] | TransformedTargetRegressor is incompatible with GaussianProcessRegressor when using return_std=True
### Describe the bug
`TransformedTargetRegressor` is incompatible with `GaussianProcessRegressor` when using `return_std=True` due to the fact that the model returns a tuple rather than an array.
### Steps/Code to... | 24,638 | [
-0.008046490140259266,
0.03686538711190224,
0.019994977861642838,
0.007714786566793919,
0.09174077957868576,
-0.005964282434433699,
0.045622117817401886,
-0.015871139243245125,
-0.03390980884432793,
0.001885807141661644,
0.02100127935409546,
0.03846333920955658,
0.0442882776260376,
0.08005... |
https://github.com/scikit-learn/scikit-learn/issues/24638 | [
"Enhancement",
"module:compose"
] | TransformedTargetRegressor is incompatible with GaussianProcessRegressor when using return_std=True
### Describe the bug
`TransformedTargetRegressor` is incompatible with `GaussianProcessRegressor` when using `return_std=True` due to the fact that the model returns a tuple rather than an array.
### Steps/Code to... | 24,638 | [
-0.008046490140259266,
0.03686538711190224,
0.019994977861642838,
0.007714786566793919,
0.09174077957868576,
-0.005964282434433699,
0.045622117817401886,
-0.015871139243245125,
-0.03390980884432793,
0.001885807141661644,
0.02100127935409546,
0.03846333920955658,
0.0442882776260376,
0.08005... |
https://github.com/scikit-learn/scikit-learn/issues/24636 | [
"Bug",
"Needs Triage"
] | Multi-class roc_auc_score raises error when y_true is not sampled with all label of classes
### Describe the bug
Sometimes we would like to train or validate a multi-class classification model without using large batch size or the term **n_sample** in scikit-learn but with too many number of classes **n_classes**. ... | 24,636 | [
0.011205869726836681,
0.017341099679470062,
0.03556932136416435,
0.03292446210980415,
0.107762411236763,
-0.007799222599714994,
-0.027020927518606186,
0.01657433621585369,
0.013306437991559505,
-0.015673860907554626,
0.013840246014297009,
-0.016222845762968063,
-0.0028751553036272526,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24636 | [
"Bug",
"Needs Triage"
] | Multi-class roc_auc_score raises error when y_true is not sampled with all label of classes
### Describe the bug
Sometimes we would like to train or validate a multi-class classification model without using large batch size or the term **n_sample** in scikit-learn but with too many number of classes **n_classes**. ... | 24,636 | [
0.011205869726836681,
0.017341099679470062,
0.03556932136416435,
0.03292446210980415,
0.107762411236763,
-0.007799222599714994,
-0.027020927518606186,
0.01657433621585369,
0.013306437991559505,
-0.015673860907554626,
0.013840246014297009,
-0.016222845762968063,
-0.0028751553036272526,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24636 | [
"Bug",
"Needs Triage"
] | Multi-class roc_auc_score raises error when y_true is not sampled with all label of classes
### Describe the bug
Sometimes we would like to train or validate a multi-class classification model without using large batch size or the term **n_sample** in scikit-learn but with too many number of classes **n_classes**. ... | 24,636 | [
0.011205869726836681,
0.017341099679470062,
0.03556932136416435,
0.03292446210980415,
0.107762411236763,
-0.007799222599714994,
-0.027020927518606186,
0.01657433621585369,
0.013306437991559505,
-0.015673860907554626,
0.013840246014297009,
-0.016222845762968063,
-0.0028751553036272526,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24636 | [
"Bug",
"Needs Triage"
] | Multi-class roc_auc_score raises error when y_true is not sampled with all label of classes
### Describe the bug
Sometimes we would like to train or validate a multi-class classification model without using large batch size or the term **n_sample** in scikit-learn but with too many number of classes **n_classes**. ... | 24,636 | [
0.011205869726836681,
0.017341099679470062,
0.03556932136416435,
0.03292446210980415,
0.107762411236763,
-0.007799222599714994,
-0.027020927518606186,
0.01657433621585369,
0.013306437991559505,
-0.015673860907554626,
0.013840246014297009,
-0.016222845762968063,
-0.0028751553036272526,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24636 | [
"Bug",
"Needs Triage"
] | Multi-class roc_auc_score raises error when y_true is not sampled with all label of classes
### Describe the bug
Sometimes we would like to train or validate a multi-class classification model without using large batch size or the term **n_sample** in scikit-learn but with too many number of classes **n_classes**. ... | 24,636 | [
0.011205869726836681,
0.017341099679470062,
0.03556932136416435,
0.03292446210980415,
0.107762411236763,
-0.007799222599714994,
-0.027020927518606186,
0.01657433621585369,
0.013306437991559505,
-0.015673860907554626,
0.013840246014297009,
-0.016222845762968063,
-0.0028751553036272526,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24634 | [
"Documentation",
"module:model_selection"
] | HalvingRandomSearchCV error
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search_successive_halving.py#L220
when resources is the hyperparameter of estimator, the best performance is not always at its maximum, right?. so in my opinion, in this case, we should ... | 24,634 | [
0.00863436795771122,
-0.057556603103876114,
0.021021224558353424,
-0.00472393399104476,
0.04042329266667366,
-0.05043007805943489,
0.0005894402856938541,
0.03950053080916405,
-0.03283815458416939,
-0.020981216803193092,
0.021063396707177162,
0.04870191961526871,
0.015931980684399605,
-0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24634 | [
"Documentation",
"module:model_selection"
] | HalvingRandomSearchCV error
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search_successive_halving.py#L220
when resources is the hyperparameter of estimator, the best performance is not always at its maximum, right?. so in my opinion, in this case, we should ... | 24,634 | [
0.00863436795771122,
-0.057556603103876114,
0.021021224558353424,
-0.00472393399104476,
0.04042329266667366,
-0.05043007805943489,
0.0005894402856938541,
0.03950053080916405,
-0.03283815458416939,
-0.020981216803193092,
0.021063396707177162,
0.04870191961526871,
0.015931980684399605,
-0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24634 | [
"Documentation",
"module:model_selection"
] | HalvingRandomSearchCV error
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search_successive_halving.py#L220
when resources is the hyperparameter of estimator, the best performance is not always at its maximum, right?. so in my opinion, in this case, we should ... | 24,634 | [
0.00863436795771122,
-0.057556603103876114,
0.021021224558353424,
-0.00472393399104476,
0.04042329266667366,
-0.05043007805943489,
0.0005894402856938541,
0.03950053080916405,
-0.03283815458416939,
-0.020981216803193092,
0.021063396707177162,
0.04870191961526871,
0.015931980684399605,
-0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24634 | [
"Documentation",
"module:model_selection"
] | HalvingRandomSearchCV error
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search_successive_halving.py#L220
when resources is the hyperparameter of estimator, the best performance is not always at its maximum, right?. so in my opinion, in this case, we should ... | 24,634 | [
0.00863436795771122,
-0.057556603103876114,
0.021021224558353424,
-0.00472393399104476,
0.04042329266667366,
-0.05043007805943489,
0.0005894402856938541,
0.03950053080916405,
-0.03283815458416939,
-0.020981216803193092,
0.021063396707177162,
0.04870191961526871,
0.015931980684399605,
-0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24634 | [
"Documentation",
"module:model_selection"
] | HalvingRandomSearchCV error
### Describe the bug
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search_successive_halving.py#L220
when resources is the hyperparameter of estimator, the best performance is not always at its maximum, right?. so in my opinion, in this case, we should ... | 24,634 | [
0.00863436795771122,
-0.057556603103876114,
0.021021224558353424,
-0.00472393399104476,
0.04042329266667366,
-0.05043007805943489,
0.0005894402856938541,
0.03950053080916405,
-0.03283815458416939,
-0.020981216803193092,
0.021063396707177162,
0.04870191961526871,
0.015931980684399605,
-0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24622 | [
"Needs Triage"
] | Proposal for creating a SwitchCase
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24619
<div type='discussions-op-text'>
<sup>Originally posted by **JaimeArboleda** October 10, 2022</sup>
New operator in `sklearn.compose` for creating *branches* or *switch-cases* in the whole pipeline... | 24,622 | [
-0.010166469030082226,
0.0026502220425754786,
-0.021808549761772156,
-0.03041115775704384,
-0.011295907199382782,
-0.010601810179650784,
0.05280985310673714,
-0.04270575940608978,
-0.02013690024614334,
-0.041780825704336166,
0.012216225266456604,
0.060756273567676544,
-0.013305693864822388,
... |
https://github.com/scikit-learn/scikit-learn/issues/24622 | [
"Needs Triage"
] | Proposal for creating a SwitchCase
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24619
<div type='discussions-op-text'>
<sup>Originally posted by **JaimeArboleda** October 10, 2022</sup>
New operator in `sklearn.compose` for creating *branches* or *switch-cases* in the whole pipeline... | 24,622 | [
-0.010166469030082226,
0.0026502220425754786,
-0.021808549761772156,
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0.05280985310673714,
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0.012216225266456604,
0.060756273567676544,
-0.013305693864822388,
... |
https://github.com/scikit-learn/scikit-learn/issues/24622 | [
"Needs Triage"
] | Proposal for creating a SwitchCase
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24619
<div type='discussions-op-text'>
<sup>Originally posted by **JaimeArboleda** October 10, 2022</sup>
New operator in `sklearn.compose` for creating *branches* or *switch-cases* in the whole pipeline... | 24,622 | [
-0.010166469030082226,
0.0026502220425754786,
-0.021808549761772156,
-0.03041115775704384,
-0.011295907199382782,
-0.010601810179650784,
0.05280985310673714,
-0.04270575940608978,
-0.02013690024614334,
-0.041780825704336166,
0.012216225266456604,
0.060756273567676544,
-0.013305693864822388,
... |
https://github.com/scikit-learn/scikit-learn/issues/24622 | [
"Needs Triage"
] | Proposal for creating a SwitchCase
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24619
<div type='discussions-op-text'>
<sup>Originally posted by **JaimeArboleda** October 10, 2022</sup>
New operator in `sklearn.compose` for creating *branches* or *switch-cases* in the whole pipeline... | 24,622 | [
-0.010166469030082226,
0.0026502220425754786,
-0.021808549761772156,
-0.03041115775704384,
-0.011295907199382782,
-0.010601810179650784,
0.05280985310673714,
-0.04270575940608978,
-0.02013690024614334,
-0.041780825704336166,
0.012216225266456604,
0.060756273567676544,
-0.013305693864822388,
... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24615 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | Inconsistent Results For Logistic Regressions across multiple computers
### Describe the bug
Hey all -
I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I h... | 24,615 | [
0.010381030850112438,
0.04775877296924591,
0.0068663847632706165,
0.02244633436203003,
0.038899391889572144,
0.0038186104502528906,
0.1119375228881836,
0.02883227914571762,
0.05129075050354004,
0.03554476052522659,
0.02062274143099785,
-0.01902901567518711,
0.04184304550290108,
0.040962982... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
0.017042243853211403,
0.04871581867337227,
0.045799314975738525,
0.022954478859901428,
-0.007325055543333292,
0.009859178215265274,
0.049846142530441284,
0.021680597215890884,
-0.05904240533709526,
-0.0029673378448933363,
-0.017203012481331825,
0.030726134777069092,
-0.027046268805861473,
... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
-0.012291932478547096,
0.04088158905506134,
0.045114319771528244,
0.045335348695516586,
-0.021450979635119438,
0.02835795097053051,
0.07500326633453369,
-0.016071032732725143,
-0.03086276166141033,
-0.01128148939460516,
0.032473258674144745,
-0.020610716193914413,
-0.03737262636423111,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
-0.008197344839572906,
0.0375790037214756,
0.045602843165397644,
0.04681798070669174,
-0.02124187909066677,
0.028808241710066795,
0.0700368583202362,
-0.01658155769109726,
-0.030975932255387306,
-0.013084742240607738,
0.029371576383709908,
-0.01904410310089588,
-0.035732451826334,
0.032999... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
0.014728356152772903,
0.06401992589235306,
0.05890203267335892,
0.020154351368546486,
0.0036550492513924837,
0.016462894156575203,
0.07289651781320572,
-0.021913237869739532,
-0.04768218845129013,
-0.014190786518156528,
0.029041266068816185,
-0.015580108389258385,
-0.03693103790283203,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
-0.014653670601546764,
0.04274987801909447,
0.04060709848999977,
0.038695547729730606,
-0.020482638850808144,
0.019917931407690048,
0.07619579136371613,
-0.013668516650795937,
-0.013330732472240925,
-0.006839788053184748,
0.033781494945287704,
-0.025124715641140938,
-0.03170870244503021,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
0.028653640300035477,
0.07815872877836227,
0.05026697367429733,
0.012655410915613174,
0.010666945017874241,
0.011352818459272385,
0.068757563829422,
-0.01009693369269371,
-0.09471488744020462,
-0.007931211963295937,
0.006061304826289415,
-0.008064838126301765,
-0.0483807697892189,
0.044665... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
-0.008740744553506374,
0.06376629322767258,
0.03259224444627762,
0.027949973940849304,
-0.014550532214343548,
0.012421036139130592,
0.07097823917865753,
0.000862112152390182,
0.012570840306580067,
-0.0010243101278319955,
0.03630548343062401,
-0.022892117500305176,
-0.039321333169937134,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
0.004076912999153137,
0.07891695946455002,
0.025575431063771248,
0.02605690248310566,
-0.007237396668642759,
0.02540360577404499,
0.09438926726579666,
-0.01870647445321083,
0.004089273978024721,
-0.024666162207722664,
0.03194890916347504,
-0.03356148302555084,
-0.018268467858433723,
0.0538... |
https://github.com/scikit-learn/scikit-learn/issues/24614 | [
"New Feature",
"Needs Decision",
"module:linear_model",
"Needs Decision - Include Feature"
] | GLM doesn't have an offset option
### Describe the workflow you want to enable
As described at #24155, GLM should also support the offset option.
### Describe your proposed solution
For example
```python
from sklearn.linear_model import PoissonRegressor
glm = PoissonRegressor(
alpha=0,... | 24,614 | [
0.01534924004226923,
0.0542941614985466,
0.04265022650361061,
0.03233979269862175,
-0.017453748732805252,
0.02282446250319481,
0.06479274481534958,
0.0015233581652864814,
-0.031567156314849854,
-0.01964419335126877,
0.012904312461614609,
-0.03424416482448578,
-0.033629823476076126,
0.02095... |
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