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/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
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-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30139 | [
"Bug",
"Developer API"
] | The input_tags.sparse flag is often incorrect
### Describe the bug
If I understood correctly the developer API for tags, `input_tags.sparse` tells us whether an estimator can accept sparse data or not. For many estimators it seems that `input_tags.sparse` is False but should be True.
### Steps/Code to Reproduce
```... | 30,139 | [
0.020888717845082283,
-0.021700391545891762,
0.01986810751259327,
0.0368124358355999,
0.08652026206254959,
0.01006926316767931,
0.024281227961182594,
0.044520385563373566,
0.025410789996385574,
-0.008937549777328968,
0.04219292104244232,
0.0404428094625473,
0.029060520231723785,
0.04971772... |
https://github.com/scikit-learn/scikit-learn/issues/30139 | [
"Bug",
"Developer API"
] | The input_tags.sparse flag is often incorrect
### Describe the bug
If I understood correctly the developer API for tags, `input_tags.sparse` tells us whether an estimator can accept sparse data or not. For many estimators it seems that `input_tags.sparse` is False but should be True.
### Steps/Code to Reproduce
```... | 30,139 | [
0.020888717845082283,
-0.021700391545891762,
0.01986810751259327,
0.0368124358355999,
0.08652026206254959,
0.01006926316767931,
0.024281227961182594,
0.044520385563373566,
0.025410789996385574,
-0.008937549777328968,
0.04219292104244232,
0.0404428094625473,
0.029060520231723785,
0.04971772... |
https://github.com/scikit-learn/scikit-learn/issues/30139 | [
"Bug",
"Developer API"
] | The input_tags.sparse flag is often incorrect
### Describe the bug
If I understood correctly the developer API for tags, `input_tags.sparse` tells us whether an estimator can accept sparse data or not. For many estimators it seems that `input_tags.sparse` is False but should be True.
### Steps/Code to Reproduce
```... | 30,139 | [
0.020888717845082283,
-0.021700391545891762,
0.01986810751259327,
0.0368124358355999,
0.08652026206254959,
0.01006926316767931,
0.024281227961182594,
0.044520385563373566,
0.025410789996385574,
-0.008937549777328968,
0.04219292104244232,
0.0404428094625473,
0.029060520231723785,
0.04971772... |
https://github.com/scikit-learn/scikit-learn/issues/30136 | [
"Documentation"
] | Webpage typo
### Describe the issue linked to the documentation
In the first part of the [About Page](https://scikit-learn.org/stable/about.html), it says "Later that year, Matthieu Brucher **started work** on this project as part of his thesis."
### Suggest a potential alternative/fix
"Later that year, Matthieu Br... | 30,136 | [
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0.017907509580254555,
0.04089030995965004,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/30136 | [
"Documentation"
] | Webpage typo
### Describe the issue linked to the documentation
In the first part of the [About Page](https://scikit-learn.org/stable/about.html), it says "Later that year, Matthieu Brucher **started work** on this project as part of his thesis."
### Suggest a potential alternative/fix
"Later that year, Matthieu Br... | 30,136 | [
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0.013535312376916409,
0.0387570746243,
0.0095920... |
https://github.com/scikit-learn/scikit-learn/issues/30136 | [
"Documentation"
] | Webpage typo
### Describe the issue linked to the documentation
In the first part of the [About Page](https://scikit-learn.org/stable/about.html), it says "Later that year, Matthieu Brucher **started work** on this project as part of his thesis."
### Suggest a potential alternative/fix
"Later that year, Matthieu Br... | 30,136 | [
0.05735611170530319,
-0.0036265377420932055,
-0.006926615722477436,
-0.0003437001723796129,
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0.11216884106397629,
0.010317688807845116,
0.03721797466278076,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30131 | [
"Bug"
] | LinearRegression on sparse matrices is not sample weight consistent
Part of #16298.
### Describe the bug
When using a sparse container like `csr_array` for `X`, `LinearRegression` even fails to give the same coefficients for unit or no sample weight, and more generally fails the `test_linear_regression_sample_we... | 30,131 | [
0.001501450315117836,
0.033318210393190384,
0.03502211347222328,
0.029859429225325584,
0.06351626664400101,
0.006796776782721281,
0.048772938549518585,
0.05808261036872864,
0.030370011925697327,
-0.007537528406828642,
0.04393316060304642,
0.04376409575343132,
0.032724447548389435,
-0.03020... |
https://github.com/scikit-learn/scikit-learn/issues/30131 | [
"Bug"
] | LinearRegression on sparse matrices is not sample weight consistent
Part of #16298.
### Describe the bug
When using a sparse container like `csr_array` for `X`, `LinearRegression` even fails to give the same coefficients for unit or no sample weight, and more generally fails the `test_linear_regression_sample_we... | 30,131 | [
0.001501450315117836,
0.033318210393190384,
0.03502211347222328,
0.029859429225325584,
0.06351626664400101,
0.006796776782721281,
0.048772938549518585,
0.05808261036872864,
0.030370011925697327,
-0.007537528406828642,
0.04393316060304642,
0.04376409575343132,
0.032724447548389435,
-0.03020... |
https://github.com/scikit-learn/scikit-learn/issues/30130 | [
"Documentation"
] | DOC Motivate preferably using conda-forge's distribution of scikit-learn
A lot of people use scikit-learn's python wheels uploaded on PyPI.
Wheels were not designed for scientific packages and this leads to a variety of problems for users who use them — for more information see [the limitations of PyPi](https://pypac... | 30,130 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/30130 | [
"Documentation"
] | DOC Motivate preferably using conda-forge's distribution of scikit-learn
A lot of people use scikit-learn's python wheels uploaded on PyPI.
Wheels were not designed for scientific packages and this leads to a variety of problems for users who use them — for more information see [the limitations of PyPi](https://pypac... | 30,130 | [
0.012923283502459526,
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0.1... |
https://github.com/scikit-learn/scikit-learn/issues/30130 | [
"Documentation"
] | DOC Motivate preferably using conda-forge's distribution of scikit-learn
A lot of people use scikit-learn's python wheels uploaded on PyPI.
Wheels were not designed for scientific packages and this leads to a variety of problems for users who use them — for more information see [the limitations of PyPi](https://pypac... | 30,130 | [
0.003800119971856475,
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0.09391377121210098,
0.000700999575201422,
0.0784... |
https://github.com/scikit-learn/scikit-learn/issues/30130 | [
"Documentation"
] | DOC Motivate preferably using conda-forge's distribution of scikit-learn
A lot of people use scikit-learn's python wheels uploaded on PyPI.
Wheels were not designed for scientific packages and this leads to a variety of problems for users who use them — for more information see [the limitations of PyPi](https://pypac... | 30,130 | [
0.011250503361225128,
0.03829442337155342,
0.02855370007455349,
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0.03729648143053055,
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0.0008140196441672742,
0.0307463351637125,
0.09561954438686371,
-0.011709719896316528,
0.0976012... |
https://github.com/scikit-learn/scikit-learn/issues/30123 | [
"Needs Triage"
] | RISC-V
Can scikit-learn be installed and used normally on RISC-V architecture?
COMMENT:
I am not familiar with the topic but this certainly not a platform we test on. Looks like there a not too old discussion about the status of Python packaging on RISC-V [here](https://discuss.python.org/t/packaging-support-for-risc... | 30,123 | [
0.031148526817560196,
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0.10436229407787323,
-0.03777934983372688,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/30123 | [
"Needs Triage"
] | RISC-V
Can scikit-learn be installed and used normally on RISC-V architecture?
COMMENT:
In particular, we would need fast enough CI to run the test suite on a powerful enough RISC-V host. We could rely on emulation (via qemu) but I am afraid it might be very slow.
If someone can show that it is possible to run the... | 30,123 | [
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0.1... |
https://github.com/scikit-learn/scikit-learn/issues/30114 | [
"New Feature",
"Needs Decision"
] | Add differential privacy noise injection to SGDRegressor with automatic calibration
### Describe the workflow you want to enable
Enable differential privacy in SGDRegressor by adding noise injection with:
1. Manual noise scale setting, or
2. Automatic noise calibration from desired privacy parameter ε
### De... | 30,114 | [
-0.03039371781051159,
0.07282575964927673,
0.010774150490760803,
0.003694946179166436,
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0.05123108625411987,
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0.021761329844594002,
0.007217275444418192,
-0.00704147620126605,
-0.02738826721906662,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
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0.061754386872053146,
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0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
-0.016642272472381592,
0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
-0.00483917398378253,
-0.05694800615310669,
0.061754386872053146,
-0.002418464981019497,
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0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30113 | [
"New Feature",
"Needs Decision"
] | Add gradient clipping to SGDRegressor for stability and differential privacy
### Describe the workflow you want to enable
Add gradient clipping to SGDRegressor to:
1. Improve training stability when dealing with outliers or ill-conditioned data
2. Enable differentially private regression by bounding the influence o... | 30,113 | [
-0.013729915022850037,
0.08108548074960709,
-0.008728559128940105,
0.015430964529514313,
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0.061754386872053146,
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0.03332270681858063,
0.0054291412234306335,
-0.012665755115449429,
-0.04760836809873581,
... |
https://github.com/scikit-learn/scikit-learn/issues/30106 | [
"New Feature",
"Array API"
] | Reduce redundancy in floating type checks for Array API support
### Describe the workflow you want to enable
While working on #29978, we noticed that the following procedure is repeated across most regression metrics in `_regression.py` for the Array API:
```python
xp, _ = get_namespace(y_true, y_pred, sample... | 30,106 | [
-0.010197837837040424,
0.06725131720304489,
0.009012998081743717,
-0.014809727668762207,
0.050976574420928955,
-0.0026040184311568737,
0.04516008496284485,
0.04563676565885544,
-0.0036251817364245653,
-0.02242770791053772,
0.010795749723911285,
0.005954980384558439,
0.00823757704347372,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30106 | [
"New Feature",
"Array API"
] | Reduce redundancy in floating type checks for Array API support
### Describe the workflow you want to enable
While working on #29978, we noticed that the following procedure is repeated across most regression metrics in `_regression.py` for the Array API:
```python
xp, _ = get_namespace(y_true, y_pred, sample... | 30,106 | [
-0.010197837837040424,
0.06725131720304489,
0.009012998081743717,
-0.014809727668762207,
0.050976574420928955,
-0.0026040184311568737,
0.04516008496284485,
0.04563676565885544,
-0.0036251817364245653,
-0.02242770791053772,
0.010795749723911285,
0.005954980384558439,
0.00823757704347372,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30106 | [
"New Feature",
"Array API"
] | Reduce redundancy in floating type checks for Array API support
### Describe the workflow you want to enable
While working on #29978, we noticed that the following procedure is repeated across most regression metrics in `_regression.py` for the Array API:
```python
xp, _ = get_namespace(y_true, y_pred, sample... | 30,106 | [
-0.010197837837040424,
0.06725131720304489,
0.009012998081743717,
-0.014809727668762207,
0.050976574420928955,
-0.0026040184311568737,
0.04516008496284485,
0.04563676565885544,
-0.0036251817364245653,
-0.02242770791053772,
0.010795749723911285,
0.005954980384558439,
0.00823757704347372,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30106 | [
"New Feature",
"Array API"
] | Reduce redundancy in floating type checks for Array API support
### Describe the workflow you want to enable
While working on #29978, we noticed that the following procedure is repeated across most regression metrics in `_regression.py` for the Array API:
```python
xp, _ = get_namespace(y_true, y_pred, sample... | 30,106 | [
-0.010197837837040424,
0.06725131720304489,
0.009012998081743717,
-0.014809727668762207,
0.050976574420928955,
-0.0026040184311568737,
0.04516008496284485,
0.04563676565885544,
-0.0036251817364245653,
-0.02242770791053772,
0.010795749723911285,
0.005954980384558439,
0.00823757704347372,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30099 | [
"Documentation"
] | Inconsistency between lars_path documentation and behavior in code
### Describe the issue linked to the documentation
While using the `lars_path` function from the `sklearn.linear_model` module, I came across a confusing behavior that seems to contradict the documentation.
According to the [documentation for `lars... | 30,099 | [
0.05075354874134064,
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0.03149247542023659,
0.04258240759372711,
0.025097239762544632,
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0.06708696484565735,
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0.006899509113281965,
-0.025178376585245132,
0.04390928894281387,
0.023419726639986038,
0.04843799024820328,
-0.032... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
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0.058552972972393036,
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-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30094 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement `LogisticPCA` as a distinct variant of matrix decomposition useful for binary data
### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for "logistic PCA" is well fou... | 30,094 | [
-0.0007865903899073601,
0.06102646887302399,
0.003364262403920293,
-0.0005426140851341188,
0.03679163008928299,
0.029753202572464943,
0.04269677773118019,
0.01072907168418169,
0.00104513019323349,
-0.0006715116323903203,
0.058552972972393036,
-0.0021519693545997143,
-0.01230910886079073,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30088 | [
"RFC"
] | `from sklearn import this`
### Describe the workflow you want to enable
It's not just Python, there are also a lot of cool packages that [import this](https://calmcode.io/til/python-import-this). It something that I have taken to heart personally on many of my own open-source packages but it also seems that the Narwh... | 30,088 | [
0.05104723945260048,
0.0721578299999237,
-0.0034895064309239388,
-0.012051484547555447,
0.012260164134204388,
-0.015463145449757576,
0.004883966874331236,
-0.004041856620460749,
0.0012478079879656434,
-0.017545755952596664,
0.05414097383618355,
0.09162074327468872,
-0.03692272678017616,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30088 | [
"RFC"
] | `from sklearn import this`
### Describe the workflow you want to enable
It's not just Python, there are also a lot of cool packages that [import this](https://calmcode.io/til/python-import-this). It something that I have taken to heart personally on many of my own open-source packages but it also seems that the Narwh... | 30,088 | [
0.043914780020713806,
0.09125073254108429,
-0.006833954248577356,
-0.011705570854246616,
0.018434204161167145,
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0.015099667944014072,
0.0029040956869721413,
0.011834638193249702,
-0.01854691095650196,
0.05693782493472099,
0.09911630302667618,
-0.04893220588564873,
0.1... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30079 | [
"Bug",
"Needs Decision"
] | `roc_auc_score`: incorrect result after merging #27412
### Describe the bug
When all data instances come from the same class, #27412 changed the behaviour of `roc_auc_score` to return `0.0` instead of raising an exception. The argument for the change was the consistency with PR curves. I believe that this result is... | 30,079 | [
0.0036482177674770355,
0.03317131847143173,
0.019770724698901176,
0.015294869430363178,
0.04887956380844116,
-0.002901507308706641,
-0.002708606654778123,
-0.01247792411595583,
-0.046330198645591736,
-0.036951128393411636,
0.015866901725530624,
-0.016894401982426643,
0.047808434814214706,
... |
https://github.com/scikit-learn/scikit-learn/issues/30078 | [
"New Feature",
"Needs Triage"
] | svcmodel.fit(X_train,y_train) on GPU? we need native GPU mode for scikit-learn
### Describe the workflow you want to enable
svcmodel.fit(X_train,y_train) on GPU?
we need native GPU mode for scikit-learn
### Describe your proposed solution
svcmodel.fit(X_train,y_train) on GPU?
we need native GPU mode for sciki... | 30,078 | [
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0.010650306940078735,
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0.012305161915719509,
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0.06077595800161362,
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0.06616957485675812,
0.07256700843572617,
0.04265530779957771,
0.08020... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.017020978033542633,
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0.07671289891004562,
0.03070785291492939,
0.036146629601716995,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.018119042739272118,
-0.14232845604419708,
-0.021357618272304535,
-0.03215814381837845,
0.031093893572688103,
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0.04593399167060852,
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0.0632893443107605,
0.009324004873633385,
0.07321048527956009,
0.03283011540770531,
0.04182158038020134,
0.02438... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.01077968068420887,
-0.13364119827747345,
-0.023737195879220963,
-0.025613225996494293,
0.034672800451517105,
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0.03834105655550957,
0.0004534076724667102,
0.048142626881599426,
0.0023389069829136133,
0.06783159077167511,
0.039772722870111465,
0.040225524455308914,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.02010977454483509,
-0.1105395257472992,
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0.010125702247023582,
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0.03513447940349579,
0.03587310388684273,
0.0209636... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.020696377381682396,
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0.035628948360681534,
0.02388... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
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0.03531162813305855,
-0.0027... |
https://github.com/scikit-learn/scikit-learn/issues/30076 | [
"Documentation"
] | Error on the scikit-learn algorithm cheat-sheet?
### Describe the bug
In Clustering, if there are <10K samples, shouldn't yes go to Tough Luck (because there aren't enough samples), and no, go to MeanShift/VBGMM (because there are)?
### Steps/Code to Reproduce
# N/A
### Expected Results
# N/A
### Actual Results
... | 30,076 | [
0.015439818613231182,
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-0.02135922946035862,
-0.028907209634780884,
0.028422106057405472,
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0.04878976196050644,
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0.057132381945848465,
0.006071117706596851,
0.06651461869478226,
0.029617520049214363,
0.042663536965847015,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30072 | [
"New Feature",
"Needs Triage"
] | Add TQDM progress bar to .fit
### Describe the workflow you want to enable
Add TQDM progress bar to .fit
```
from sklearn.svm import SVC
svcmodel.fit(X_train,y_train)
```
### Describe your proposed solution
Add TQDM progress bar to .fit
### Describe alternatives you've considered, if relevant
Add TQDM pro... | 30,072 | [
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0.0110517218708992,
-0.0002665207430254668,
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0.07364945113658905,
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0.017886077985167503,
0.022137945517897606,
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0.048899464309215546,
0.03301854804158211,
0.07298548519611359,
-0.03627216815948486,
0.105... |
https://github.com/scikit-learn/scikit-learn/issues/30072 | [
"New Feature",
"Needs Triage"
] | Add TQDM progress bar to .fit
### Describe the workflow you want to enable
Add TQDM progress bar to .fit
```
from sklearn.svm import SVC
svcmodel.fit(X_train,y_train)
```
### Describe your proposed solution
Add TQDM progress bar to .fit
### Describe alternatives you've considered, if relevant
Add TQDM pro... | 30,072 | [
-0.05445529893040657,
0.009380452334880829,
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0.06575957685709,
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0.020554732531309128,
0.0098107373341918,
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0.050693150609731674,
0.03482356667518616,
0.06581851094961166,
-0.032719556242227554,
0.11617... |
https://github.com/scikit-learn/scikit-learn/issues/30072 | [
"New Feature",
"Needs Triage"
] | Add TQDM progress bar to .fit
### Describe the workflow you want to enable
Add TQDM progress bar to .fit
```
from sklearn.svm import SVC
svcmodel.fit(X_train,y_train)
```
### Describe your proposed solution
Add TQDM progress bar to .fit
### Describe alternatives you've considered, if relevant
Add TQDM pro... | 30,072 | [
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0.07405795902013779,
-0.02468077652156353,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30058 | [
"Documentation"
] | DOC broken image link in user guide due to removal of example
### Describe the issue linked to the documentation
The image at the bottom of Section 3.5.1 on https://scikit-learn.org/dev/modules/learning_curve.html is broken, which I believe is due to the removal of some example in #29936. We may want to rework or r... | 30,058 | [
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0.050263650715351105,
0.02607695758342743,
0.006... |
https://github.com/scikit-learn/scikit-learn/issues/30058 | [
"Documentation"
] | DOC broken image link in user guide due to removal of example
### Describe the issue linked to the documentation
The image at the bottom of Section 3.5.1 on https://scikit-learn.org/dev/modules/learning_curve.html is broken, which I believe is due to the removal of some example in #29936. We may want to rework or r... | 30,058 | [
0.031135661527514458,
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0.02636769786477089,
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0.05952385440468788,
0.0325445793569088,
-0.005228... |
https://github.com/scikit-learn/scikit-learn/issues/30056 | [
"Bug"
] | LinearSVC does not correctly handle sample_weight under class_weight strategy 'balanced'
### Describe the bug
LinearSVC does not pass sample weights through when computing class weights under the "balanced" strategy leading to sample weight invariance issues cross-linked to meta-issue #16298
### Steps/Code to Reprod... | 30,056 | [
0.010405356995761395,
-0.033501069992780685,
0.016667887568473816,
0.016854148358106613,
0.0876692607998848,
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0.010257130488753319,
0.014916935004293919,
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-0.029806723818182945,
0.02811066433787346,
0.08498657494783401,
0.017965778708457947,
-0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30052 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel (last failure: Oct 13, 2024) ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/5764259953508352)** (Oct 13, 2024)
COMMENT:
Probably due to https://github.com/scikit-learn/scikit-learn/pull/29861, the same CI issue was seen the PR in https://github.com/scikit-learn/sci... | 30,052 | [
0.008111533708870411,
-0.0028410698287189007,
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0.03566576540470123,
0.01834936998784542,
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0.013977584429085255,
0.05676344409584999,
0.034410133957862854,
0.022168854251503944,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30052 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel (last failure: Oct 13, 2024) ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/5764259953508352)** (Oct 13, 2024)
COMMENT:
By the way it would be good to figure out why 4 seemingly identical issues have been opened ... #30052 #30053 #30054 #30055 | 30,052 | [
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0.02549717202782631,
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0.012867210432887077,
0.052348800003528595,
0.016101887449622154,
0.023724447935819626,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30048 | [
"Documentation"
] | DOC misleading version added info for `cv_results["n_features"]` in `RFECV`
### Describe the bug
I'm using the scikit-learn version 1.3.0. When I use
`rfecv = RFECV(....)
rfecv.fit(X, y)
print(rfecv.cv_results_)`
that code gives me a traceback: `KeyError: 'n_features'`
I seen that key in the d... | 30,048 | [
0.045584626495838165,
-0.08153096586465836,
0.0021498387213796377,
0.016614295542240143,
0.0226761344820261,
-0.0002461546682752669,
0.025877511128783226,
-0.000369847723050043,
-0.00027003162540495396,
0.0021118451841175556,
0.06127498298883438,
0.045403070747852325,
0.042861323803663254,
... |
https://github.com/scikit-learn/scikit-learn/issues/30048 | [
"Documentation"
] | DOC misleading version added info for `cv_results["n_features"]` in `RFECV`
### Describe the bug
I'm using the scikit-learn version 1.3.0. When I use
`rfecv = RFECV(....)
rfecv.fit(X, y)
print(rfecv.cv_results_)`
that code gives me a traceback: `KeyError: 'n_features'`
I seen that key in the d... | 30,048 | [
0.045584626495838165,
-0.08153096586465836,
0.0021498387213796377,
0.016614295542240143,
0.0226761344820261,
-0.0002461546682752669,
0.025877511128783226,
-0.000369847723050043,
-0.00027003162540495396,
0.0021118451841175556,
0.06127498298883438,
0.045403070747852325,
0.042861323803663254,
... |
https://github.com/scikit-learn/scikit-learn/issues/30042 | [
"New Feature"
] | Add partial_fit Functionality to LinearDiscriminantAnalysis Classifier
### Describe the workflow you want to enable
Currently, Scikit-learn's LinearDiscriminantAnalysis (LDA) classifier does not support incremental learning through the partial_fit method. This poses challenges when processing large scale classifica... | 30,042 | [
-0.014224346727132797,
0.10824200510978699,
-0.017307689413428307,
0.012443491257727146,
0.03723782300949097,
-0.00912205409258604,
0.030181080102920532,
0.020569412037730217,
0.051787298172712326,
-0.03756890445947647,
0.06913170218467712,
-0.0026204471942037344,
-0.07056695222854614,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30042 | [
"New Feature"
] | Add partial_fit Functionality to LinearDiscriminantAnalysis Classifier
### Describe the workflow you want to enable
Currently, Scikit-learn's LinearDiscriminantAnalysis (LDA) classifier does not support incremental learning through the partial_fit method. This poses challenges when processing large scale classifica... | 30,042 | [
-0.019690420478582382,
0.09462164342403412,
-0.00651620514690876,
0.013767382130026817,
0.05914498120546341,
-0.007103803567588329,
0.024806266650557518,
0.02750921994447708,
0.05813781917095184,
-0.03295530006289482,
0.07029616832733154,
0.014438452199101448,
-0.05659455060958862,
0.05549... |
https://github.com/scikit-learn/scikit-learn/issues/30037 | [
"New Feature"
] | Implement the two-parameter Box-Cox transform variant
### Describe the workflow you want to enable
Currently, ony the single-parameter box-cox is implemented in sklearn.preprocessing.power_transform
The two parameter variant is defined as
![](https://wikimedia.org/api/rest_v1/media/math/render/svg/f0bcf29e7ad0c... | 30,037 | [
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0.043951403349637985,
0.05723893269896507,
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0.05945303291082382,
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0.018733108416199684,
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0.016748517751693726,
-0.009426960721611977,
0.006649017333984375,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.0034572810400277376,
0.033385660499334335,
0.016241980716586113,
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0.025702783837914467,
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0.022714875638484955,
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0.07569609582424164,
0.05506458505988121,
0.027399331331253052,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.017281673848628998,
0.02687482163310051,
0.011719878762960434,
-0.020322319120168686,
0.03378412500023842,
-0.020882226526737213,
-0.004602307919412851,
0.035854704678058624,
-0.025672268122434616,
-0.012614150531589985,
0.08094511181116104,
0.05200760439038277,
0.017446963116526604,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
0.005085302982479334,
0.05856042355298996,
0.020867355167865753,
-0.02788517437875271,
0.027792522683739662,
-0.018331753090023994,
-0.007707154378294945,
0.030214356258511543,
-0.022557860240340233,
-0.007986876182258129,
0.07738615572452545,
0.04974725469946861,
0.0234010498970747,
0.024... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.01078562717884779,
0.0269919540733099,
0.017204787582159042,
-0.02192694880068302,
0.02566857822239399,
-0.023693809285759926,
-0.018927525728940964,
0.03521215170621872,
-0.024248169735074043,
-0.006833961699157953,
0.08846966177225113,
0.039017803966999054,
0.023907944560050964,
0.024... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
0.0017459280788898468,
0.06914754211902618,
0.02502433955669403,
-0.028068624436855316,
0.020974235609173775,
-0.02874729409813881,
-0.004877133760601282,
0.029365893453359604,
-0.020274652168154716,
-0.003865225473418832,
0.056028228253126144,
0.04629751294851303,
0.0231168232858181,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.006446192041039467,
0.00789051502943039,
0.017474200576543808,
-0.023712990805506706,
0.03179037570953369,
-0.01579718291759491,
0.009310226887464523,
0.020477840676903725,
-0.01702892780303955,
-0.020431775599718094,
0.0740039199590683,
0.06645353883504868,
0.031516097486019135,
0.0068... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
0.0006242346134968102,
0.042709458619356155,
0.012571921572089195,
-0.02473599649965763,
0.02114124782383442,
-0.022939158603549004,
0.0023517438676208258,
0.025542518123984337,
-0.019874332472682,
-0.014435590244829655,
0.07462827116250992,
0.031930409371852875,
0.03030102141201496,
0.026... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.0024266468826681376,
0.04225138947367668,
0.016701621934771538,
-0.023886529728770256,
0.030498789623379707,
-0.022931378334760666,
-0.010500785894691944,
0.03546442836523056,
-0.019298303872346878,
-0.02159181609749794,
0.09232540428638458,
0.030558278784155846,
0.013833804987370968,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.004081947263330221,
0.03607990965247154,
0.016096945852041245,
-0.028575576841831207,
0.026375338435173035,
-0.02468917891383171,
-0.005873116664588451,
0.036560118198394775,
-0.01822914369404316,
-0.013743906281888485,
0.09433484077453613,
0.037231601774692535,
0.014448558911681175,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.012461460195481777,
0.016380824148654938,
0.01514296606183052,
-0.02470257878303528,
0.01840771920979023,
-0.023347677662968636,
-0.010710598900914192,
0.032099973410367966,
-0.02660016156733036,
-0.010037226602435112,
0.08961497992277145,
0.04510248079895973,
0.02098054066300392,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.007290890906006098,
0.01164401788264513,
0.014901196584105492,
-0.020980751141905785,
0.02414811961352825,
-0.02796294540166855,
-0.018171165138483047,
0.02291223593056202,
-0.044753722846508026,
-0.017978964373469353,
0.06460549682378769,
0.048148367553949356,
0.02639291249215603,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/30036 | [
"Bug",
"Needs Decision"
] | OneVsRestClassifier cannot be used with TunedThresholdClassifierCV
https://github.com/scikit-learn/scikit-learn/blob/d5082d32de2797f9594c9477f2810c743560a1f1/sklearn/model_selection/_classification_threshold.py#L386
When predict is called on `OneVsRestClassifier`, it calls `predict_proba` on the underlying classifi... | 30,036 | [
-0.004174570087343454,
0.035332463681697845,
0.025713495910167694,
-0.01687263883650303,
0.04383503645658493,
-0.026361849159002304,
-0.009489285759627819,
0.042110927402973175,
-0.024682972580194473,
-0.027650777250528336,
0.06620141863822937,
0.052461929619312286,
0.005790225695818663,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30027 | [
"Bug"
] | SGDOneClassSVM model does not converge with default stopping criteria(stops prematurely)
### Describe the bug
SGDOneClassSVM does not converge with default early stopping criteria, because the used loss is not actual loss, but only error, which can be easily 0.0 and then increase as the model converges to adequate ... | 30,027 | [
0.0018383458955213428,
0.026111982762813568,
0.027294663712382317,
0.01148146390914917,
0.097084641456604,
-0.021375399082899094,
-0.001294745714403689,
0.03779064863920212,
-0.01436897274106741,
0.018507765606045723,
0.034011583775281906,
0.08792611211538315,
-0.009530262090265751,
0.0101... |
https://github.com/scikit-learn/scikit-learn/issues/30027 | [
"Bug"
] | SGDOneClassSVM model does not converge with default stopping criteria(stops prematurely)
### Describe the bug
SGDOneClassSVM does not converge with default early stopping criteria, because the used loss is not actual loss, but only error, which can be easily 0.0 and then increase as the model converges to adequate ... | 30,027 | [
0.0018383458955213428,
0.026111982762813568,
0.027294663712382317,
0.01148146390914917,
0.097084641456604,
-0.021375399082899094,
-0.001294745714403689,
0.03779064863920212,
-0.01436897274106741,
0.018507765606045723,
0.034011583775281906,
0.08792611211538315,
-0.009530262090265751,
0.0101... |
https://github.com/scikit-learn/scikit-learn/issues/30027 | [
"Bug"
] | SGDOneClassSVM model does not converge with default stopping criteria(stops prematurely)
### Describe the bug
SGDOneClassSVM does not converge with default early stopping criteria, because the used loss is not actual loss, but only error, which can be easily 0.0 and then increase as the model converges to adequate ... | 30,027 | [
0.0018383458955213428,
0.026111982762813568,
0.027294663712382317,
0.01148146390914917,
0.097084641456604,
-0.021375399082899094,
-0.001294745714403689,
0.03779064863920212,
-0.01436897274106741,
0.018507765606045723,
0.034011583775281906,
0.08792611211538315,
-0.009530262090265751,
0.0101... |
https://github.com/scikit-learn/scikit-learn/issues/30024 | [
"New Feature"
] | One-class SVM probabilistic output
### Describe the workflow you want to enable
LIBSVM introduced one-class probabilistic outputs last year in version 3.31.
### Describe your proposed solution
Add a `probability=True/False` argument to [OneClassSVM](https://scikit-learn.org/stable/modules/generated/sklearn.... | 30,024 | [
-0.028419964015483856,
0.016221757978200912,
0.01870547980070114,
-0.018282804638147354,
0.029005879536271095,
-0.0536869540810585,
-0.0172985028475523,
-0.009775497019290924,
-0.04516233876347542,
-0.0008867677533999085,
0.10956594347953796,
0.04065338149666786,
0.0032415343448519707,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30016 | [
"Bug"
] | TfidfVectorizer does not preserve dtype for large size inputs
### Describe the bug
After fitting `TfidfVectorizer`, its `idf_` has `dtype` `np.float64` regardless of the provided `dtype` when the input data are large. The conversion from `np.float32` to `np.float64` happens [here](https://github.com/scikit-learn/sc... | 30,016 | [
-0.004412590991705656,
-0.01158562395721674,
0.029253816232085228,
0.027829287573695183,
0.10188835859298706,
0.018973734229803085,
0.04736127704381943,
0.062229834496974945,
-0.08380617201328278,
-0.03166533261537552,
0.0021461511496454477,
-0.03410197049379349,
0.009128157049417496,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30016 | [
"Bug"
] | TfidfVectorizer does not preserve dtype for large size inputs
### Describe the bug
After fitting `TfidfVectorizer`, its `idf_` has `dtype` `np.float64` regardless of the provided `dtype` when the input data are large. The conversion from `np.float32` to `np.float64` happens [here](https://github.com/scikit-learn/sc... | 30,016 | [
-0.004412590991705656,
-0.01158562395721674,
0.029253816232085228,
0.027829287573695183,
0.10188835859298706,
0.018973734229803085,
0.04736127704381943,
0.062229834496974945,
-0.08380617201328278,
-0.03166533261537552,
0.0021461511496454477,
-0.03410197049379349,
0.009128157049417496,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30016 | [
"Bug"
] | TfidfVectorizer does not preserve dtype for large size inputs
### Describe the bug
After fitting `TfidfVectorizer`, its `idf_` has `dtype` `np.float64` regardless of the provided `dtype` when the input data are large. The conversion from `np.float32` to `np.float64` happens [here](https://github.com/scikit-learn/sc... | 30,016 | [
-0.004412590991705656,
-0.01158562395721674,
0.029253816232085228,
0.027829287573695183,
0.10188835859298706,
0.018973734229803085,
0.04736127704381943,
0.062229834496974945,
-0.08380617201328278,
-0.03166533261537552,
0.0021461511496454477,
-0.03410197049379349,
0.009128157049417496,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30016 | [
"Bug"
] | TfidfVectorizer does not preserve dtype for large size inputs
### Describe the bug
After fitting `TfidfVectorizer`, its `idf_` has `dtype` `np.float64` regardless of the provided `dtype` when the input data are large. The conversion from `np.float32` to `np.float64` happens [here](https://github.com/scikit-learn/sc... | 30,016 | [
-0.004412590991705656,
-0.01158562395721674,
0.029253816232085228,
0.027829287573695183,
0.10188835859298706,
0.018973734229803085,
0.04736127704381943,
0.062229834496974945,
-0.08380617201328278,
-0.03166533261537552,
0.0021461511496454477,
-0.03410197049379349,
0.009128157049417496,
0.02... |
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