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/29823 | [
"Documentation"
] | Misleading variable name for the example of AUC calculation
### Describe the issue linked to the documentation
In the [example of AUC calculation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html), it was given that:
```python
import numpy as np
from sklearn import metrics
y = np.arra... | 29,823 | [
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0.011021650396287441,
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0.049254029989242554,
0.004795829299837351,
0.08912765234708786,
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0.0017966224113479257,
0.044136520475149155,
0.017055923119187355,
0.03026982769370079,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29823 | [
"Documentation"
] | Misleading variable name for the example of AUC calculation
### Describe the issue linked to the documentation
In the [example of AUC calculation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html), it was given that:
```python
import numpy as np
from sklearn import metrics
y = np.arra... | 29,823 | [
0.02675802633166313,
-0.03431704267859459,
0.011755332350730896,
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0.04375413805246353,
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0.0008941002888604999,
0.04309232532978058,
0.012996740639209747,
0.03421064093708992,
0.0157... |
https://github.com/scikit-learn/scikit-learn/issues/29823 | [
"Documentation"
] | Misleading variable name for the example of AUC calculation
### Describe the issue linked to the documentation
In the [example of AUC calculation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html), it was given that:
```python
import numpy as np
from sklearn import metrics
y = np.arra... | 29,823 | [
0.027415812015533447,
-0.03346104547381401,
0.011035111732780933,
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0.0013270972995087504,
0.044305168092250824,
0.015449500642716885,
0.0317973867058754,
0.0163... |
https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
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https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
-0.047395095229148865,
0.1306735873222351,
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0.06092563644051552,
0.00015260970394592732,
-0.013785499148070812,... |
https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
-0.047395095229148865,
0.1306735873222351,
0.009511936455965042,
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0.08329708129167557,
0.012732045724987984,
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-0.0016130987787619233,
0.06092563644051552,
0.00015260970394592732,
-0.013785499148070812,... |
https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
-0.047395095229148865,
0.1306735873222351,
0.009511936455965042,
-0.053227104246616364,
-0.019665129482746124,
-0.017255933955311775,
0.08329708129167557,
0.012732045724987984,
-0.0032171602360904217,
-0.0016130987787619233,
0.06092563644051552,
0.00015260970394592732,
-0.013785499148070812,... |
https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
-0.047395095229148865,
0.1306735873222351,
0.009511936455965042,
-0.053227104246616364,
-0.019665129482746124,
-0.017255933955311775,
0.08329708129167557,
0.012732045724987984,
-0.0032171602360904217,
-0.0016130987787619233,
0.06092563644051552,
0.00015260970394592732,
-0.013785499148070812,... |
https://github.com/scikit-learn/scikit-learn/issues/29813 | [
"New Feature",
"Needs Decision - Close"
] | Adding timeseries-tailored baseline strategy to Dummy* estimators. Making them more intelligent with strategy="best".
### Describe the workflow you want to enable
While always predicting the mean (for regression) or the most frequent class (for classification) are solid baselines for many ML workloads, they are too... | 29,813 | [
-0.047395095229148865,
0.1306735873222351,
0.009511936455965042,
-0.053227104246616364,
-0.019665129482746124,
-0.017255933955311775,
0.08329708129167557,
0.012732045724987984,
-0.0032171602360904217,
-0.0016130987787619233,
0.06092563644051552,
0.00015260970394592732,
-0.013785499148070812,... |
https://github.com/scikit-learn/scikit-learn/issues/29807 | [
"New Feature"
] | make_regression always generates positive coefficients
### Describe the workflow you want to enable
This is my first issue, please forgive the non-standard format.
I noticed that when using make_regression to generate random data, I always get positive coefficients.I read the source code and found that this situat... | 29,807 | [
-0.033613406121730804,
0.027791067957878113,
0.026384787634015083,
-0.020671984180808067,
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0.031287696212530136,
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0.05044656619429588,
0.060654688626527786,
0.009551119059324265,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29807 | [
"New Feature"
] | make_regression always generates positive coefficients
### Describe the workflow you want to enable
This is my first issue, please forgive the non-standard format.
I noticed that when using make_regression to generate random data, I always get positive coefficients.I read the source code and found that this situat... | 29,807 | [
-0.03434827923774719,
0.0297918189316988,
0.02679288759827614,
-0.02102046273648739,
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0.0301860049366951,
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-0.014008201658725739,
0.049927741289138794,
0.06087952107191086,
0.008349701762199402,
0.05307... |
https://github.com/scikit-learn/scikit-learn/issues/29807 | [
"New Feature"
] | make_regression always generates positive coefficients
### Describe the workflow you want to enable
This is my first issue, please forgive the non-standard format.
I noticed that when using make_regression to generate random data, I always get positive coefficients.I read the source code and found that this situat... | 29,807 | [
-0.030491000041365623,
0.021049970760941505,
0.02275872230529785,
-0.013470239005982876,
0.0453876368701458,
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0.033426232635974884,
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-0.008392211981117725,
0.04965556785464287,
0.06405342370271683,
0.017416469752788544,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29805 | [
"Documentation"
] | DOC: Add Bioconductor's package to the list of scikit-learn Related Projects
### Describe the issue linked to the documentation
### Description
Add Bioconductor's package to the list of scikit-learn Related Projects:
https://scikit-learn.org/stable/related_projects.html
https://bioconductor.org/packages/release/... | 29,805 | [
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0.0504097044467926,
0.02189745008945465,
0.036430057138204575,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
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0.01774054951965809,
0.006123429164290428,
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0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
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-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29799 | [
"Bug",
"Needs Info"
] | Importing sklearn takes too much time compared to other imports except spaCy
### Describe the bug
Can you open a separate issue please with more details about your problem?
In particular, please include the following information in your new issue:
- is this a regression in scikit-learn 1.5, i.e. d... | 29,799 | [
-0.005527354311197996,
-0.0067491233348846436,
0.01774054951965809,
0.006123429164290428,
0.02869088388979435,
0.004174217116087675,
0.020262572914361954,
0.029154306277632713,
0.07423549890518188,
-0.028699630871415138,
0.01775481551885605,
0.05481389909982681,
-0.007761191576719284,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29794 | [
"New Feature"
] | Ensure RandomizedSearchCV (and other optimizers) skips duplicated hyperparameter combinations
### Describe the workflow you want to enable
RandomizedSearchCV and similar hyperparameter tuners need to handle duplicate hyperparameter combinations. This issue is particularly noticeable when a user has a small number o... | 29,794 | [
-0.01352087501436472,
0.028723252937197685,
0.02689635567367077,
0.0014749999390915036,
0.07937255501747131,
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0.012126581743359566,
0.06013844907283783,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29794 | [
"New Feature"
] | Ensure RandomizedSearchCV (and other optimizers) skips duplicated hyperparameter combinations
### Describe the workflow you want to enable
RandomizedSearchCV and similar hyperparameter tuners need to handle duplicate hyperparameter combinations. This issue is particularly noticeable when a user has a small number o... | 29,794 | [
-0.01352087501436472,
0.028723252937197685,
0.02689635567367077,
0.0014749999390915036,
0.07937255501747131,
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0.012126581743359566,
0.06013844907283783,
-0.002179259667173028,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29792 | [
"Bug"
] | Discrepancy between .fit_transform() and .transform() methods in the LLE module
### Describe the bug
A user would expect the same result from
- `.fit(X)` and then `.transform(X)`
- `.fit_transformX()`
But this is not the case in the current code for `LocallyLinearEmbedding`.
### Steps/Code to Reproduce
... | 29,792 | [
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0.05675838887691498,
0.07082251459360123,
-0.0165... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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0.0786... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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0.005103799048811197,
-0.0016326409531757236,
-0.01405988447368145,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29790 | [
"New Feature"
] | Include T-Processes Subclass of Gaussian-Processes
This is a feature request to implement T-process. Moving the discussion into the issue tracker to get more visibility.
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/28942
<div type='discussions-op-text'>
<sup>Originally posted by *... | 29,790 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/29784 | [
"Bug",
"Needs Triage"
] | Big problem with scikit-learn on Python311 when installing (FreeBSD)
### Describe the bug
[long log.txt](https://github.com/user-attachments/files/16875744/long.log.txt)
https://github.com/man-group/dtale/issues/877#issuecomment-2329784822
### Steps/Code to Reproduce
After `pip install -U scikit-learn==1.1.3`
htt... | 29,784 | [
0.03294702619314194,
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0.026476869359612465,
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0.04081261530518532,
0.0537755973637104,
-0.002956536365672946,
0.06030... |
https://github.com/scikit-learn/scikit-learn/issues/29784 | [
"Bug",
"Needs Triage"
] | Big problem with scikit-learn on Python311 when installing (FreeBSD)
### Describe the bug
[long log.txt](https://github.com/user-attachments/files/16875744/long.log.txt)
https://github.com/man-group/dtale/issues/877#issuecomment-2329784822
### Steps/Code to Reproduce
After `pip install -U scikit-learn==1.1.3`
htt... | 29,784 | [
0.033453457057476044,
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0.019023364409804344,
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0.04585652053356171,
0.04932590201497078,
0.01516355387866497,
0.0283189... |
https://github.com/scikit-learn/scikit-learn/issues/29783 | [
"Bug"
] | Running RFECV.fit inside joblib.Parallel causes ValueError or AttributeError
```py
from sklearn.datasets import make_classification
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from joblib import Parallel, del... | 29,783 | [
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0.0029325613286346197,
-0.0005533240037038922,
... |
https://github.com/scikit-learn/scikit-learn/issues/29783 | [
"Bug"
] | Running RFECV.fit inside joblib.Parallel causes ValueError or AttributeError
```py
from sklearn.datasets import make_classification
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from joblib import Parallel, del... | 29,783 | [
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0.06638321280479431,
0.0029325613286346197,
-0.0005533240037038922,
... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0.07... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0.073859... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0.073014326... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
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0.08... |
https://github.com/scikit-learn/scikit-learn/issues/29781 | [
"Build / CI"
] | CI CUDA CI not running in lock-file update automated PR
Discussed in https://github.com/scikit-learn/scikit-learn/pull/29576#issuecomment-2255371442, for now we need to remember to unset "CUDA CI" label and set it again manually on automated array API lock-file PRs like https://github.com/scikit-learn/scikit-learn/pul... | 29,781 | [
0.0110695268958807,
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0.02868061326444149,
-0.019265582785010338,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29778 | [
"Needs Triage"
] | Implementation of fit_transform in ColumnTransformer
In `TransformerMixin`, `fit_transform` is implemented via `self.fit(X, **fit_params).transform(X)`. But it appears that `ColumnTransformer` is implemented in the opposite way: `fit` calls `self.fit_transform(X, y=y, **params)`. Is there a reason for this?
This ... | 29,778 | [
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0.061512384563684464,
0.03276875987648964,
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0.02619420550763607,
-0.0017334239091724157,
0.01920376904308796,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29772 | [
"Regression"
] | C regularization parameter error when assigned infinity
### Describe the bug
I am trying to run a very simple SVC with the regularization parameter set to infinity, that is a hard-margin classifier.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
iris = ... | 29,772 | [
0.012003210373222828,
-0.027876388281583786,
0.03757525607943535,
-0.01321785431355238,
0.13002492487430573,
-0.028156062588095665,
-0.02045293152332306,
0.052753325551748276,
-0.007261316291987896,
0.04956187680363655,
0.07044428586959839,
0.07522626966238022,
0.005204094108194113,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/29772 | [
"Regression"
] | C regularization parameter error when assigned infinity
### Describe the bug
I am trying to run a very simple SVC with the regularization parameter set to infinity, that is a hard-margin classifier.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
iris = ... | 29,772 | [
0.012003210373222828,
-0.027876388281583786,
0.03757525607943535,
-0.01321785431355238,
0.13002492487430573,
-0.028156062588095665,
-0.02045293152332306,
0.052753325551748276,
-0.007261316291987896,
0.04956187680363655,
0.07044428586959839,
0.07522626966238022,
0.005204094108194113,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/29772 | [
"Regression"
] | C regularization parameter error when assigned infinity
### Describe the bug
I am trying to run a very simple SVC with the regularization parameter set to infinity, that is a hard-margin classifier.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
iris = ... | 29,772 | [
0.012003210373222828,
-0.027876388281583786,
0.03757525607943535,
-0.01321785431355238,
0.13002492487430573,
-0.028156062588095665,
-0.02045293152332306,
0.052753325551748276,
-0.007261316291987896,
0.04956187680363655,
0.07044428586959839,
0.07522626966238022,
0.005204094108194113,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/29768 | [
"Needs Triage"
] | z
COMMENT:
Closing, this seems to have been opened by mistake ... | 29,768 | [
0.045938488095998764,
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0.0288072619587183,
0.020041078329086304,
0.007679950911551714,
0.009141522459685802,
0.010422414168715477,
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-0.02444775216281414,
-0.018164847046136856,
0.04304014518857002,
0.02557685784995556,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
0.030209360644221306,
0.06113181635737419,
0.03638839349150658,
-0.01680522784590721,
-0.0172564759850502,
0.06840810179710388,
-0.015874093398451805,
0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29757 | [
"Question"
] | Compiling Fails due to sklearn/metrics/pairwise.py
### Describe the bug
_This may be a duplicate of [29754](https://github.com/scikit-learn/scikit-learn/issues/29754)._
Having merged from upstream, the imports in `sklearn/metrics/pairwise.py` do not compile.
I am getting error:
"sklearn/metrics/_dist_metri... | 29,757 | [
-0.002343905856832862,
0.016560129821300507,
-0.006005674134939909,
0.008454995229840279,
0.09003254771232605,
0.03627147153019905,
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0.06840810179710388,
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0.01383... |
https://github.com/scikit-learn/scikit-learn/issues/29754 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 31, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10642170515)** (Aug 31, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10650754762) on Sep 01... | 29,754 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29748 | [
"New Feature",
"Needs Decision"
] | Expose Seed in FeatureHasher and HashingVectorizer
### Describe the workflow you want to enable
Varying the seed of the FeatureHasher allows the user to control what inputs collide. This can allow for a better feature space either through experimentation (as a hyperparameter) or explicitly searching for a space that ... | 29,748 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/29748 | [
"New Feature",
"Needs Decision"
] | Expose Seed in FeatureHasher and HashingVectorizer
### Describe the workflow you want to enable
Varying the seed of the FeatureHasher allows the user to control what inputs collide. This can allow for a better feature space either through experimentation (as a hyperparameter) or explicitly searching for a space that ... | 29,748 | [
-0.012820955365896225,
0.047119706869125366,
0.010876979678869247,
0.027535583823919296,
0.02446886897087097,
0.009691862389445305,
0.031082145869731903,
0.005053785629570484,
0.022182483226060867,
-0.025758229196071625,
0.07644038647413254,
-0.0075112017802894115,
-0.05003003403544426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29748 | [
"New Feature",
"Needs Decision"
] | Expose Seed in FeatureHasher and HashingVectorizer
### Describe the workflow you want to enable
Varying the seed of the FeatureHasher allows the user to control what inputs collide. This can allow for a better feature space either through experimentation (as a hyperparameter) or explicitly searching for a space that ... | 29,748 | [
-0.012820955365896225,
0.047119706869125366,
0.010876979678869247,
0.027535583823919296,
0.02446886897087097,
0.009691862389445305,
0.031082145869731903,
0.005053785629570484,
0.022182483226060867,
-0.025758229196071625,
0.07644038647413254,
-0.0075112017802894115,
-0.05003003403544426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29748 | [
"New Feature",
"Needs Decision"
] | Expose Seed in FeatureHasher and HashingVectorizer
### Describe the workflow you want to enable
Varying the seed of the FeatureHasher allows the user to control what inputs collide. This can allow for a better feature space either through experimentation (as a hyperparameter) or explicitly searching for a space that ... | 29,748 | [
-0.012820955365896225,
0.047119706869125366,
0.010876979678869247,
0.027535583823919296,
0.02446886897087097,
0.009691862389445305,
0.031082145869731903,
0.005053785629570484,
0.022182483226060867,
-0.025758229196071625,
0.07644038647413254,
-0.0075112017802894115,
-0.05003003403544426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29748 | [
"New Feature",
"Needs Decision"
] | Expose Seed in FeatureHasher and HashingVectorizer
### Describe the workflow you want to enable
Varying the seed of the FeatureHasher allows the user to control what inputs collide. This can allow for a better feature space either through experimentation (as a hyperparameter) or explicitly searching for a space that ... | 29,748 | [
-0.012820955365896225,
0.047119706869125366,
0.010876979678869247,
0.027535583823919296,
0.02446886897087097,
0.009691862389445305,
0.031082145869731903,
0.005053785629570484,
0.022182483226060867,
-0.025758229196071625,
0.07644038647413254,
-0.0075112017802894115,
-0.05003003403544426,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29742 | [
"Bug",
"Sprint"
] | spin docs --no-plot runs the examples
Seen at the EuroScipy sprint
Commands run by spin:
```
$ export SPHINXOPTS=-W -D plot_gallery=0 -j auto
$ cd doc
$ make html
```
Looks like our Makefile does not use SPHINXOPTS the same way as expected:
Probably we have a slightly different way of building the doc
`... | 29,742 | [
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0.008461615070700645,
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0.002604913664981723,
-0.0028853679541498423,
... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
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0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29735 | [
"Documentation"
] | Improve documentation to specify the interface of metric as a callable in KNNImputer
## Repurpose issue to solve
In `KNNImputer`, there is no mention regarding the expected interface of the parameter `metric` apart from the signature when passing a callable. We could reuse the documentation of `NearestNeighors` tha... | 29,735 | [
0.005718557629734278,
0.034852247685194016,
0.025150148198008537,
-0.031021516770124435,
0.02889338508248329,
0.003997227177023888,
0.05432213097810745,
0.04645862057805061,
0.0375976599752903,
-0.03116288036108017,
0.008958456106483936,
0.06692618131637573,
-0.009661943651735783,
-0.00699... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29734 | [
"Bug"
] | Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification
### Describe the bug
I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:
```pytb
ValueError: pos_label=1 is not ... | 29,734 | [
-0.00709898304194212,
-0.07863007485866547,
0.022424815222620964,
0.0158160999417305,
0.07960238307714462,
-0.003693223698064685,
0.0800287276506424,
0.016088075935840607,
0.023564763367176056,
-0.0018231114372611046,
0.0025048109237104654,
0.022429758682847023,
0.043728213757276535,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/29730 | [
"New Feature",
"Needs Info"
] | Add a LogTransformer and a LogWithShiftTransformer
### Describe the workflow you want to enable
I suggest adding new transformers to scikit-learn named `LogTransformer` and `LogWithShiftTransformer`, which would add the functionality of applying a logarithmic transformation and a logarithmic transformation capable of... | 29,730 | [
-0.02836712822318077,
0.04090762510895729,
0.04569279029965401,
-0.06267017871141434,
0.0009455648250877857,
-0.02589588798582554,
-0.022866763174533844,
-0.009806615300476551,
0.0026096482761204243,
-0.02920253947377205,
0.041437096893787384,
-0.01717904396355152,
-0.03747352585196495,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29730 | [
"New Feature",
"Needs Info"
] | Add a LogTransformer and a LogWithShiftTransformer
### Describe the workflow you want to enable
I suggest adding new transformers to scikit-learn named `LogTransformer` and `LogWithShiftTransformer`, which would add the functionality of applying a logarithmic transformation and a logarithmic transformation capable of... | 29,730 | [
-0.030641043558716774,
0.028418460860848427,
0.037168752402067184,
-0.06712819635868073,
-0.006051476113498211,
-0.014969645999372005,
-0.010515071451663971,
-0.012051267549395561,
-0.00702363857999444,
-0.032857127487659454,
0.02706061117351055,
-0.0009077531285583973,
-0.048490699380636215... |
https://github.com/scikit-learn/scikit-learn/issues/29729 | [
"Documentation",
"Needs Triage"
] | Remove outdated brand file identity.pdf
### Describe the issue linked to the documentation
This document is outdated : doc/logos/identity.pdf
### Suggest a potential alternative/fix
_No response_
COMMENT:
@francoisgoupil Do we have a similar document when that has been updated when renewing the scikit-learn tradem... | 29,729 | [
0.0644138902425766,
0.0076097482815384865,
0.018667759373784065,
-0.030137160792946815,
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0.020867910236120224,
0.020679393783211708,
0.028239112347364426,
0.019971150904893875,
-0.022281041368842125,
0.019401052966713905,
0.03659239783883095,
0.04254261031746864,
0.002... |
https://github.com/scikit-learn/scikit-learn/issues/29729 | [
"Documentation",
"Needs Triage"
] | Remove outdated brand file identity.pdf
### Describe the issue linked to the documentation
This document is outdated : doc/logos/identity.pdf
### Suggest a potential alternative/fix
_No response_
COMMENT:
Thanks @x-probabl for raising the issue.
@glemaitre we don't have this yet, but it is on our TODO. For the m... | 29,729 | [
0.033619076013565063,
0.02759019285440445,
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0.020597057417035103,
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0.0036321997176855803,
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0.0465925857424736,
-0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/29729 | [
"Documentation",
"Needs Triage"
] | Remove outdated brand file identity.pdf
### Describe the issue linked to the documentation
This document is outdated : doc/logos/identity.pdf
### Suggest a potential alternative/fix
_No response_
COMMENT:
Once we have a replacement, happy to replace the file, but in the meantime, I honestly don't see how it's outd... | 29,729 | [
0.032756730914115906,
0.043307896703481674,
0.019129160791635513,
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0.016483422368764877,
0.04653611406683922,
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https://github.com/scikit-learn/scikit-learn/issues/29725 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Sep 01, 2024) ⚠️
**CI is still failing on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69751&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Sep 01, 2024)
- sklearn.datasets._lfw.fetc... | 29,725 | [
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0.021036487072706223,
0.058... |
https://github.com/scikit-learn/scikit-learn/issues/29722 | [
"New Feature"
] | Make `KNeighborsClassifier.predict` and `KNeighborsRegressor.predict` react the same way to `X=None`
### Describe the workflow you want to enable
Currently `KNeighborsRegressor.predict()` accepts `None` as input, in which case it returns prediction for all samples in the training set based on the nearest neighbors ... | 29,722 | [
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0.0058607496321201324,
0.028877077624201775,
-0.05114588886499405,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29722 | [
"New Feature"
] | Make `KNeighborsClassifier.predict` and `KNeighborsRegressor.predict` react the same way to `X=None`
### Describe the workflow you want to enable
Currently `KNeighborsRegressor.predict()` accepts `None` as input, in which case it returns prediction for all samples in the training set based on the nearest neighbors ... | 29,722 | [
-0.004386939108371735,
0.046507276594638824,
0.021265728399157524,
0.005501242820173502,
0.0009362511336803436,
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0.04800824820995331,
0.02212519198656082,
0.03563563898205757,
-0.014118743129074574,
0.0058607496321201324,
0.028877077624201775,
-0.05114588886499405,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29722 | [
"New Feature"
] | Make `KNeighborsClassifier.predict` and `KNeighborsRegressor.predict` react the same way to `X=None`
### Describe the workflow you want to enable
Currently `KNeighborsRegressor.predict()` accepts `None` as input, in which case it returns prediction for all samples in the training set based on the nearest neighbors ... | 29,722 | [
-0.004386939108371735,
0.046507276594638824,
0.021265728399157524,
0.005501242820173502,
0.0009362511336803436,
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0.04800824820995331,
0.02212519198656082,
0.03563563898205757,
-0.014118743129074574,
0.0058607496321201324,
0.028877077624201775,
-0.05114588886499405,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29722 | [
"New Feature"
] | Make `KNeighborsClassifier.predict` and `KNeighborsRegressor.predict` react the same way to `X=None`
### Describe the workflow you want to enable
Currently `KNeighborsRegressor.predict()` accepts `None` as input, in which case it returns prediction for all samples in the training set based on the nearest neighbors ... | 29,722 | [
-0.004386939108371735,
0.046507276594638824,
0.021265728399157524,
0.005501242820173502,
0.0009362511336803436,
-0.030208460986614227,
0.04800824820995331,
0.02212519198656082,
0.03563563898205757,
-0.014118743129074574,
0.0058607496321201324,
0.028877077624201775,
-0.05114588886499405,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29715 | [
"Bug"
] | LocallyLinearEmbedding : n_neighbors <= n_samples
### Describe the bug
Minor bug in `LocallyLinearEmbedding`'s parameter validation:
https://github.com/scikit-learn/scikit-learn/blob/70fdc843a4b8182d97a3508c1a426acc5e87e980/sklearn/manifold/_locally_linear.py#L226-L230
The `if` condition contradicts the error m... | 29,715 | [
-0.0012394733494147658,
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0.023020535707473755,
0.03803015872836113,
0.04227161034941673,
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0.04709858447313309,
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0.060919079929590225,
0.018017491325736046,
0.05012119188904762,
0.03172712028026581,
0.022064467892050743,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29715 | [
"Bug"
] | LocallyLinearEmbedding : n_neighbors <= n_samples
### Describe the bug
Minor bug in `LocallyLinearEmbedding`'s parameter validation:
https://github.com/scikit-learn/scikit-learn/blob/70fdc843a4b8182d97a3508c1a426acc5e87e980/sklearn/manifold/_locally_linear.py#L226-L230
The `if` condition contradicts the error m... | 29,715 | [
-0.0012394733494147658,
-0.04841535910964012,
0.023020535707473755,
0.03803015872836113,
0.04227161034941673,
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0.04709858447313309,
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0.060919079929590225,
0.018017491325736046,
0.05012119188904762,
0.03172712028026581,
0.022064467892050743,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29703 | [
"Developer API"
] | Split common tests into groups
With https://github.com/scikit-learn/scikit-learn/pull/29699 we start by having two groups of tests:
- API: the PR introduces a very basic start for this category from existing tests, but the idea is to add more here, and to properly document them in the developer guide as we go later... | 29,703 | [
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0.09023785591125488,
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0.009280379861593246,
0.02888357825577259,
0.11814981698989868,
0.08895081281661987,
0.053732167929410934,
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0.04436995089054108,
0.021769849583506584,
-0.030182983726263046,
0.03271... |
https://github.com/scikit-learn/scikit-learn/issues/29698 | [
"Needs Info",
"Needs Reproducible Code"
] | Problem using RandomizedSearchCV
Hello, I am Yuvraj.
Today, I encountered an issue while running a model using RandomizedSearchCV. The process works fine when n_iter=2, but it gets stuck when n_iter=3. I am unsure why this happens.
![image](https://github.com/user-attachments/assets/c8da7183-276b-4bfa-b50d-4374a... | 29,698 | [
0.049594487994909286,
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0.020927609875798225,
0.00325801526196301,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29698 | [
"Needs Info",
"Needs Reproducible Code"
] | Problem using RandomizedSearchCV
Hello, I am Yuvraj.
Today, I encountered an issue while running a model using RandomizedSearchCV. The process works fine when n_iter=2, but it gets stuck when n_iter=3. I am unsure why this happens.
![image](https://github.com/user-attachments/assets/c8da7183-276b-4bfa-b50d-4374a... | 29,698 | [
0.06556115299463272,
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0.006061633117496967,
0.025194482877850533,
... |
https://github.com/scikit-learn/scikit-learn/issues/29698 | [
"Needs Info",
"Needs Reproducible Code"
] | Problem using RandomizedSearchCV
Hello, I am Yuvraj.
Today, I encountered an issue while running a model using RandomizedSearchCV. The process works fine when n_iter=2, but it gets stuck when n_iter=3. I am unsure why this happens.
![image](https://github.com/user-attachments/assets/c8da7183-276b-4bfa-b50d-4374a... | 29,698 | [
0.054551348090171814,
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0.012457014992833138,
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0.00998701062053442,
0.029775457456707954,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29698 | [
"Needs Info",
"Needs Reproducible Code"
] | Problem using RandomizedSearchCV
Hello, I am Yuvraj.
Today, I encountered an issue while running a model using RandomizedSearchCV. The process works fine when n_iter=2, but it gets stuck when n_iter=3. I am unsure why this happens.
![image](https://github.com/user-attachments/assets/c8da7183-276b-4bfa-b50d-4374a... | 29,698 | [
0.0446624718606472,
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0.01797058992087841,
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0.048988837748765945,
-0.001955701969563961,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
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0.07617073506116867,
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0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
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