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/24545 | [
"Bug"
] | Error when returning embedded transformers in Jupyter notebook
### Describe the bug
When creating a custom transformer object that includes a transformer type as an instance, a `TypeError` is thrown if the object is returned at the end of a Jupyter cell. This does not cause an error in the terminal, but raises an e... | 24,545 | [
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0.03257336... |
https://github.com/scikit-learn/scikit-learn/issues/24545 | [
"Bug"
] | Error when returning embedded transformers in Jupyter notebook
### Describe the bug
When creating a custom transformer object that includes a transformer type as an instance, a `TypeError` is thrown if the object is returned at the end of a Jupyter cell. This does not cause an error in the terminal, but raises an e... | 24,545 | [
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0.03257336... |
https://github.com/scikit-learn/scikit-learn/issues/24540 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Exit Code -1073741819 when doing K-means++ clustering
### Describe the bug
Unfortunately I am getting an exit code in Pycharm when doing clustering with k-means++.
I tried nearly everything. Setup new Pycharm project try using different versions of numpy or sklearn.
### Steps/Code to Reproduce
```python
def... | 24,540 | [
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0.009449360892176628,
... |
https://github.com/scikit-learn/scikit-learn/issues/24540 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Exit Code -1073741819 when doing K-means++ clustering
### Describe the bug
Unfortunately I am getting an exit code in Pycharm when doing clustering with k-means++.
I tried nearly everything. Setup new Pycharm project try using different versions of numpy or sklearn.
### Steps/Code to Reproduce
```python
def... | 24,540 | [
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0.009449360892176628,
... |
https://github.com/scikit-learn/scikit-learn/issues/24540 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Exit Code -1073741819 when doing K-means++ clustering
### Describe the bug
Unfortunately I am getting an exit code in Pycharm when doing clustering with k-means++.
I tried nearly everything. Setup new Pycharm project try using different versions of numpy or sklearn.
### Steps/Code to Reproduce
```python
def... | 24,540 | [
0.0037060347385704517,
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-0.02681581862270832,
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0.10115109384059906,
0.009449360892176628,
... |
https://github.com/scikit-learn/scikit-learn/issues/24540 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Exit Code -1073741819 when doing K-means++ clustering
### Describe the bug
Unfortunately I am getting an exit code in Pycharm when doing clustering with k-means++.
I tried nearly everything. Setup new Pycharm project try using different versions of numpy or sklearn.
### Steps/Code to Reproduce
```python
def... | 24,540 | [
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0.10115109384059906,
0.009449360892176628,
... |
https://github.com/scikit-learn/scikit-learn/issues/24540 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Exit Code -1073741819 when doing K-means++ clustering
### Describe the bug
Unfortunately I am getting an exit code in Pycharm when doing clustering with k-means++.
I tried nearly everything. Setup new Pycharm project try using different versions of numpy or sklearn.
### Steps/Code to Reproduce
```python
def... | 24,540 | [
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0.10115109384059906,
0.009449360892176628,
... |
https://github.com/scikit-learn/scikit-learn/issues/24537 | [
"Bug",
"Needs Triage"
] | Segmentation error when calling .fit()
### Describe the bug
Hey all,
I'm currently busy working on a solution for a classification problem using LogisticRegression from sklearn.linear_model. I'm training multiple classifiers at the same time with the same hyperparameters and only slightly different input. The la... | 24,537 | [
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https://github.com/scikit-learn/scikit-learn/issues/24537 | [
"Bug",
"Needs Triage"
] | Segmentation error when calling .fit()
### Describe the bug
Hey all,
I'm currently busy working on a solution for a classification problem using LogisticRegression from sklearn.linear_model. I'm training multiple classifiers at the same time with the same hyperparameters and only slightly different input. The la... | 24,537 | [
-0.02289387956261635,
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0.015396698378026485,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/24537 | [
"Bug",
"Needs Triage"
] | Segmentation error when calling .fit()
### Describe the bug
Hey all,
I'm currently busy working on a solution for a classification problem using LogisticRegression from sklearn.linear_model. I'm training multiple classifiers at the same time with the same hyperparameters and only slightly different input. The la... | 24,537 | [
-0.02289387956261635,
0.020414777100086212,
0.015396698378026485,
0.0009626063401810825,
0.10527441650629044,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/24529 | [
"Question"
] | Saved model
Hi,
I have saved a model of RandomForestClassifier from previous version (0.21.3).
now, if i try to load it in a new version i get the following error: No module name 'sklearn.ensemble.forest'
How can I transfer my previous saved model to a new version?
COMMENT:
Hi @yana25,
Loading a model saved... | 24,529 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/24529 | [
"Question"
] | Saved model
Hi,
I have saved a model of RandomForestClassifier from previous version (0.21.3).
now, if i try to load it in a new version i get the following error: No module name 'sklearn.ensemble.forest'
How can I transfer my previous saved model to a new version?
COMMENT:
I am moving this issue into the disc... | 24,529 | [
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https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
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0.01821071095764637,
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0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24525 | [
"Build / CI"
] | Should we continue to support compiler=intelem?
I have an build refactor removing `distutils` and `numpy.disutils` and only uses `setuptools` that successfully builds our wheels and passes tests. I think it is best to move to a pure `setuptools` implementation first, because there are still some lingering issues `meso... | 24,525 | [
-0.02729422226548195,
0.10659090429544449,
-0.010229542851448059,
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0.01821071095764637,
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0.051885008811950684,
0.0028349095955491066,
0.026... |
https://github.com/scikit-learn/scikit-learn/issues/24524 | [
"New Feature",
"Needs Triage"
] | Add TQDM progress bar to .fit
### Describe the workflow you want to enable
There is no cohesive way of knowing when a classifier will finish training. What is shown by `verbose = True` is not consistent across models.
### Describe your proposed solution
I propose wrapping all most/all `.fit()` functions in tqdm.
... | 24,524 | [
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0.083... |
https://github.com/scikit-learn/scikit-learn/issues/24524 | [
"New Feature",
"Needs Triage"
] | Add TQDM progress bar to .fit
### Describe the workflow you want to enable
There is no cohesive way of knowing when a classifier will finish training. What is shown by `verbose = True` is not consistent across models.
### Describe your proposed solution
I propose wrapping all most/all `.fit()` functions in tqdm.
... | 24,524 | [
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https://github.com/scikit-learn/scikit-learn/issues/24519 | [
"Easy",
"API"
] | Deprecate the kwargs argument of utils.extmath.density
The function ``density`` from sklearn.utils.extmath accepts extra kwargs but completely ignore them. I suggest we deprecate this.
Here's a guide on how to proceed: https://scikit-learn.org/stable/developers/contributing.html#maintaining-backwards-compatibility
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https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
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https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
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0.04103328660130501,
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0.016894007101655006,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
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0.01638444885611534,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0016017029993236065,
0.03537857159972191,
0.05193355306982994,
0.00531810475513339,
0.08536046743392944,
-0.009058283641934395,
-0.01668669655919075,
-0.009357924573123455,
-0.05859535187482834,
0.02713468298316002,
0.023785462602972984,
-0.03839001804590225,
0.009472189471125603,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0046250540763139725,
0.010599758476018906,
0.04818801209330559,
0.0014351956779137254,
0.10299510508775711,
0.0016824444755911827,
-0.018019134178757668,
-0.0036461816634982824,
-0.03975697234272957,
0.024385668337345123,
0.023160461336374283,
-0.03199746832251549,
0.010600459761917591,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
0.0010539308423176408,
-0.002049485221505165,
0.041703931987285614,
0.01404626201838255,
0.09615826606750488,
0.0014864997938275337,
-0.014338837936520576,
-0.004799182992428541,
-0.042723968625068665,
0.021237455308437347,
0.0021780566312372684,
-0.015293782576918602,
0.012029074132442474,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0037049083039164543,
-0.0010433073621243238,
0.03738094121217728,
0.022672245278954506,
0.09332896023988724,
-0.003405614523217082,
-0.017444230616092682,
0.005623492877930403,
-0.0748104453086853,
0.016773823648691177,
0.025590157136321068,
-0.029352761805057526,
0.007981815375387669,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0076907044276595116,
0.0051020123064517975,
0.0493883341550827,
0.009232932701706886,
0.10492043942213058,
-0.005294399335980415,
-0.0105099156498909,
0.010309115052223206,
-0.06520003825426102,
0.02005355805158615,
0.024452488869428635,
-0.03588273748755455,
0.007513783872127533,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0016978653147816658,
0.005672984756529331,
0.03795620799064636,
0.010469433851540089,
0.10253804177045822,
0.0031430546659976244,
-0.013629262335598469,
-0.005276337265968323,
-0.04280134290456772,
0.015246374532580376,
0.011915202252566814,
-0.010367102921009064,
0.007734065875411034,
... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.003970173187553883,
0.009099853225052357,
0.04260847344994545,
0.01829659380018711,
0.09958775341510773,
0.0008855744381435215,
-0.013553109019994736,
-0.005531188100576401,
-0.045097559690475464,
0.010434220544993877,
-0.00045892639900557697,
-0.0178235974162817,
0.01031316164880991,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.0007863201899453998,
0.02093173749744892,
0.04744894057512283,
0.016236919909715652,
0.11734355241060257,
0.0023791182320564985,
-0.017149614170193672,
0.00937085971236229,
-0.060145217925310135,
0.016034631058573723,
0.00784947071224451,
-0.029058003798127174,
0.00580893037840724,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.007271854672580957,
0.015334189869463444,
0.043690275400877,
0.011100550182163715,
0.09900925308465958,
-0.00016782328020781279,
-0.011811008676886559,
0.0057870978489518166,
-0.05265307426452637,
0.01809374988079071,
0.02029062621295452,
-0.014385269954800606,
0.01172054372727871,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24515 | [
"Bug",
"help wanted",
"module:metrics"
] | BUG log_loss renormalizes the predictions
### Describe the bug
`log_loss(y_true, y_pred)` renormalizes `y_pred` internally such that it sums to 1. This way, a really bad model, the predictions of which do not sum to 1, gets a better loss then it actually has.
### Steps/Code to Reproduce
```python
from scipy.specia... | 24,515 | [
-0.00737348897382617,
0.010388510301709175,
0.043480925261974335,
0.012147199362516403,
0.09967438876628876,
-0.00022406259085983038,
-0.01068848092108965,
0.005514883436262608,
-0.05328775942325592,
0.01901596039533615,
0.0187276229262352,
-0.014890769496560097,
0.011490892618894577,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24508 | [
"Bug",
"Needs Triage"
] | Sparse random projection description is incorrect in docs
### Describe the bug
See: https://scikit-learn.org/stable/modules/generated/sklearn.random_projection.SparseRandomProjection.html#sklearn-random-projection-sparserandomprojection
The docs say that if s = 1 / density, then the weights for drawing the value... | 24,508 | [
0.02726871147751808,
-0.05442681908607483,
0.020092304795980453,
0.01707141473889351,
0.03115144744515419,
-0.02384878136217594,
0.02608036994934082,
0.0197849590331316,
-0.06660351157188416,
0.02597755566239357,
0.04101915657520294,
-0.0037479051388800144,
0.04509172961115837,
-0.01219694... |
https://github.com/scikit-learn/scikit-learn/issues/24507 | [
"New Feature"
] | Support usage of `predict_params` and `predict_proba_params` in cross validation
### Describe the workflow you want to enable
We can currently pass `predict_params` and `predict_proba_params` to `Pipeline`s, predictors, etc., at predict time when performing "manual" calls. When performing cross validation, however,... | 24,507 | [
-0.016091223806142807,
0.07131118327379227,
0.03405044600367546,
-0.0350046381354332,
0.03946423530578613,
-0.051650211215019226,
-0.01870768703520298,
0.0021278904750943184,
0.013068410567939281,
-0.013990162871778011,
0.02692270651459694,
0.03183285519480705,
-0.019678577780723572,
0.066... |
https://github.com/scikit-learn/scikit-learn/issues/24507 | [
"New Feature"
] | Support usage of `predict_params` and `predict_proba_params` in cross validation
### Describe the workflow you want to enable
We can currently pass `predict_params` and `predict_proba_params` to `Pipeline`s, predictors, etc., at predict time when performing "manual" calls. When performing cross validation, however,... | 24,507 | [
-0.016091223806142807,
0.07131118327379227,
0.03405044600367546,
-0.0350046381354332,
0.03946423530578613,
-0.051650211215019226,
-0.01870768703520298,
0.0021278904750943184,
0.013068410567939281,
-0.013990162871778011,
0.02692270651459694,
0.03183285519480705,
-0.019678577780723572,
0.066... |
https://github.com/scikit-learn/scikit-learn/issues/24505 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_ICC.pylatest_conda_forge_mkl ⚠️
**CI failed on [Linux_Nightly_ICC.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47016&view=logs&j=8628a494-79d0-53fa-274c-1b00464f7121)** (Sep 24, 2022)
Unable to find junit file. Please see link for detail... | 24,505 | [
-0.005785264074802399,
0.016214318573474884,
-0.0444205142557621,
-0.055467389523983,
0.010606060735881329,
0.015553937293589115,
0.025381941348314285,
0.05640920624136925,
0.011247945949435234,
0.022993018850684166,
0.0241978969424963,
0.03398391976952553,
-0.015717219561338425,
0.0666106... |
https://github.com/scikit-learn/scikit-learn/issues/24505 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_ICC.pylatest_conda_forge_mkl ⚠️
**CI failed on [Linux_Nightly_ICC.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47016&view=logs&j=8628a494-79d0-53fa-274c-1b00464f7121)** (Sep 24, 2022)
Unable to find junit file. Please see link for detail... | 24,505 | [
-0.027604475617408752,
0.01691676303744316,
-0.03696022927761078,
-0.04504168778657913,
0.027190303429961205,
0.0034740741830319166,
0.05084197223186493,
0.042362239211797714,
0.012132286094129086,
0.025206612423062325,
0.02172086201608181,
0.025934049859642982,
-0.014248568564653397,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24502 | [
"RFC",
"module:metrics"
] | RFC Should pairwise_distances preserve float32 ?
Currently the dtype of the distance matrix returned by `pairwise_distances` is not very consistent, depending on the metric and on the value of n_jobs.
For float64 input, everything is consistent: the returned matrix is always in float64.
For mixed float64 X and flo... | 24,502 | [
-0.05953368917107582,
0.02346855401992798,
0.025197353214025497,
0.02717384696006775,
0.012895402498543262,
0.008125616237521172,
0.09721649438142776,
0.03079191781580448,
-0.011295403353869915,
-0.019284963607788086,
-0.0035454370081424713,
-0.04822590574622154,
0.04060013219714165,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24502 | [
"RFC",
"module:metrics"
] | RFC Should pairwise_distances preserve float32 ?
Currently the dtype of the distance matrix returned by `pairwise_distances` is not very consistent, depending on the metric and on the value of n_jobs.
For float64 input, everything is consistent: the returned matrix is always in float64.
For mixed float64 X and flo... | 24,502 | [
-0.05953368917107582,
0.02346855401992798,
0.025197353214025497,
0.02717384696006775,
0.012895402498543262,
0.008125616237521172,
0.09721649438142776,
0.03079191781580448,
-0.011295403353869915,
-0.019284963607788086,
-0.0035454370081424713,
-0.04822590574622154,
0.04060013219714165,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24502 | [
"RFC",
"module:metrics"
] | RFC Should pairwise_distances preserve float32 ?
Currently the dtype of the distance matrix returned by `pairwise_distances` is not very consistent, depending on the metric and on the value of n_jobs.
For float64 input, everything is consistent: the returned matrix is always in float64.
For mixed float64 X and flo... | 24,502 | [
-0.05953368917107582,
0.02346855401992798,
0.025197353214025497,
0.02717384696006775,
0.012895402498543262,
0.008125616237521172,
0.09721649438142776,
0.03079191781580448,
-0.011295403353869915,
-0.019284963607788086,
-0.0035454370081424713,
-0.04822590574622154,
0.04060013219714165,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24502 | [
"RFC",
"module:metrics"
] | RFC Should pairwise_distances preserve float32 ?
Currently the dtype of the distance matrix returned by `pairwise_distances` is not very consistent, depending on the metric and on the value of n_jobs.
For float64 input, everything is consistent: the returned matrix is always in float64.
For mixed float64 X and flo... | 24,502 | [
-0.05953368917107582,
0.02346855401992798,
0.025197353214025497,
0.02717384696006775,
0.012895402498543262,
0.008125616237521172,
0.09721649438142776,
0.03079191781580448,
-0.011295403353869915,
-0.019284963607788086,
-0.0035454370081424713,
-0.04822590574622154,
0.04060013219714165,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24501 | [
"Documentation"
] | plot_learning_curve.py should not sort the fit time axis before plotting
Dears,
About 10 months ago, the `plot_learning_curve.py` example was changed by Mr. @thomasjpfan to sort the `fit_time` plot axis.
In my humble opinion, that's wrong because a learning curve is train-size ascending regardless the time it sp... | 24,501 | [
-0.04452739283442497,
0.023414960131049156,
0.01718916930258274,
0.011932630091905594,
0.028344737365841866,
-0.002365082036703825,
0.03321392089128494,
0.028232824057340622,
-0.01638958789408207,
0.015017970465123653,
0.06572620570659637,
0.027118878439068794,
0.026688454672694206,
0.0275... |
https://github.com/scikit-learn/scikit-learn/issues/24501 | [
"Documentation"
] | plot_learning_curve.py should not sort the fit time axis before plotting
Dears,
About 10 months ago, the `plot_learning_curve.py` example was changed by Mr. @thomasjpfan to sort the `fit_time` plot axis.
In my humble opinion, that's wrong because a learning curve is train-size ascending regardless the time it sp... | 24,501 | [
-0.056577168405056,
0.02759694866836071,
-0.0002141898003173992,
0.012724768370389938,
0.04846988990902901,
-0.005169839132577181,
0.05100320652127266,
0.039199698716402054,
-0.0093679279088974,
0.004646732471883297,
0.05199054628610611,
0.04039203003048897,
0.007952742278575897,
0.0487900... |
https://github.com/scikit-learn/scikit-learn/issues/24501 | [
"Documentation"
] | plot_learning_curve.py should not sort the fit time axis before plotting
Dears,
About 10 months ago, the `plot_learning_curve.py` example was changed by Mr. @thomasjpfan to sort the `fit_time` plot axis.
In my humble opinion, that's wrong because a learning curve is train-size ascending regardless the time it sp... | 24,501 | [
-0.055602189153432846,
0.040083639323711395,
0.017953313887119293,
0.015389797277748585,
0.05992071330547333,
0.0004904071683995426,
0.0321304015815258,
0.056397996842861176,
0.007827048189938068,
0.018100621178746223,
0.027039416134357452,
0.044333092868328094,
0.009969794191420078,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/24501 | [
"Documentation"
] | plot_learning_curve.py should not sort the fit time axis before plotting
Dears,
About 10 months ago, the `plot_learning_curve.py` example was changed by Mr. @thomasjpfan to sort the `fit_time` plot axis.
In my humble opinion, that's wrong because a learning curve is train-size ascending regardless the time it sp... | 24,501 | [
-0.045747242867946625,
0.044518060982227325,
0.0071708024479448795,
0.01024483609944582,
0.04917442426085472,
-0.006845297757536173,
0.04055923596024513,
0.05631237104535103,
-0.006582132540643215,
0.016824770718812943,
0.05660146102309227,
0.032801780849695206,
0.0014463405823335052,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24501 | [
"Documentation"
] | plot_learning_curve.py should not sort the fit time axis before plotting
Dears,
About 10 months ago, the `plot_learning_curve.py` example was changed by Mr. @thomasjpfan to sort the `fit_time` plot axis.
In my humble opinion, that's wrong because a learning curve is train-size ascending regardless the time it sp... | 24,501 | [
-0.0434701107442379,
0.021621236577630043,
0.016141928732395172,
0.004114591982215643,
0.0388820581138134,
0.0036404766142368317,
0.03705204278230667,
0.045123063027858734,
-0.008717883378267288,
0.012589750811457634,
0.05988225340843201,
0.019969517365098,
0.013584643602371216,
0.03735591... |
https://github.com/scikit-learn/scikit-learn/issues/24500 | [
"Needs Triage"
] | learning_curve() returning wrong (accumulated) times across parallel n_jobs
When running `learning_curve()` with parallel processing (`n_jobs` > 1) it wrongly returns `fit_times` and `score_times` as sums of their respective duration across all parallel jobs of `_fit_and_score()` rather than a meaningful, let's say, a... | 24,500 | [
-0.07094882428646088,
0.01216168887913227,
0.027118228375911713,
0.04440387710928917,
0.026535624638199806,
-0.02253608964383602,
0.020272862166166306,
-0.012396886013448238,
-0.030102549120783806,
0.013015111908316612,
0.03279251977801323,
0.011332829482853413,
0.039635781198740005,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24499 | [
"Documentation",
"Needs Triage"
] | Reference for sklearn.feature_selection.chi2
### Describe the issue linked to the documentation
Hi folks,
I am somewhat in doubt that the `sklearn.feature_selection.chi2` function is implemented correctly. At least, checking the source code, it is entirely unclear to me why that kind of scoring would make sense.... | 24,499 | [
-0.020321743562817574,
-0.02307790331542492,
0.007809677626937628,
-0.026422014459967613,
-0.04948713630437851,
0.026623237878084183,
0.07596182823181152,
-0.013984555378556252,
-0.006585007067769766,
0.007699036970734596,
0.036703892052173615,
0.028134256601333618,
0.09521610289812088,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.11... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.09... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.120... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.12429225... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.120166... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.098... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.11743... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.09200392663478851,
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0.1175196... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.10867... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.04178089275956154,
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0.1070... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.07643239945173264,
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0.1151... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.08280498534440994,
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0.12445... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.0797586515545845,
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0.017579732462763786,
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0.01105986163020134,
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0.1296425610... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.07839199155569077,
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0.018944241106510162,
0.09060376137495041,
0.011711078695952892,
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0.09993... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
-0.051999956369400024,
0.06907343119382858,
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0.08300811052322388,
0.006997162010520697,
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0.10... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.09... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.106000... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.007494715042412281,
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0.11178787... |
https://github.com/scikit-learn/scikit-learn/issues/24491 | [
"Build / CI",
"help wanted",
"Array API"
] | Weekly CI run with NVidia GPU hardware
Now that #22554 was merged in `main`, it would be great to find a a way to run a weekly scheduled job to run the scikit-learn `main` test on a CI worker with an NVidia GPU and CuPy.
In case of failure, it could create a report as [dedicated issues](https://github.com/scikit-l... | 24,491 | [
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0.08290465176105499,
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0.006365960463881493,
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0.02376631461083889,
-0.03131384029984474,
0.112... |
https://github.com/scikit-learn/scikit-learn/issues/24490 | [
"New Feature",
"module:compose"
] | add **fit_params to sklearn.compose.ColumnTransformer().fit()
### Describe the workflow you want to enable
The `fit` function of both [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline).Pipeline and [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#... | 24,490 | [
-0.03144370764493942,
0.030022285878658295,
0.03381869196891785,
-0.03194700926542282,
0.07314532995223999,
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0.03597801923751831,
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-0.02410542219877243,
0.022877737879753113,
0.015807850286364555,
0.03145068883895874,
0.08624... |
https://github.com/scikit-learn/scikit-learn/issues/24486 | [
"Bug",
"module:model_selection"
] | GroupShuffleSplit chokes on pd.Int16Dtype() with a cryptic error
### Describe the bug
`GroupShuffleSplit` chokes on `pd.Int16Dtype()` with a cryptic error.
It looks like internally the data series gets converted to a list, and list comparison returns a scalar, while an iterable is expected
### Steps/Code to Rep... | 24,486 | [
-0.014065383933484554,
0.0017926975851878524,
-0.006550670601427555,
0.029042622074484825,
0.05822744593024254,
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0.09195252507925034,
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0.007760469801723957,
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0.009386236779391766,
-0.022253619506955147,
0.019619781523942947,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24486 | [
"Bug",
"module:model_selection"
] | GroupShuffleSplit chokes on pd.Int16Dtype() with a cryptic error
### Describe the bug
`GroupShuffleSplit` chokes on `pd.Int16Dtype()` with a cryptic error.
It looks like internally the data series gets converted to a list, and list comparison returns a scalar, while an iterable is expected
### Steps/Code to Rep... | 24,486 | [
-0.014065383933484554,
0.0017926975851878524,
-0.006550670601427555,
0.029042622074484825,
0.05822744593024254,
0.03378739580512047,
0.09195252507925034,
0.048099126666784286,
0.007760469801723957,
-0.04539356753230095,
0.009386236779391766,
-0.022253619506955147,
0.019619781523942947,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24486 | [
"Bug",
"module:model_selection"
] | GroupShuffleSplit chokes on pd.Int16Dtype() with a cryptic error
### Describe the bug
`GroupShuffleSplit` chokes on `pd.Int16Dtype()` with a cryptic error.
It looks like internally the data series gets converted to a list, and list comparison returns a scalar, while an iterable is expected
### Steps/Code to Rep... | 24,486 | [
-0.014065383933484554,
0.0017926975851878524,
-0.006550670601427555,
0.029042622074484825,
0.05822744593024254,
0.03378739580512047,
0.09195252507925034,
0.048099126666784286,
0.007760469801723957,
-0.04539356753230095,
0.009386236779391766,
-0.022253619506955147,
0.019619781523942947,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24469 | [
"Documentation"
] | DOC Mention pandas dataframe support in `ColumnTransformer` in FAQ
### Describe the issue linked to the documentation
FAQ question: [Why does Scikit-learn not directly work with, for example, pandas.DataFrame?](https://scikit-learn.org/stable/faq.html#why-does-scikit-learn-not-directly-work-with-for-example-pandas-da... | 24,469 | [
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0.04941128194332123,
0.019871072843670845,
0.004437593277543783,
0.0556... |
https://github.com/scikit-learn/scikit-learn/issues/24469 | [
"Documentation"
] | DOC Mention pandas dataframe support in `ColumnTransformer` in FAQ
### Describe the issue linked to the documentation
FAQ question: [Why does Scikit-learn not directly work with, for example, pandas.DataFrame?](https://scikit-learn.org/stable/faq.html#why-does-scikit-learn-not-directly-work-with-for-example-pandas-da... | 24,469 | [
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0.04549407958984375,
0.025540778413414955,
0.009831174276769161,
0.0587... |
https://github.com/scikit-learn/scikit-learn/issues/24469 | [
"Documentation"
] | DOC Mention pandas dataframe support in `ColumnTransformer` in FAQ
### Describe the issue linked to the documentation
FAQ question: [Why does Scikit-learn not directly work with, for example, pandas.DataFrame?](https://scikit-learn.org/stable/faq.html#why-does-scikit-learn-not-directly-work-with-for-example-pandas-da... | 24,469 | [
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0.0831351950764656,
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0.05019338056445122,
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0.05440186709165573,
0.019986575469374657,
0.010703939013183117,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/24469 | [
"Documentation"
] | DOC Mention pandas dataframe support in `ColumnTransformer` in FAQ
### Describe the issue linked to the documentation
FAQ question: [Why does Scikit-learn not directly work with, for example, pandas.DataFrame?](https://scikit-learn.org/stable/faq.html#why-does-scikit-learn-not-directly-work-with-for-example-pandas-da... | 24,469 | [
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0.08972771465778351,
0.021247098222374916,
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0.05034364387392998,
0.03623148798942566,
0.11537584662437439,
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0.04532553628087044,
0.01797807402908802,
0.007785772904753685,
0.0469... |
https://github.com/scikit-learn/scikit-learn/issues/24464 | [
"Documentation"
] | DOC See Also descriptions do not match for multiple functions/classes
### Describe the issue linked to the documentation
While working on a docstring-related pull request (#24259) I noticed that, sometimes, the See Also description for the same function/class does not match. For instance, the `accuracy_score` descrip... | 24,464 | [
0.06971058249473572,
0.03187812864780426,
-0.007129993289709091,
0.02581307664513588,
0.04837191477417946,
0.03171135112643242,
0.024113954976201057,
0.02013634890317917,
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0.002701721852645278,
-0.0014937723753973842,
0.05211576074361801,
0.01598... |
https://github.com/scikit-learn/scikit-learn/issues/24464 | [
"Documentation"
] | DOC See Also descriptions do not match for multiple functions/classes
### Describe the issue linked to the documentation
While working on a docstring-related pull request (#24259) I noticed that, sometimes, the See Also description for the same function/class does not match. For instance, the `accuracy_score` descrip... | 24,464 | [
0.06971058249473572,
0.03187812864780426,
-0.007129993289709091,
0.02581307664513588,
0.04837191477417946,
0.03171135112643242,
0.024113954976201057,
0.02013634890317917,
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-0.06298404932022095,
0.002701721852645278,
-0.0014937723753973842,
0.05211576074361801,
0.01598... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06651230156421661,
0.05477592721581459,
-0.007496490143239498,
0.0021068539936095476,
-0.04386930167675018,
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0.007261873222887516,
0.06521455943584442,
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-0.02882484532892704,
0.019795188680291176,
0.02931726910173893,
-0.021357208490371704,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06635048240423203,
0.05638471245765686,
-0.0076735797338187695,
0.0015063376631587744,
-0.042802199721336365,
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0.0055278330110013485,
0.06572078913450241,
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0.017174452543258667,
0.03154139220714569,
-0.01929677277803421,
... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06602486968040466,
0.060006652027368546,
0.0007663810392841697,
0.00624697282910347,
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0.013286279514431953,
0.042241375893354416,
-0.016823377460241318,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06536474078893661,
0.060623906552791595,
0.0010514515452086926,
0.008939048275351524,
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0.01392684131860733,
0.035909105092287064,
-0.019450126215815544,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06958689540624619,
0.06252027302980423,
-0.009835951961576939,
0.004259712062776089,
-0.03140048682689667,
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0.01642175018787384,
0.04194347932934761,
-0.023503001779317856,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.06026566028594971,
0.06355223804712296,
0.005074616055935621,
0.005426240153610706,
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0.008135071024298668,
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0.022012168541550636,
0.0466231144964695,
-0.024653319269418716,
0.018... |
https://github.com/scikit-learn/scikit-learn/issues/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
-0.057031698524951935,
0.0599374920129776,
0.0036934996023774147,
0.008008946664631367,
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0.01897640898823738,
0.04414563998579979,
-0.01582387275993824,
0.0... |
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