html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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
... | 24,519 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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