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/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 |
https://github.com/scikit-learn/scikit-learn/issues/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 |
https://github.com/scikit-learn/scikit-learn/issues/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 |
https://github.com/scikit-learn/scikit-learn/issues/30212 | [
"Documentation",
"Needs Triage"
] | Missing documentation on ConvergenceWarning?
### Describe the issue linked to the documentation
Hi!
I was looking to know more about the convergence warning, I found [this link](https://scikit-learn.org/1.5/modules/generated/sklearn.exceptions.ConvergenceWarning.html), which redirects towards sklearn.utils. However,... | 30,212 |
https://github.com/scikit-learn/scikit-learn/issues/30212 | [
"Documentation",
"Needs Triage"
] | Missing documentation on ConvergenceWarning?
### Describe the issue linked to the documentation
Hi!
I was looking to know more about the convergence warning, I found [this link](https://scikit-learn.org/1.5/modules/generated/sklearn.exceptions.ConvergenceWarning.html), which redirects towards sklearn.utils. However,... | 30,212 |
https://github.com/scikit-learn/scikit-learn/issues/30199 | [
"New Feature",
"Needs Triage"
] | Add "mish" activation function to sklearn.neural_network.MLPClassifier and make it the default
### Describe the workflow you want to enable
Currently, the default activation function for `sklearn.neural_network.MLPClassifier` is "relu". However, there are several papers that demonstrate better results with "mish" =... | 30,199 |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
+1
On Nov 1, 2024, 21:50, at ... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I'm +0.5
On the (-) side, I... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I would say I'm +0.5.
Froze... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
My argument is similar to what... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
How about `sklearn.frozen.Free... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
`FrozenModel`? Everything's a ... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I agree with @adrinjalali's in... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I would actually prefer `Freez... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
https://github.com/scikit-lear... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
First reaction wise I like `Fr... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
> Also Frozen(my_random_forest... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I also prefer `Frozen(Estimato... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
https://github.com/scikit-lear... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I'm okay with the current `Fro... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
Ok then, I guess we're settled... | 30,194 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 |
https://github.com/scikit-learn/scikit-learn/issues/30183 | [
"Documentation",
"Needs Investigation"
] | The Affinity Matrix Is NON-BINARY with`affinity="precomputed_nearest_neighbors"`
### Describe the issue linked to the documentation
## Issue Source:
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/sklearn/cluster/_spectral.py#L452-L454
## Issue Description
The Aff... | 30,183 |
https://github.com/scikit-learn/scikit-learn/issues/30183 | [
"Documentation",
"Needs Investigation"
] | The Affinity Matrix Is NON-BINARY with`affinity="precomputed_nearest_neighbors"`
### Describe the issue linked to the documentation
## Issue Source:
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/sklearn/cluster/_spectral.py#L452-L454
## Issue Description
The Aff... | 30,183 |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 |
https://github.com/scikit-learn/scikit-learn/issues/30180 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line: https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L161
there is a reference attached to "Enhancement proposals (SLEPs)." ... | 30,180 |
https://github.com/scikit-learn/scikit-learn/issues/30180 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line: https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L161
there is a reference attached to "Enhancement proposals (SLEPs)." ... | 30,180 |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 |
https://github.com/scikit-learn/scikit-learn/issues/30161 | [
"Needs Info"
] | Refactor _check_partial_fit_first_call to separate validation from state modification
### Describe the workflow you want to enable
This change aims to improve the architectural design of `partial_fit` classes validation by separating the validation logic from state modification. This will make the code more maintaina... | 30,161 |
https://github.com/scikit-learn/scikit-learn/issues/30161 | [
"Needs Info"
] | Refactor _check_partial_fit_first_call to separate validation from state modification
### Describe the workflow you want to enable
This change aims to improve the architectural design of `partial_fit` classes validation by separating the validation logic from state modification. This will make the code more maintaina... | 30,161 |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 |
https://github.com/scikit-learn/scikit-learn/issues/30159 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Oct 27, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/11537349026)** (Oct 27, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/11546977899) on Oct 28... | 30,159 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 |
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