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/29567 | [
"Bug"
] | ⚠️ CI failed on Wheel builder (last failure: Jul 27, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10120613947)** (Jul 27, 2024)
COMMENT:
For further reference, there was a aimilar bus error on July 27 for cp311-macosx_arm64 and cp312-macosx_arm64 see [bui... | 29,567 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 |
https://github.com/scikit-learn/scikit-learn/issues/29556 | [
"Bug",
"Needs Triage"
] | Sklearn metric module - mean squared error
### Describe the bug
```python
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
axis_X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]).reshape(-1, 1)
axis_X_train = ax... | 29,556 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 |
https://github.com/scikit-learn/scikit-learn/issues/29547 | [
"Documentation"
] | GridSearchCV support for 'precomputed' kernel not documented
### Describe the issue linked to the documentation
GridSearchCV seems to work even with a precomputed kernel but there is nothing about it in the documentation. Is there a reason for this or did it just go unnoticed?
### Suggest a potential alternative/fix... | 29,547 |
https://github.com/scikit-learn/scikit-learn/issues/29547 | [
"Documentation"
] | GridSearchCV support for 'precomputed' kernel not documented
### Describe the issue linked to the documentation
GridSearchCV seems to work even with a precomputed kernel but there is nothing about it in the documentation. Is there a reason for this or did it just go unnoticed?
### Suggest a potential alternative/fix... | 29,547 |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 |
https://github.com/scikit-learn/scikit-learn/issues/29543 | [
"Documentation"
] | "Choosing the right estimator"-widget links broken
### Describe the bug
The links in the helper graph (think it's called machine learning map) to guide choosing an estimator (link: https://scikit-learn.org/stable/machine_learning_map.html#ml-map) is broken - the links are not up-to-date to reflect the url-structure... | 29,543 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 |
https://github.com/scikit-learn/scikit-learn/issues/29533 | [
"New Feature",
"Needs Triage"
] | Add FN and FP weight parameter in MCC
### Describe the workflow you want to enable
Introducing a weight parameter for false negatives (FN) and false positives (FP) in Matthews Correlation Coefficient (MCC) would enhance the metric’s flexibility and applicability, particularly in contexts where the costs of differen... | 29,533 |
https://github.com/scikit-learn/scikit-learn/issues/29533 | [
"New Feature",
"Needs Triage"
] | Add FN and FP weight parameter in MCC
### Describe the workflow you want to enable
Introducing a weight parameter for false negatives (FN) and false positives (FP) in Matthews Correlation Coefficient (MCC) would enhance the metric’s flexibility and applicability, particularly in contexts where the costs of differen... | 29,533 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 |
https://github.com/scikit-learn/scikit-learn/issues/29523 | [
"Bug",
"Needs Triage"
] | KNNImputer - output shape not equal input shape
### Describe the bug
The output of the fit_tranform is not equal to the input shape, when the NaN's are all in one column
### Steps/Code to Reproduce
```
from sklearn.impute import KNNImputer
input = np.random.rand(5, 5)
input[0,4]=np.nan
input[1,4]=np.nan
input... | 29,523 |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 |
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