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/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 |
https://github.com/scikit-learn/scikit-learn/issues/25571 | [
"Bug"
] | Bug in Calibration Curve Documentation
### Describe the bug
https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html
In the calibration curve page, a "scores_df" is generated to showcase supporting model evaluation metrics in addition to the calibration curves.
I noticed that my ROC... | 25,571 |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 |
https://github.com/scikit-learn/scikit-learn/issues/25565 | [
"Documentation",
"Moderate",
"Build / CI"
] | High level documentation of the CI infrastructure
As originally discussed in https://github.com/scikit-learn/scikit-learn/pull/25562#discussion_r1098396646:
I think it might be helpful to give a high level description of our CI somewhere in the doc, both for new contributors and maintainers. In particular, we shoul... | 25,565 |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 |
https://github.com/scikit-learn/scikit-learn/issues/25564 | [
"workflow"
] | Streamlining Bug Fix Releases
Reading over https://github.com/scikit-learn/scikit-learn/pull/25457 I wish we had workflow where we can immediately backport fixes to `1.2.X` once the fix is on `main`. This way we do not need to do a big interactive rebase when we release. We would only need to update the authors list a... | 25,564 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25560 | [
"Bug",
"module:impute",
"Needs Decision - Include Feature"
] | set_output API do not preserve original dtypes for pandas
### Describe the bug
Following issue #24182,
When using the set_output with expected output to be a pandas' data frame, while converting tougher columns with different dtypes the output does not preserve the original dtype but the "common type" by numpy.
... | 25,560 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25552 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Implement beta calibration
### Describe the workflow you want to enable
It would be nice to implement beta calibration as an additional option in CalibratedClassifierCV.
### Describe your proposed solution
Use the implementation provided in https://github.com/betacal/python (MIT license).
### Describe alternatives... | 25,552 |
https://github.com/scikit-learn/scikit-learn/issues/25550 | [
"Bug",
"module:preprocessing"
] | OneHotEncoder `drop_idx_` attribute description in presence of infrequent categories
### Describe the issue linked to the documentation
### Issue summary
In the OneHotEncoder documentation both for [v1.2](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preproces... | 25,550 |
https://github.com/scikit-learn/scikit-learn/issues/25550 | [
"Bug",
"module:preprocessing"
] | OneHotEncoder `drop_idx_` attribute description in presence of infrequent categories
### Describe the issue linked to the documentation
### Issue summary
In the OneHotEncoder documentation both for [v1.2](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preproces... | 25,550 |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 |
https://github.com/scikit-learn/scikit-learn/issues/25539 | [
"Documentation"
] | documentation of k-means param n_init isn't worded nicely for people unfamiliar with the implementation
### Describe the issue linked to the documentation
Currently the doc says:
> When n_init='auto', the number of runs will be 10 if using init='random', and 1 if using init='kmeans++'.
in https://scikit-learn... | 25,539 |
https://github.com/scikit-learn/scikit-learn/issues/25534 | [
"Bug",
"Needs Triage"
] | `_check_unknown` returns error for `np.isnan(known_values)` with int64 arrays
### Describe the bug
When `precision_score` is called with two numpy int64 arrays as y_true and y_pred, an error is thrown in the `_check_unknown` function in [sklearn](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn)/[utils]... | 25,534 |
https://github.com/scikit-learn/scikit-learn/issues/25534 | [
"Bug",
"Needs Triage"
] | `_check_unknown` returns error for `np.isnan(known_values)` with int64 arrays
### Describe the bug
When `precision_score` is called with two numpy int64 arrays as y_true and y_pred, an error is thrown in the `_check_unknown` function in [sklearn](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn)/[utils]... | 25,534 |
https://github.com/scikit-learn/scikit-learn/issues/25533 | [
"Bug",
"Needs Triage"
] | Error while installing DeepLabCut: Collecting scikit-learn>=1.0
### Describe the bug
I'm trying to install DeeplLabCut and was encountering an error.
The devs guided me over here to as it seems to be an error while installing scikit-learn.
Issue for reference: https://github.com/DeepLabCut/DeepLabCut/issues/2139
... | 25,533 |
https://github.com/scikit-learn/scikit-learn/issues/25533 | [
"Bug",
"Needs Triage"
] | Error while installing DeepLabCut: Collecting scikit-learn>=1.0
### Describe the bug
I'm trying to install DeeplLabCut and was encountering an error.
The devs guided me over here to as it seems to be an error while installing scikit-learn.
Issue for reference: https://github.com/DeepLabCut/DeepLabCut/issues/2139
... | 25,533 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25532 | [
"Bug"
] | `pairwise_distances` is inconsistent with `scipy.spatial.distance` when using `metric="matching"`
### Describe the bug
Although the metric `matching` is already removed from the documentation, `pairwise_distances` function still allows its usage. When used, the input arrays are converted into boolean. This brings i... | 25,532 |
https://github.com/scikit-learn/scikit-learn/issues/25529 | [
"New Feature",
"Needs Triage"
] | quantum kernel with scikit -learn
### Describe the workflow you want to enable
I have designed a quantum kernel function with Pennylane quantum simulator. When i want to use Gaussian process for classification in combination with the quantum kernel i encountered this problem:
```py
AttributeError: 'function' o... | 25,529 |
https://github.com/scikit-learn/scikit-learn/issues/25529 | [
"New Feature",
"Needs Triage"
] | quantum kernel with scikit -learn
### Describe the workflow you want to enable
I have designed a quantum kernel function with Pennylane quantum simulator. When i want to use Gaussian process for classification in combination with the quantum kernel i encountered this problem:
```py
AttributeError: 'function' o... | 25,529 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25527 | [
"Bug",
"module:cluster"
] | KMeans initialization does not use sample weights
### Describe the bug
Clustering by KMeans does not weight the input data.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.cluster import KMeans
x = np.array([1, 1, 5, 5, 100, 100])
w = 10**np.array([8.,8,8,8,-8,-8]) # large weights for 1 ... | 25,527 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25525 | [
"Bug",
"module:feature_extraction"
] | Extend SequentialFeatureSelector example to demonstrate how to use negative tol
### Describe the bug
I utilized the **SequentialFeatureSelector** for feature selection in my code, with the direction set to "backward." The tolerance value is negative and the selection process stops when the decrease in the metric, A... | 25,525 |
https://github.com/scikit-learn/scikit-learn/issues/25522 | [
"RFC"
] | Behaviour of `warm_start=True` and `max_iter` (and `n_estimators`)
This issue is an RFC to clarify the expected behavior `max_iter` and `n_iter_` (or `estimators` and `len(estimators_)` equivalently) when used with `warm_start=True`.
### Estimators to be considered
The estimators to be considered can be found in... | 25,522 |
https://github.com/scikit-learn/scikit-learn/issues/25522 | [
"RFC"
] | Behaviour of `warm_start=True` and `max_iter` (and `n_estimators`)
This issue is an RFC to clarify the expected behavior `max_iter` and `n_iter_` (or `estimators` and `len(estimators_)` equivalently) when used with `warm_start=True`.
### Estimators to be considered
The estimators to be considered can be found in... | 25,522 |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 |
https://github.com/scikit-learn/scikit-learn/issues/25519 | [
"Bug"
] | empirical_covariance silently returns invalid results on inputs with a complex dtype
### Describe the bug
Considering complex inputs $X$, like in [radar image processing](https://ammarmian.github.io/pdf/wiley_book_2021.pdf), we want to estimate the covariance matrix.
When `assume_centered=True`, `empirical_covaria... | 25,519 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25505 | [
"Bug"
] | Bisecting Kmeans fails to bisect a certain cluster
### Describe the bug
Hi all,
I'm using the `sklearn.cluster.BisectingKMeans` to perform a clustering, and it worked for a range of k values, until it failed at k=9 (I don't think the k-value is important though). The issue seems to be that it failed to split a c... | 25,505 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25499 | [
"Bug"
] | CalibratedClassifierCV doesn't work with `set_config(transform_output="pandas")`
### Describe the bug
CalibratedClassifierCV with isotonic regression doesn't work when we previously set `set_config(transform_output="pandas")`.
The IsotonicRegression seems to return a dataframe, which is a problem for `_CalibratedC... | 25,499 |
https://github.com/scikit-learn/scikit-learn/issues/25497 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073)** (Jan 27, 2023)
COMMENT:
It looks like the failure was spurious. I reran the failing job. Let's see. | 25,497 |
https://github.com/scikit-learn/scikit-learn/issues/25497 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073)** (Jan 27, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/4021364073) on Jan 27, 2023 | 25,497 |
https://github.com/scikit-learn/scikit-learn/issues/25496 | [
"Bug",
"Needs Triage"
] | Partial Dependence Plot orients differently compared to Partial Dependence values
### Describe the bug
The issue is that the 2D partial dependence plot from scikit-learn orients in a different way that what you would get using raw pdp values from sklearn as well.
### Steps/Code to Reproduce
```python
imp... | 25,496 |
https://github.com/scikit-learn/scikit-learn/issues/25496 | [
"Bug",
"Needs Triage"
] | Partial Dependence Plot orients differently compared to Partial Dependence values
### Describe the bug
The issue is that the 2D partial dependence plot from scikit-learn orients in a different way that what you would get using raw pdp values from sklearn as well.
### Steps/Code to Reproduce
```python
imp... | 25,496 |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 |
https://github.com/scikit-learn/scikit-learn/issues/25495 | [
"Bug",
"Needs Triage"
] | Feature scaling affects decision tree predictions (it shouldn't affect according to the theory)
### Describe the bug
[data.csv](https://github.com/scikit-learn/scikit-learn/files/10513429/data.csv)
Here is the dataset example with one feature and one target.
According to the dacision tree algorithm decision tree ... | 25,495 |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 |
https://github.com/scikit-learn/scikit-learn/issues/25492 | [
"Bug"
] | Enable feature selectors to pass pandas DataFrame to estimator
### Describe the workflow you want to enable
When running SequentialFeatureSelector (or, presumably, other feature selection methods) with a pandas DataFrame input, the reduced-feature input is passed to the estimator as a numpy array. This seems incons... | 25,492 |
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