Talip7/scikit-learn-issues-multilabel huggingface dataset:
I can not achieve inplace scaling by using sklearn.preprocessing.minmax_scale

### Describe the bug

## By setting the copy=False, ndarray data has not changed unexpectedly
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/78b636a1-1556-4f79-a5be-7f683ba45094)

### Steps/Code to Reproduce

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.preprocessing as pre

np.random.seed(10)
data = np.random.randint(1, 10, size=(5, 3))
print(data)
pre.minmax_scale(data, feature_range=(0, 1), axis=0, copy=False)
print(data)
```

### Expected Results

## A reasonable explanation about the copy parameter of minmax_scala funciton

### Actual Results

# There are no warings and errors, just the result is not wrong!
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/1fdcc7df-f763-41bc-8e58-bd4f0e856b91)


### Versions

```shell
Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 65.5.0
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0
```

COMMENT:
> After [this line](https://github.com/scikit-learn/scikit-learn/blob/a31e1085e13af88a22d6c983192d54a58eaa845b/sklearn/preprocessing/_data.py#L649) There is a new instance of X and the data type of new one is float. So maybe you should modify your code to below. `data=pre.minmax_scale(data, feature_range=(0, 1), axis=0, copy=False)`
## Thanks for you reply, but I think there should't return anything of the funtion and just make the data normalized inplacely.
## After I set the dtype of data to np.float, data is normalized inplace, but still return a useless copy of data, i.e., data_copy, this does make nonse and not consistent with the official doc" copy =False would avoid a copy of data".
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/2c16be04-7529-4aee-8f94-33c84bbc5d92)
## Official doc about the copy parameter of function minmax_scale
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/3ba97972-4177-46f4-b753-81817cb54485)

Labels: Documentation
I can not achieve inplace scaling by using sklearn.preprocessing.minmax_scale

### Describe the bug

## By setting the copy=False, ndarray data has not changed unexpectedly
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/78b636a1-1556-4f79-a5be-7f683ba45094)

### Steps/Code to Reproduce

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.preprocessing as pre

np.random.seed(10)
data = np.random.randint(1, 10, size=(5, 3))
print(data)
pre.minmax_scale(data, feature_range=(0, 1), axis=0, copy=False)
print(data)
```

### Expected Results

## A reasonable explanation about the copy parameter of minmax_scala funciton

### Actual Results

# There are no warings and errors, just the result is not wrong!
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/1fdcc7df-f763-41bc-8e58-bd4f0e856b91)


### Versions

```shell
Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 65.5.0
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0
```

COMMENT:
/take

Labels: Documentation
I can not achieve inplace scaling by using sklearn.preprocessing.minmax_scale

### Describe the bug

## By setting the copy=False, ndarray data has not changed unexpectedly
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/78b636a1-1556-4f79-a5be-7f683ba45094)

### Steps/Code to Reproduce

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.preprocessing as pre

np.random.seed(10)
data = np.random.randint(1, 10, size=(5, 3))
print(data)
pre.minmax_scale(data, feature_range=(0, 1), axis=0, copy=False)
print(data)
```

### Expected Results

## A reasonable explanation about the copy parameter of minmax_scala funciton

### Actual Results

# There are no warings and errors, just the result is not wrong!
![image](https://github.com/scikit-learn/scikit-learn/assets/68744203/1fdcc7df-f763-41bc-8e58-bd4f0e856b91)


### Versions

```shell
Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 65.5.0
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0
```

COMMENT:
This issue had stalled to the best of my knowledge.

Labels: Documentation
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
Looks like this would be an easy improvement (replacing `min` with `nanmin`, etc)

Labels: Bug
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
I don't understand the use case illustrated by the reproducer. How is the display supposed to guess the expected values if they are passed as `np.NaN`?

Labels: Bug
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
I share the same feeling as @ogrisel:

- How a confusion matrix can contain `nan` values?
- The display constructor is not supposed to be called directly as documented.

So I would expect to have a real use case where we trigger this issue before advocating it as a bug. For instance, having an `_assert_all_finite` on the confusion matrix is an alternative.

Labels: Bug
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
> Our usecase is specifically leaving some cells in the confusion matrix blank (to explicitly express the combination did not exist),

0 seems like a perfectly valid way of counting the number of occurrences of things that were never observed. Why would you need to use nan for that?

Labels: Bug
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
Correct, but the advantage of the NaN route is that NaNs are represented as white squares as they do not fit on the colourscale. This makes them visually distinct from values that lie very close to the lower bound of the colourscale.

Labels: Bug
ConfusionMatrixDisplay does not correctly change text color when confusion matrix contains NaN

### Describe the bug

In our specific usecase we generate a Confusion Matrix using our own software to be passed on to ConfusionMatrixDisplay. Due to the nature of our needs this confusion matrix could contain one or more NaN values (as we want to make it explicit that this combination does not exist instead of replacing NaN by 0).

When passing a confusion matrix to ConfusionMatrixDisplay and `include_values` is set to True the `.plot()` method is unable to properly colour the text of the included values. This is due to the fact that the `thresh` variable ends up being NaN:
https://github.com/scikit-learn/scikit-learn/blob/52b692511450f7439eb929ad16933871c9a6ba68/sklearn/metrics/_plot/confusion_matrix.py#L156 

Both `.max()` and `.min()` propagate NaN as per the [Numpy docs](https://numpy.org/doc/stable/reference/generated/numpy.min.html). Their suggestion is to use `nanmin()` and `nanmax()`.

I am now wondering if this could be a sensible adaptation for this method (I'm willing to implement it myself and open a pull request). 



### Steps/Code to Reproduce

```
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay

conf_mat = np.array([[1, 2], [3, np.nan]])

disp = ConfusionMatrixDisplay(confusion_matrix = conf_mat)

disp.plot()
```

### Expected Results

Expected result would be a confusion matrix plot with correctly coloured text in the cells, similar to this example without a NaN value:
![Unknown-1](https://github.com/scikit-learn/scikit-learn/assets/4438111/2ca5b20e-a51f-4f8d-a758-c54668f50e26)


### Actual Results

As you can see the label in the top left cell is unreadable:
![Unknown-2](https://github.com/scikit-learn/scikit-learn/assets/4438111/e03de808-079f-4475-881d-db974e7c36f0)


### Versions

```shell
System:
    python: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:23:33) [GCC 12.3.0]
executable: /opt/conda/bin/python
   machine: Linux-6.1.21-v8+-aarch64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.1.2
        numpy: 1.24.4
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.1.0
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libopenblas
       filepath: /opt/conda/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: cortexa72

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: libomp
       filepath: /opt/conda/lib/libomp.so
        version: None
```

COMMENT:
I am closing this issue since that we don't want to introduce a misleading semantic.

Labels: Bug
Monotonicity constraints for GradientBoostingClassifier and GradientBoostingRegressor

### Describe the workflow you want to enable

As a follow-up of #13649, I'd like to use
```python
GradientBoostingClassifier(monotonic_cst=...)
```
same as in `HistGradientBoostingClassifier` and int `RandomForestClassifier`.

### Describe your proposed solution

Add `monotonic_cst` to `GradientBoostingClassifier` and `GradientBoostingRegressor`.

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
Hi, I would like to try implementing this one.

Labels: New Feature, Moderate, module:ensemble
⚠️ CI failed on macOS.pylatest_conda_mkl_no_openmp ⚠️

**CI failed on [macOS.pylatest_conda_mkl_no_openmp](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58670&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2)** (Sep 06, 2023)
- test_pickle_version_warning_is_issued_when_no_version_info_in_pickle

COMMENT:
## CI is no longer failing! ✅

[Successful run](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58833&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2) on Sep 11, 2023

Labels: Needs Triage
⚠️ CI failed on macOS.pylatest_conda_mkl_no_openmp ⚠️

**CI failed on [macOS.pylatest_conda_mkl_no_openmp](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58670&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2)** (Sep 06, 2023)
- test_pickle_version_warning_is_issued_when_no_version_info_in_pickle

COMMENT:
test_pickle_version_warning_is_issued_when_no_version_info_in_pickle failure has its own issue 
 https://github.com/scikit-learn/scikit-learn/issues/27268

Labels: Needs Triage