Talip7/scikit-learn-issues-multilabel huggingface dataset:
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
If you put 4 samples to 0 instead of 5, or alternatively increase the `n_samples` to make sure that the non-zero weighted data points are always more numerous than the number of `features` to get a non singular problem, do you still get the problem? It could be indeed related to the behavior I observed in https://github.com/scikit-learn/scikit-learn/issues/22855#issuecomment-1463878692.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
I edited the script of the summary to show that it fails in 20% of the time for 100 seeds.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
I confirm that I also reproduce 20% failures with the new script.

I also confirm that using `Ridge(alpha=0, solver="lsqr")` works fine 100% of the time.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
I will try to change the lapack driver and maybe play with the `cond` option.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
With the `gelsy` driver I get a 13% failure rate. So not great either.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
By setting `cond=1e-15` with the default `gelsd` lapack driver, I get 100% pass and we get the same solution as `Ridge(alpha=0, solver="lsqr")` (I only checked the last seed).

But I am not sure how to set a good value for `cond` in general.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
As discussed in #22947 the `X_with_intercept` approach can be problematic (for ridge) and think it's important that our `LinearRegression` and `Ridge` estimators deal with the intercept in a consistent manner.

So I would rather pass `cond=1e-15` to `lstsq` in `LinearRegression` as a short term fix for the undedetermined case while keeping the current way to handle intercepts in OLS (that is `fit_on_centered data followed by self.intercept_ = y_mean - self.coef_ @ X_mean`) even if that means that the intercept does not take part in the computation of the norm of the minimal norm solution.

Later we can also discuss other solvers for `LinearRegression` to also improve its fit time performance #22855.

Labels: Bug, module:linear_model
LinearRegression with zero sample_weights is not the same as excluding those rows

### Describe the bug

Excluding rows having `sample_weight == 0` in `LinearRegression` does not give the same results.

### Steps/Code to Reproduce

```python
from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression

results = []
for i in range(100):
    rng = np.random.RandomState(i)
    n_samples, n_features = 10, 5
    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    reg = LinearRegression()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0, intercept_0 = reg.coef_.copy(), reg.intercept_

    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    print(f"{coef_0=}")
    print(f"{reg.coef_=}")
    results.append(np.allclose(reg.coef_, coef_0, rtol=1e-6))
print(Counter(results))
```

### Expected Results

Always `True`.

### Actual Results

```python
Counter({True: 79, False: 21})  # it fails 20% of the time
```

The print statement gives:
```
coef_0 =    array([ 1.43516166, -1.78826443,  0.15365526,  1.82233166, -1.6       ])
reg.coef_ = array([ 2.24022351, -1.04917851,  0.45341088,  0.80315086, -0.25726798])
```


### Versions

```shell
System:
    python: 3.9.15 (main, Nov 15 2022, 05:24:15)  [Clang 14.0.0 (clang-1400.0.29.202)]
   machine: macOS-13.3.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.2.2
        numpy: 1.23.5
        scipy: 1.10.1
       Cython: 0.29.33

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
```
```

COMMENT:
I close in favor/duplicate of #22947.

Labels: Bug, module:linear_model
Confusion Matrix is 1x1 instead of NxN if all labels and predicted labels are the same

### Describe the bug

A confusion matrix with all correctly predicted labels will generate a 1x1 confusion matrix, instead of a nxn confusion matrix (for n classes)

### Steps/Code to Reproduce

```
from sklearn.metrics import confusion_matrix

y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_pred = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

cm = confusion_matrix(y_true, y_pred)
print(cm)
```

### Expected Results

```
[[0 0]
 [0 10]]
```

### Actual Results

```
[[10]]
```

### Versions

```shell
System:
    python: 3.9.16 (main, Feb  4 2023, 12:10:12)  [GCC 10.2.1 20210110]
executable: ......./bin/python
   machine: Linux-4.19-ovh-xxxx-std-ipv6-64-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.2.2
          pip: 22.0.4
   setuptools: 58.1.0
        numpy: 1.24.2
        scipy: 1.10.1
       Cython: None
       pandas: 2.0.0
   matplotlib: None
       joblib: 1.2.0
threadpoolctl: 3.1.0
```

COMMENT:
You should use `labels` in this case:

```python
In [9]: from sklearn.metrics import confusion_matrix
   ...: 
   ...: y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
   ...: y_pred = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
   ...: 
   ...: cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
   ...: print(cm)
```
```
[[ 0  0]
 [ 0 10]]
```

Labels: Bug, Needs Triage
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
/take

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
The culprit is `pandas` dev. I will bisect to know which commit changed the behaviour.

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
So it comes from this commit: https://github.com/pandas-dev/pandas/pull/52542

It comes from calling `pd.concat(..., ignore_index=True)` with a first dataset containing `None` (thus an object dtype) with a second dataset containing `np.nan` and float (thus a float64 dtype).

The previous behaviour cast the column as `object` dtype while the new behaviour is casting into float64.

I am trying to craft a minimal reproducer.

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
There are plenty of (~280) recent errors probably due to a pandas change (and maybe numpy too?), symptoms look like this:
- `FutureWarning: is_sparse is deprecated and will be removed in a future version. Check isinstance(dtype, pd.SparseDtype) instead.`
- `FutureWarning: The behavior of DataFrame concatenation with all-NA entries is deprecated. In a future version, this will no longer exclude all-NA columns when determining the result dtypes. To retain the old behavior, cast the all-NA columns to the desired dtype before the concat operation.`
- `ValueError: setting an array element with a sequence.`
- `DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)`

Number of failures look like this (quick and dirty analysis may miss a few kind of errors):
```
    245 FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.
     19 DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
     14 ValueError: setting an array element with a sequence.
      5 FutureWarning: The behavior of DataFrame concatenation with all-NA entries is deprecated. In a future version, this will no longer exclude all-NA columns when determining the result dtypes. To retain the old behavior, cast the all-NA columns to the desired dtype before the concat operation.
```

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
Opened #26287 about pandas is_sparse

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
Seems like some np.find_common_type DeprecationWarning are coming from pandas https://github.com/pandas-dev/pandas/issues/53236 and should hopefully be fixed soon.

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
## CI is no longer failing! ✅

[Successful run](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=55942&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf) on Jun 19, 2023

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
For you information, SciPy is currently transitioning from the sparse matrix semantic to the sparse array semantic (see https://github.com/scikit-learn/scikit-learn/issues/26418 for discussing what it means for scikit-learn).

If tests using sparse data fail on `pylatest_pip_scipy_dev`, feel free to ping me.

Labels: Bug, Build / CI
⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️

**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=56792&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 06, 2023)
Unable to find junit file. Please see link for details.

COMMENT:
These issues have all been fixed. Let's close

Labels: Bug, Build / CI