Talip7/scikit-learn-issues-multilabel huggingface dataset:
Add beginner-friendly examples

## 🎯 Beginner Examples Request

### Description
It would be great to have more beginner-friendly examples in the project.

### Suggested additions:
- Simple "Hello World" examples
- Step-by-step tutorials
- Common use case demonstrations
- Code comments for clarity

### Why this matters:
- Helps new developers get started
- Makes the project more inclusive
- Encourages community growth

### Type
- [ ] Documentation
- [ ] Enhancement
- [ ] Good first issue

---
*I'm a beginner and would love to help create examples that help others like me!*

COMMENT:
See [#31970 (comment)](https://github.com/scikit-learn/scikit-learn/issues/31970#issuecomment-3204208173).

Labels: spam
⚠️ CI failed on Wheel builder (last failure: Aug 22, 2025) ⚠️

**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/17145548838)** (Aug 22, 2025)

COMMENT:
Needs to be looked at in more details. Seems like a variation of https://github.com/scikit-learn/scikit-learn/issues/31955 where the issue happens in the Python 3.13 free-threaded and we need an equivalent work-around as in https://github.com/scikit-learn/scikit-learn/pull/31964 (the Docker image is not used in the free-threaded case).

Labels: Bug
Create troubleshooting guide

## 🔧 Troubleshooting Guide

### Description
A troubleshooting guide would help users solve common problems.

### Suggested content:
- Common error messages and solutions
- Installation troubleshooting
- Configuration issues
- Performance problems

### Benefits:
- Reduces support burden
- Improves user experience
- Self-service help

### Type
- [ ] Documentation
- [ ] Enhancement
- [ ] Good first issue

---
*I'd like to help create a comprehensive troubleshooting guide!*

COMMENT:
Thanks for opening the issue. 

This appears to be spam. [The same content has been posted across multiple repos](https://github.com/search?q=involves%3ANaveenkm07+Create+troubleshooting+guide&type=issues). 

I'm closing it here to keep the tracker clean.

Labels: spam
⚠️ CI failed on Linux.pylatest_pip_openblas_pandas (last failure: Aug 19, 2025) ⚠️

**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79175&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Aug 19, 2025)
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_array-True]

COMMENT:
## CI is no longer failing! ✅

[Successful run](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79253&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e) on Aug 22, 2025

Labels: Needs Triage
⚠️ CI failed on Linux.pylatest_pip_openblas_pandas (last failure: Aug 19, 2025) ⚠️

**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79175&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Aug 19, 2025)
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_matrix-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csc_array-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_matrix-csr_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csc_array-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_matrix-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_matrix-True]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_array-False]
- test_sparse_matmul_to_dense[23-float32-csr_array-csr_array-True]

COMMENT:
Fixed by https://github.com/scikit-learn/scikit-learn/pull/31983

Labels: Needs Triage
a11y - scikit-learn docs accessibility audit and remediation

### Description

**Note:** This is scoped as part of an ongoing NASA ROSES grant in collaboration with Quansight; as such, a couple of us at Quansight will take on the work outlined in this issue.

Per the NASA ROSES grant, we will conduct an accessibility review of the [scikit-learn documentation site](https://scikit-learn.org/stable/) and work on remediation of the flagged issues. 

Since scikit-learn uses the PyData Sphinx Theme, on which we have already conducted thorough accessibility audits and spent a substantial amount of work over the last couple of years to make this theme more accessible, the audit and remediation of scikit-learn will focus on customised/custom features added to the scikit-learn documentation. 

### Proposed implementation 

To achieve this goal, I propose the following approach:

1. Scope what needs to be audited/tested - and update this issue to reflect this
2. Test/audit components and report back on the findings in this issue
3. Iteratively work on any remediation tasks as needed.

Please let me know if you have any questions or suggestions on how to approach this more effectively, so we can keep you all aligned and ensure a smooth contribution. 

Also, if y'all can assign me to this issue, it would be great! ✨

COMMENT:
Sounds like a good plan. Looking forward to the improvements!

Something to ponder: sometimes we create "meta issues" when there are many changes of the same type are needed (say rewriting every docstring that has a particular parameter in it or linking examples from docstrings or making estimators array API compatible). This works quite well in directing people who are looking to make a small contribution and when there is a large number of changes needed. The issue author then becomes more of a reviewer instead of doing the work. Depending on what you find, this could be an option. Though hopefully a lot can be done with a few changes to the CSS?

Labels: Documentation
a11y - scikit-learn docs accessibility audit and remediation

### Description

**Note:** This is scoped as part of an ongoing NASA ROSES grant in collaboration with Quansight; as such, a couple of us at Quansight will take on the work outlined in this issue.

Per the NASA ROSES grant, we will conduct an accessibility review of the [scikit-learn documentation site](https://scikit-learn.org/stable/) and work on remediation of the flagged issues. 

Since scikit-learn uses the PyData Sphinx Theme, on which we have already conducted thorough accessibility audits and spent a substantial amount of work over the last couple of years to make this theme more accessible, the audit and remediation of scikit-learn will focus on customised/custom features added to the scikit-learn documentation. 

### Proposed implementation 

To achieve this goal, I propose the following approach:

1. Scope what needs to be audited/tested - and update this issue to reflect this
2. Test/audit components and report back on the findings in this issue
3. Iteratively work on any remediation tasks as needed.

Please let me know if you have any questions or suggestions on how to approach this more effectively, so we can keep you all aligned and ensure a smooth contribution. 

Also, if y'all can assign me to this issue, it would be great! ✨

COMMENT:
A good shout! I love meta-issues to keep track of complex pieces of work.

Labels: Documentation
a11y - scikit-learn docs accessibility audit and remediation

### Description

**Note:** This is scoped as part of an ongoing NASA ROSES grant in collaboration with Quansight; as such, a couple of us at Quansight will take on the work outlined in this issue.

Per the NASA ROSES grant, we will conduct an accessibility review of the [scikit-learn documentation site](https://scikit-learn.org/stable/) and work on remediation of the flagged issues. 

Since scikit-learn uses the PyData Sphinx Theme, on which we have already conducted thorough accessibility audits and spent a substantial amount of work over the last couple of years to make this theme more accessible, the audit and remediation of scikit-learn will focus on customised/custom features added to the scikit-learn documentation. 

### Proposed implementation 

To achieve this goal, I propose the following approach:

1. Scope what needs to be audited/tested - and update this issue to reflect this
2. Test/audit components and report back on the findings in this issue
3. Iteratively work on any remediation tasks as needed.

Please let me know if you have any questions or suggestions on how to approach this more effectively, so we can keep you all aligned and ensure a smooth contribution. 

Also, if y'all can assign me to this issue, it would be great! ✨

COMMENT:
Also thanks @trallard for the transparency effort in communicating the context and the plan ahead of time, this is very appreciated (even if I was already aware of the context myself) :)

Labels: Documentation
a11y - scikit-learn docs accessibility audit and remediation

### Description

**Note:** This is scoped as part of an ongoing NASA ROSES grant in collaboration with Quansight; as such, a couple of us at Quansight will take on the work outlined in this issue.

Per the NASA ROSES grant, we will conduct an accessibility review of the [scikit-learn documentation site](https://scikit-learn.org/stable/) and work on remediation of the flagged issues. 

Since scikit-learn uses the PyData Sphinx Theme, on which we have already conducted thorough accessibility audits and spent a substantial amount of work over the last couple of years to make this theme more accessible, the audit and remediation of scikit-learn will focus on customised/custom features added to the scikit-learn documentation. 

### Proposed implementation 

To achieve this goal, I propose the following approach:

1. Scope what needs to be audited/tested - and update this issue to reflect this
2. Test/audit components and report back on the findings in this issue
3. Iteratively work on any remediation tasks as needed.

Please let me know if you have any questions or suggestions on how to approach this more effectively, so we can keep you all aligned and ensure a smooth contribution. 

Also, if y'all can assign me to this issue, it would be great! ✨

COMMENT:
Leaving a comment here so I get updates about this issue. :)

Labels: Documentation
⚠️ CI failed on Wheel builder (last failure: Aug 19, 2025) ⚠️

**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/17059042784)** (Aug 19, 2025)

COMMENT:
I opened https://github.com/scikit-learn/scikit-learn/pull/31964 as a work-around in scikit-learn.

I opened https://github.com/joblib/loky/issues/459 to track the issue in loky.

Labels: Needs Triage
UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Describe the bug

When using pandas dataframes and a `TransformedTargetRegressor` with `PowerTransformer` with `set_output(transform="pandas")`, I get this warning:

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

The warning does not arise when using other estimators (e.g. `StandardScaler`) but only with `PowerTransformer`.

The problem seems to originate from the `inverse_transform` implementation of `PowerTransformer`.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import PowerTransformer, StandardScaler

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# This works fine:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=StandardScaler().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)

# But this gets a warning:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=PowerTransformer().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)
```

### Expected Results

No warning

### Actual Results

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Versions

```shell
System:
    python: 3.11.9

Python dependencies:
      sklearn: 1.7.1
          pip: 25.2
   setuptools: 65.5.0
        numpy: 2.0.2
        scipy: 1.15.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0
```

COMMENT:
Somewhat related issue: #29361

Labels: Bug
UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Describe the bug

When using pandas dataframes and a `TransformedTargetRegressor` with `PowerTransformer` with `set_output(transform="pandas")`, I get this warning:

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

The warning does not arise when using other estimators (e.g. `StandardScaler`) but only with `PowerTransformer`.

The problem seems to originate from the `inverse_transform` implementation of `PowerTransformer`.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import PowerTransformer, StandardScaler

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# This works fine:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=StandardScaler().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)

# But this gets a warning:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=PowerTransformer().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)
```

### Expected Results

No warning

### Actual Results

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Versions

```shell
System:
    python: 3.11.9

Python dependencies:
      sklearn: 1.7.1
          pip: 25.2
   setuptools: 65.5.0
        numpy: 2.0.2
        scipy: 1.15.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0
```

COMMENT:
Hi @ThibaultDECO Thank you for reporting this! 
I can reproduce the warning with sklearn==1.7.1 using the snippet in the description.
Root cause seems to be how PowerTransformer.inverse_transform validates inputs when set_output(transform="pandas") is used with TransformedTargetRegressor.
Plan to fix: switch input validation in inverse_transform to check_array(...) (instead of the feature-name aware path), then return via _set_output(X_inv, X_in) so pandas containers/column names are preserved and no feature-name warning is raised.
If maintainers agree with this approach, I’d like to open a PR with:

code change in sklearn/preprocessing/_data.py

regression tests (pandas + TransformedTargetRegressor)

a short whatsnew entry
Thanks! 🙏

Labels: Bug
UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Describe the bug

When using pandas dataframes and a `TransformedTargetRegressor` with `PowerTransformer` with `set_output(transform="pandas")`, I get this warning:

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

The warning does not arise when using other estimators (e.g. `StandardScaler`) but only with `PowerTransformer`.

The problem seems to originate from the `inverse_transform` implementation of `PowerTransformer`.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import PowerTransformer, StandardScaler

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# This works fine:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=StandardScaler().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)

# But this gets a warning:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=PowerTransformer().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)
```

### Expected Results

No warning

### Actual Results

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Versions

```shell
System:
    python: 3.11.9

Python dependencies:
      sklearn: 1.7.1
          pip: 25.2
   setuptools: 65.5.0
        numpy: 2.0.2
        scipy: 1.15.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0
```

COMMENT:
Hi @35250 
Thank you for your investigation !
I actually already opened PR #31948 to partially fix this issue, feel free to amend it with your proposals (`_set_output(X_inv, X_in)`, regression tests, whatsnew). Thanks!

Labels: Bug
UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Describe the bug

When using pandas dataframes and a `TransformedTargetRegressor` with `PowerTransformer` with `set_output(transform="pandas")`, I get this warning:

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

The warning does not arise when using other estimators (e.g. `StandardScaler`) but only with `PowerTransformer`.

The problem seems to originate from the `inverse_transform` implementation of `PowerTransformer`.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import PowerTransformer, StandardScaler

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# This works fine:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=StandardScaler().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)

# But this gets a warning:
pipeline = TransformedTargetRegressor(
    regressor=LinearRegression(),
    transformer=PowerTransformer().set_output(transform="pandas")
)
pipeline.fit(X_train, y_train)
y_test_pred = pipeline.predict(X_test)
```

### Expected Results

No warning

### Actual Results

> UserWarning: X has feature names, but PowerTransformer was fitted without feature names

### Versions

```shell
System:
    python: 3.11.9

Python dependencies:
      sklearn: 1.7.1
          pip: 25.2
   setuptools: 65.5.0
        numpy: 2.0.2
        scipy: 1.15.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0
```

COMMENT:
Hi @ThibaultDECO , 

Thanks for the info! I’ll review PR #31948 and see how I can contribute, including adding regression tests, _set_output updates, and whatsnew documentation. 

Looking forward to collaborating!

Labels: Bug