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/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 |
https://github.com/scikit-learn/scikit-learn/issues/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 |
https://github.com/scikit-learn/scikit-learn/issues/27907 | [
"Bug"
] | Dummy estimators don't have the `feature_names_in_` nor `n_features_in_` attributes
### Describe the bug
`DummyClassifier` and `DummyRegressor` estimators don't have the `feature_names_in_` nor `n_features_in_` attributes. The reason is that they don't call `self._validate_data` during `fit` like other estimators do.... | 27,907 |
https://github.com/scikit-learn/scikit-learn/issues/27907 | [
"Bug"
] | Dummy estimators don't have the `feature_names_in_` nor `n_features_in_` attributes
### Describe the bug
`DummyClassifier` and `DummyRegressor` estimators don't have the `feature_names_in_` nor `n_features_in_` attributes. The reason is that they don't call `self._validate_data` during `fit` like other estimators do.... | 27,907 |
https://github.com/scikit-learn/scikit-learn/issues/27907 | [
"Bug"
] | Dummy estimators don't have the `feature_names_in_` nor `n_features_in_` attributes
### Describe the bug
`DummyClassifier` and `DummyRegressor` estimators don't have the `feature_names_in_` nor `n_features_in_` attributes. The reason is that they don't call `self._validate_data` during `fit` like other estimators do.... | 27,907 |
https://github.com/scikit-learn/scikit-learn/issues/27907 | [
"Bug"
] | Dummy estimators don't have the `feature_names_in_` nor `n_features_in_` attributes
### Describe the bug
`DummyClassifier` and `DummyRegressor` estimators don't have the `feature_names_in_` nor `n_features_in_` attributes. The reason is that they don't call `self._validate_data` during `fit` like other estimators do.... | 27,907 |
https://github.com/scikit-learn/scikit-learn/issues/27907 | [
"Bug"
] | Dummy estimators don't have the `feature_names_in_` nor `n_features_in_` attributes
### Describe the bug
`DummyClassifier` and `DummyRegressor` estimators don't have the `feature_names_in_` nor `n_features_in_` attributes. The reason is that they don't call `self._validate_data` during `fit` like other estimators do.... | 27,907 |
https://github.com/scikit-learn/scikit-learn/issues/27905 | [
"Needs Triage"
] | Ensure predictions sparse before `sp.hstack` in `ClassifierChain`
We use `sp.hstack` in a number of places in `ClassifierChain` where we may be stacking sparse with dense, e.g.,:
https://github.com/scikit-learn/scikit-learn/blob/36f6734789fc7e4940792c1cfb6a6e90dfcae484/sklearn/multioutput.py#L948
and
https://... | 27,905 |
https://github.com/scikit-learn/scikit-learn/issues/27905 | [
"Needs Triage"
] | Ensure predictions sparse before `sp.hstack` in `ClassifierChain`
We use `sp.hstack` in a number of places in `ClassifierChain` where we may be stacking sparse with dense, e.g.,:
https://github.com/scikit-learn/scikit-learn/blob/36f6734789fc7e4940792c1cfb6a6e90dfcae484/sklearn/multioutput.py#L948
and
https://... | 27,905 |
https://github.com/scikit-learn/scikit-learn/issues/27905 | [
"Needs Triage"
] | Ensure predictions sparse before `sp.hstack` in `ClassifierChain`
We use `sp.hstack` in a number of places in `ClassifierChain` where we may be stacking sparse with dense, e.g.,:
https://github.com/scikit-learn/scikit-learn/blob/36f6734789fc7e4940792c1cfb6a6e90dfcae484/sklearn/multioutput.py#L948
and
https://... | 27,905 |
https://github.com/scikit-learn/scikit-learn/issues/27903 | [
"API",
"Needs Decision",
"RFC"
] | allow_nan tag in Pipelines
Unfortunately, our tag system for allowing nans does not work with pipelines. Lets say we have a pipeline with two steps and the final step does not accept nans:
1. If the first step is an Imputer, then the pipeline accept nans. For example: `make_pipeline(SimpleImputer(), LogisticRegress... | 27,903 |
https://github.com/scikit-learn/scikit-learn/issues/27903 | [
"API",
"Needs Decision",
"RFC"
] | allow_nan tag in Pipelines
Unfortunately, our tag system for allowing nans does not work with pipelines. Lets say we have a pipeline with two steps and the final step does not accept nans:
1. If the first step is an Imputer, then the pipeline accept nans. For example: `make_pipeline(SimpleImputer(), LogisticRegress... | 27,903 |
https://github.com/scikit-learn/scikit-learn/issues/27903 | [
"API",
"Needs Decision",
"RFC"
] | allow_nan tag in Pipelines
Unfortunately, our tag system for allowing nans does not work with pipelines. Lets say we have a pipeline with two steps and the final step does not accept nans:
1. If the first step is an Imputer, then the pipeline accept nans. For example: `make_pipeline(SimpleImputer(), LogisticRegress... | 27,903 |
https://github.com/scikit-learn/scikit-learn/issues/27903 | [
"API",
"Needs Decision",
"RFC"
] | allow_nan tag in Pipelines
Unfortunately, our tag system for allowing nans does not work with pipelines. Lets say we have a pipeline with two steps and the final step does not accept nans:
1. If the first step is an Imputer, then the pipeline accept nans. For example: `make_pipeline(SimpleImputer(), LogisticRegress... | 27,903 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27894 | [
"Performance",
"Needs Benchmarks"
] | Use SYRK instead of GEMM in pairwise distance
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/27877
<div type='discussions-op-text'>
<sup>Originally posted by **darshanp4** November 30, 2023</sup>
Hello
I was checking the DBSCAN algo , where mostly computing pairwise distance it us... | 27,894 |
https://github.com/scikit-learn/scikit-learn/issues/27893 | [
"Bug"
] | sklearn.cluster.HDBSCAN shape error when making medoids with precomputed metric
### Describe the bug
When fitting with HDBSCAN with metric="precomputed" and store_centers='medoid', it would raise the ValueError
`ValueError: Precomputed metric requires shape (n_queries, n_indexed). Got (11, 300) for 11 indexed.`
C... | 27,893 |
https://github.com/scikit-learn/scikit-learn/issues/27893 | [
"Bug"
] | sklearn.cluster.HDBSCAN shape error when making medoids with precomputed metric
### Describe the bug
When fitting with HDBSCAN with metric="precomputed" and store_centers='medoid', it would raise the ValueError
`ValueError: Precomputed metric requires shape (n_queries, n_indexed). Got (11, 300) for 11 indexed.`
C... | 27,893 |
https://github.com/scikit-learn/scikit-learn/issues/27887 | [
"Bug",
"Needs Triage"
] | sklearn.linear_model.lars_path_gram ONLY accepts Xy to be of shape (n_features,) and NOT (n_features, n_targets)
### Describe the bug
The [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path_gram.html) says lars_path_gram accepts Xy to be _"array-like of shape (n_features... | 27,887 |
https://github.com/scikit-learn/scikit-learn/issues/27887 | [
"Bug",
"Needs Triage"
] | sklearn.linear_model.lars_path_gram ONLY accepts Xy to be of shape (n_features,) and NOT (n_features, n_targets)
### Describe the bug
The [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path_gram.html) says lars_path_gram accepts Xy to be _"array-like of shape (n_features... | 27,887 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27882 | [
"New Feature",
"help wanted"
] | [RFC] Varying the number of outputs considered for splitting in Multi Output Decision Trees
### Describe the workflow you want to enable
One strength of RFRs is that they are incredibly robust and therefore provide a strong baseline for many tasks without needing to consider normalization or scaling of either the inp... | 27,882 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27881 | [
"New Feature",
"Needs Decision",
"RFC"
] | [RFC] Leaf Level Variance in Multi Output Decision Trees
### Describe the workflow you want to enable
For single output RFR trained with the squared error criterion the impurity of the leaves can be used as a crude but useful estimate of the aleatoric uncertainty. In the multi output case the impurity is the sum ov... | 27,881 |
https://github.com/scikit-learn/scikit-learn/issues/27880 | [
"Documentation"
] | DOC replace MAPE in lagged features example
A few improvements could be made on the new example of #25350:
- Mean absolute percentage error (MAPE) is used quite a lot. I propose to replace it, in particular if predicting/forecasting the mean value. Note that MAPE is optimized by the median of a distribution with pdf ... | 27,880 |
https://github.com/scikit-learn/scikit-learn/issues/27880 | [
"Documentation"
] | DOC replace MAPE in lagged features example
A few improvements could be made on the new example of #25350:
- Mean absolute percentage error (MAPE) is used quite a lot. I propose to replace it, in particular if predicting/forecasting the mean value. Note that MAPE is optimized by the median of a distribution with pdf ... | 27,880 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27879 | [
"Bug"
] | Pandas Copy-on-Write mode should be enabled in all tests
### Describe the bug
Pandas COW will be enabled by default in version 3.0.
For example, today I just found that `TargetEncoder` doesn't work properly with it enabled.
There are probably many other examples that could be uncovered by testing.
### Steps/Co... | 27,879 |
https://github.com/scikit-learn/scikit-learn/issues/27876 | [
"Documentation",
"Needs Triage"
] | HDBSCAN: Remove centroids_ attribute from API documentation
### Describe the issue linked to the documentation
The API documentation of `HDBSCAN` on the [scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html#sklearn.cluster.HDBSCAN) lists `centroids_` as an attribute. Ho... | 27,876 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27873 | [
"RFC"
] | RFC Unify old GradientBoosting estimators and HGBT
### Current situation
We have the unfortunate situation to have 2 different versions of gradient boosting, the old estimators ([`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn-en... | 27,873 |
https://github.com/scikit-learn/scikit-learn/issues/27869 | [
"New Feature"
] | Clarification and Improvement Suggestions for OrdinalEncoder Input and Output
### Describe the workflow you want to enable
Hi there,
I'm relatively new to working with scikit-learn, and as I delve into it, a couple of aspects of the `OrdinalEncoder` have raised questions for me regarding its functionality and ... | 27,869 |
https://github.com/scikit-learn/scikit-learn/issues/27869 | [
"New Feature"
] | Clarification and Improvement Suggestions for OrdinalEncoder Input and Output
### Describe the workflow you want to enable
Hi there,
I'm relatively new to working with scikit-learn, and as I delve into it, a couple of aspects of the `OrdinalEncoder` have raised questions for me regarding its functionality and ... | 27,869 |
https://github.com/scikit-learn/scikit-learn/issues/27867 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7027741686)** (Nov 29, 2023)
COMMENT:
So apparently we have some failures with NumPy 2 here.
@ogrisel is it something known from the PR that have been open by @seberg?
I did not follow those unf... | 27,867 |
https://github.com/scikit-learn/scikit-learn/issues/27867 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7027741686)** (Nov 29, 2023)
COMMENT:
Grrrrrrrr, this is a new thing, indirectly related to bumping maxdims. I also bumped MAXARGS, which is ABI compatible but changes the size of 1 or 2 objects.... | 27,867 |
https://github.com/scikit-learn/scikit-learn/issues/27867 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7027741686)** (Nov 29, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/7041734769) on Nov 30, 2023 | 27,867 |
https://github.com/scikit-learn/scikit-learn/issues/27849 | [
"Needs Triage"
] | Ridge replacement for normalize=True gives different results
> I will look more closely next week but even this breaks:
>
> ```python
> from sklearn.datasets import make_regression
> from sklearn import linear_model
> from sklearn.pipeline import make_pipeline
> from sklearn.preprocessing import StandardScaler
... | 27,849 |
https://github.com/scikit-learn/scikit-learn/issues/27849 | [
"Needs Triage"
] | Ridge replacement for normalize=True gives different results
> I will look more closely next week but even this breaks:
>
> ```python
> from sklearn.datasets import make_regression
> from sklearn import linear_model
> from sklearn.pipeline import make_pipeline
> from sklearn.preprocessing import StandardScaler
... | 27,849 |
https://github.com/scikit-learn/scikit-learn/issues/27849 | [
"Needs Triage"
] | Ridge replacement for normalize=True gives different results
> I will look more closely next week but even this breaks:
>
> ```python
> from sklearn.datasets import make_regression
> from sklearn import linear_model
> from sklearn.pipeline import make_pipeline
> from sklearn.preprocessing import StandardScaler
... | 27,849 |
https://github.com/scikit-learn/scikit-learn/issues/27849 | [
"Needs Triage"
] | Ridge replacement for normalize=True gives different results
> I will look more closely next week but even this breaks:
>
> ```python
> from sklearn.datasets import make_regression
> from sklearn import linear_model
> from sklearn.pipeline import make_pipeline
> from sklearn.preprocessing import StandardScaler
... | 27,849 |
https://github.com/scikit-learn/scikit-learn/issues/27848 | [
"New Feature",
"Needs Triage"
] | Contraction Clustering (RASTER): A very fast and parallelizable clustering algorithm
### Describe the workflow you want to enable
RASTER is a very fast clustering algorithm that runs in linear time, uses constant memory, and only requires a single pass. The relevant package is `cluster`.
### Describe your proposed s... | 27,848 |
https://github.com/scikit-learn/scikit-learn/issues/27848 | [
"New Feature",
"Needs Triage"
] | Contraction Clustering (RASTER): A very fast and parallelizable clustering algorithm
### Describe the workflow you want to enable
RASTER is a very fast clustering algorithm that runs in linear time, uses constant memory, and only requires a single pass. The relevant package is `cluster`.
### Describe your proposed s... | 27,848 |
https://github.com/scikit-learn/scikit-learn/issues/27848 | [
"New Feature",
"Needs Triage"
] | Contraction Clustering (RASTER): A very fast and parallelizable clustering algorithm
### Describe the workflow you want to enable
RASTER is a very fast clustering algorithm that runs in linear time, uses constant memory, and only requires a single pass. The relevant package is `cluster`.
### Describe your proposed s... | 27,848 |
https://github.com/scikit-learn/scikit-learn/issues/27846 | [
"Build / CI"
] | ⚠️ CI failed on Ubuntu_Atlas.ubuntu_atlas (last failure: Aug 28, 2025) ⚠️
**CI is still failing on [Ubuntu_Atlas.ubuntu_atlas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79396&view=logs&j=689a1c8f-ff4e-5689-1a1a-6fa551ae9eba)** (Aug 28, 2025)
- test_float_precision[33-MiniBatchKMeans-dense]... | 27,846 |
https://github.com/scikit-learn/scikit-learn/issues/27846 | [
"Build / CI"
] | ⚠️ CI failed on Ubuntu_Atlas.ubuntu_atlas (last failure: Aug 28, 2025) ⚠️
**CI is still failing on [Ubuntu_Atlas.ubuntu_atlas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79396&view=logs&j=689a1c8f-ff4e-5689-1a1a-6fa551ae9eba)** (Aug 28, 2025)
- test_float_precision[33-MiniBatchKMeans-dense]... | 27,846 |
https://github.com/scikit-learn/scikit-learn/issues/27846 | [
"Build / CI"
] | ⚠️ CI failed on Ubuntu_Atlas.ubuntu_atlas (last failure: Aug 28, 2025) ⚠️
**CI is still failing on [Ubuntu_Atlas.ubuntu_atlas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79396&view=logs&j=689a1c8f-ff4e-5689-1a1a-6fa551ae9eba)** (Aug 28, 2025)
- test_float_precision[33-MiniBatchKMeans-dense]... | 27,846 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27843 | [
"New Feature"
] | set_output doesn't work for inverse_transform method
### Describe the bug
Using `set_output(transfrom="pandas")` doesn't return a pandas dataframe for the StandardScaler's `inverse_transform` method.
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import StandardScaler
from sklearn.datasets impor... | 27,843 |
https://github.com/scikit-learn/scikit-learn/issues/27839 | [
"Bug"
] | LocalOutlierFactor might not work with duplicated samples
This an investigation from the discussion in https://github.com/scikit-learn/scikit-learn/discussions/27838
`LocalFactorOutlier` might be difficult to use when there are duplicate values larger then `n_neighbors`. In this case, the distance for these neighbo... | 27,839 |
https://github.com/scikit-learn/scikit-learn/issues/27839 | [
"Bug"
] | LocalOutlierFactor might not work with duplicated samples
This an investigation from the discussion in https://github.com/scikit-learn/scikit-learn/discussions/27838
`LocalFactorOutlier` might be difficult to use when there are duplicate values larger then `n_neighbors`. In this case, the distance for these neighbo... | 27,839 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
https://github.com/scikit-learn/scikit-learn/issues/27829 | [
"Bug",
"help wanted"
] | Different HDBSCAN clusters from scikit-learn and scikit-learn-contrib packages
### Describe the bug
The `HDBSCAN()` functions provided by [scikit-learn-contrib/hdbscan](https://github.com/scikit-learn-contrib/hdbscan) and this package can give different clustering results, e.g. when using the **`cluster_selection_eps... | 27,829 |
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