html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
|---|---|---|---|---|
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 | [
0.004135213792324066,
-0.05861535668373108,
0.026940390467643738,
0.03239798545837402,
0.06157804653048515,
0.010081687942147255,
0.05444779247045517,
0.0009595628362149,
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0.0012931758537888527,
-0.030416050925850868,
0.05083966255187988,
0.0367... |
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 | [
0.004135213792324066,
-0.05861535668373108,
0.026940390467643738,
0.03239798545837402,
0.06157804653048515,
0.010081687942147255,
0.05444779247045517,
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0.0012931758537888527,
-0.030416050925850868,
0.05083966255187988,
0.0367... |
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 | [
0.06575498729944229,
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0.0155... |
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 | [
0.06575498729944229,
0.03077273815870285,
0.038796305656433105,
-0.021527795121073723,
0.08153615891933441,
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0.14130978286266327,
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0.005156018305569887,
0.029404979199171066,
-0.018921107053756714,
-0.0025113483425229788,
0.0155... |
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 | [
0.06575498729944229,
0.03077273815870285,
0.038796305656433105,
-0.021527795121073723,
0.08153615891933441,
0.023787107318639755,
0.14130978286266327,
0.024752328172326088,
0.0386701300740242,
0.005156018305569887,
0.029404979199171066,
-0.018921107053756714,
-0.0025113483425229788,
0.0155... |
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 | [
0.06575498729944229,
0.03077273815870285,
0.038796305656433105,
-0.021527795121073723,
0.08153615891933441,
0.023787107318639755,
0.14130978286266327,
0.024752328172326088,
0.0386701300740242,
0.005156018305569887,
0.029404979199171066,
-0.018921107053756714,
-0.0025113483425229788,
0.0155... |
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 | [
0.06575498729944229,
0.03077273815870285,
0.038796305656433105,
-0.021527795121073723,
0.08153615891933441,
0.023787107318639755,
0.14130978286266327,
0.024752328172326088,
0.0386701300740242,
0.005156018305569887,
0.029404979199171066,
-0.018921107053756714,
-0.0025113483425229788,
0.0155... |
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 | [
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0.05... |
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 | [
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0.05... |
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 | [
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0.05... |
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 | [
-0.0735960602760315,
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0.09511... |
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 | [
-0.07576693594455719,
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-0.0095290532335639,
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0.0965... |
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 | [
-0.07059862464666367,
0.0215977281332016,
-0.00966706220060587,
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0.091... |
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 | [
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... |
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 | [
-0.026410434395074844,
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... |
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 | [
-0.024928025901317596,
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0... |
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 | [
-0.021524718031287193,
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0.0... |
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 | [
-0.02446814626455307,
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0.0... |
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 | [
-0.034563980996608734,
-0.025120744481682777,
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0.04... |
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 | [
-0.026460446417331696,
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0... |
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 | [
-0.02395874448120594,
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0... |
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 | [
-0.02387985959649086,
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0.018444282934069633,
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0... |
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 | [
-0.016977112740278244,
-0.025106487795710564,
0.004252177197486162,
0.06659241765737534,
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0.... |
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 | [
-0.032354701310396194,
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0... |
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 | [
-0.009542015381157398,
-0.048578184098005295,
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0.05... |
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 | [
-0.03831576555967331,
-0.011629721149802208,
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0.03583855554461479,
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0.03128974884748459,
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0.03198... |
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 | [
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0... |
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 | [
-0.02508062869310379,
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0.0074296556413173676,
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... |
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 | [
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0.0053... |
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 | [
-0.03150958567857742,
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0.0053... |
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 | [
0.0340476855635643,
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0.02090582065284252,
0.03867132589221001,
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0.06205449625849724,
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0.021969232708215714,
0.04923618584871292,
-0.007099947426468134,
-0.0... |
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 | [
0.0340476855635643,
-0.025160448625683784,
0.02090582065284252,
0.03867132589221001,
0.044571686536073685,
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0.06205449625849724,
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0.021969232708215714,
0.04923618584871292,
-0.007099947426468134,
-0.0... |
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 | [
0.0012577211018651724,
-0.026373008266091347,
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0.009522274136543274,
0.011495590209960938,
-0.007638220675289631,
0.... |
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 | [
-0.0006859668646939099,
-0.02123185805976391,
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0.008495081216096878,
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... |
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 | [
0.010088027454912663,
-0.036155980080366135,
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0.007760807406157255,
0.01522879209369421,
-0.0025131015572696924,
... |
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 | [
0.004792075138539076,
-0.007761154789477587,
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0.0080652991309762,
-0.011914205737411976,
... |
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 | [
0.008438386023044586,
-0.03182337433099747,
0.02120896428823471,
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0.006366734858602285,
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0.0034690515603870153,
0.012431615963578224,
-0.003928150050342083,
0... |
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 | [
0.009633473120629787,
-0.025845088064670563,
0.023455126211047173,
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0.021458882838487625,
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0.0074913762509822845,
0.012680097483098507,
-0.009106897749006748,
... |
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 | [
-0.0000921444661798887,
-0.02330685220658779,
0.01766706071794033,
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0.020055683329701424,
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0.0074780406430363655,
0.009630050510168076,
-0.010368972085416317,
... |
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 | [
0.004625806584954262,
-0.011270769871771336,
0.019485654309391975,
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0.009664970450103283,
0.008149276487529278,
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0.009957240894436836,
-0.011730670928955078,
... |
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 | [
0.0006767016602680087,
-0.0225151926279068,
0.017429402098059654,
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-0.027000654488801956,
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0.019963035359978676,
0.01135605201125145,
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0.009301034733653069,
0.011383738368749619,
-0.011081239208579063,
0.0... |
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 | [
-0.009525618515908718,
0.019547080621123314,
0.024603724479675293,
-0.00591616565361619,
0.012935626320540905,
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0.00960543379187584,
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0.0125564681366086,
0.015011661686003208,
0.012... |
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 | [
-0.014943151734769344,
0.01857294887304306,
0.021716073155403137,
0.004247463308274746,
0.002691781148314476,
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0.010487981140613556,
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0.01850670576095581,
0.022977445274591446,
0.0... |
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 | [
-0.028023220598697662,
0.001047179801389575,
0.009279809892177582,
0.008870710618793964,
0.0056627974845469,
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0.013984031043946743,
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0.010690422728657722,
0.02585281990468502,
0.01... |
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 | [
-0.02518361434340477,
0.004917951766401529,
0.011951933614909649,
0.0010243221186101437,
0.006683762650936842,
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0.009895403869450092,
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0.02224479429423809,
0.016192946583032608,
0.003... |
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 | [
-0.015500779263675213,
0.0028178016655147076,
0.021606910973787308,
0.008869594894349575,
0.00406844774261117,
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0.011238446459174156,
-0.008645819500088692,
0.02232912927865982,
0.022291699424386024,
0.... |
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 | [
-0.028675232082605362,
0.013877459801733494,
0.01209307461977005,
0.005334562622010708,
0.00016533734742552042,
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-0.006871962454169989,
0.01890924945473671,
0.012526916339993477,
... |
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 | [
-0.01945430226624012,
0.010728294029831886,
0.006701436825096607,
-0.019080182537436485,
0.020824631676077843,
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0.00018393363279756159,
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0.005501779727637768,
0.0... |
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 | [
-0.01696798950433731,
0.016537832096219063,
0.0038334731943905354,
-0.010884628631174564,
0.028683507815003395,
0.01815728284418583,
0.023581551387906075,
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0.03713813051581383,
0.013371051289141178,
0.02843749150633812,
0.014382953755557537,
0.0046101463958621025,
0.10... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
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0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
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-0.0258162971585989,
0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
-0.05898710712790489,
-0.0258162971585989,
0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
-0.05898710712790489,
-0.0258162971585989,
0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
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0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
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0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
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0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
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0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
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0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
-0.05898710712790489,
-0.0258162971585989,
0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
-0.0030708021949976683,
0.05843428522348404,
0.0012720288941636682,
-0.017455609515309334,
0.05463109910488129,
0.014184357598423958,
0.06388133019208908,
0.054695334285497665,
-0.05898710712790489,
-0.0258162971585989,
0.014241842553019524,
0.06672751903533936,
0.02879747562110424,
0.0761... |
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 | [
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0.039525095373392105,
-... |
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 | [
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0.007359114941209555,
-0.0226... |
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 | [
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0.007359114941209555,
-0.0226... |
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 | [
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-0.0226... |
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 | [
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-0.0226... |
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 | [
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0.007359114941209555,
-0.0226... |
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 | [
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0.01542530208826065,
-0.04746948182582855,
0.007359114941209555,
-0.0226... |
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 | [
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0.01542530208826065,
-0.04746948182582855,
0.007359114941209555,
-0.0226... |
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 | [
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0.028898028... |
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 | [
0.0380270890891552,
0.03939254954457283,
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0.025804894044995308,
0.028898028... |
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 | [
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0.0535... |
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 | [
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0.06448133289813995,
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-0.038871750235557556,
0.... |
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 | [
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0.04084367677569389,
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0.07... |
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 | [
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0.05063099414110184,
0.0462778... |
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 | [
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0.023674994707107544,
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0.09079822152853012,
0.05063099414110184,
0.0462778... |
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 | [
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0.023674994707107544,
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0.09079822152853012,
0.05063099414110184,
0.0462778... |
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 | [
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0.05063099414110184,
0.0462778... |
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 | [
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-0.007362596690654755,
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0.03357966989278793,
0.054816700518131256,
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0.014105879701673985,
0.029307931661605835,
-0.01198180764913559,
0.020886370912194252,
-0.... |
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 | [
-0.032495398074388504,
0.007685363758355379,
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-0.007362596690654755,
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0.029307931661605835,
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0.020886370912194252,
-0.... |
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 | [
-0.032495398074388504,
0.007685363758355379,
-0.022831320762634277,
-0.007362596690654755,
-0.03733019530773163,
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0.03357966989278793,
0.054816700518131256,
0.0354839526116848,
0.014105879701673985,
0.029307931661605835,
-0.01198180764913559,
0.020886370912194252,
-0.... |
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 | [
-0.011847114190459251,
0.016442812979221344,
-0.02125507779419422,
-0.051544107496738434,
0.03685201331973076,
0.007330151274800301,
0.033196792006492615,
0.022770097479224205,
-0.023734230548143387,
0.04033970087766647,
0.04774981737136841,
-0.008934104815125465,
0.0025936979800462723,
0.... |
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 | [
-0.029686974361538887,
-0.037656255066394806,
-0.023043960332870483,
-0.01871373876929283,
0.049484096467494965,
0.013717450201511383,
0.01580519787967205,
0.015454576350748539,
-0.003856040071696043,
0.04144587367773056,
0.036085035651922226,
0.023050907999277115,
0.010637300089001656,
0.... |
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 | [
-0.016854235902428627,
0.010701826773583889,
-0.01749715954065323,
-0.0587841235101223,
0.040573637932538986,
0.011614959686994553,
0.026002945378422737,
0.018880341202020645,
-0.0033239107578992844,
0.030450381338596344,
0.03821737319231033,
-0.005546057131141424,
0.010150695219635963,
0.... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
0.012713306583464146,
-0.04474521800875664,
0.04835527017712593,
-0.04814174026250839,
0.059141308069229126,
-0.002478641225025058,
0.07729700952768326,
0.03944157436490059,
-0.004582626279443502,
0.014888603240251541,
-0.005347938742488623,
0.019942758604884148,
0.0169222354888916,
0.0621... |
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 | [
-0.008507289923727512,
-0.030071374028921127,
0.03876306489109993,
0.011189251206815243,
0.012668200768530369,
-0.013031925074756145,
0.012571706436574459,
0.007223522290587425,
0.018361616879701614,
0.03511262312531471,
0.020620577037334442,
0.04894383251667023,
-0.006114341784268618,
0.0... |
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 | [
-0.008507289923727512,
-0.030071374028921127,
0.03876306489109993,
0.011189251206815243,
0.012668200768530369,
-0.013031925074756145,
0.012571706436574459,
0.007223522290587425,
0.018361616879701614,
0.03511262312531471,
0.020620577037334442,
0.04894383251667023,
-0.006114341784268618,
0.0... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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 | [
0.0022136715706437826,
-0.1043378934264183,
-0.0021517882123589516,
-0.023028621450066566,
0.01035364344716072,
-0.012298503890633583,
0.04997536912560463,
0.003838179400190711,
0.04465098679065704,
0.01796073652803898,
0.009828987531363964,
0.02413065731525421,
0.046710189431905746,
0.022... |
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