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/31566 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Jun 17, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/15697733135)** (Jun 17, 2025)
COMMENT:
From the logs, looks like a timeout, closing to see if it happens again. | 31,566 | [
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https://github.com/scikit-learn/scikit-learn/issues/31555 | [
"Bug",
"Needs Triage"
] | is_classifier returns False for custom classifier wrappers in scikit-learn 1.6.1, even with ClassifierMixin and _estimator_type
### Describe the bug
#### Describe the bug
Since upgrading to scikit-learn 1.6.1, the utility function `is_classifier` always returns `False` for custom classifier wrappers, even if they in... | 31,555 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31554 | [
"Performance",
"help wanted",
"module:metrics",
"Needs Investigation"
] | Allow batch based metrics calculation of sklearn.metrics
### Describe the workflow you want to enable
I have a lot of data and need to calculate metrics such as accuracy_score, jaccard_score, f1_score, recall, precision etc.
### Describe your proposed solution
When I try to calculate these it can literally take da... | 31,554 | [
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https://github.com/scikit-learn/scikit-learn/issues/31546 | [
"Bug",
"Regression"
] | Regression in `DecisionBoundaryDisplay.from_estimator` with `colors` and `plot_method='contour'` after upgrading to v1.7.0
### Describe the bug
Hello. Recently, after upgrading to scikit-learn v1.7.0, I encountered an issue when using `DecisionBoundaryDisplay.from_estimator` with the `colors` keyword argument. Specif... | 31,546 | [
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https://github.com/scikit-learn/scikit-learn/issues/31546 | [
"Bug",
"Regression"
] | Regression in `DecisionBoundaryDisplay.from_estimator` with `colors` and `plot_method='contour'` after upgrading to v1.7.0
### Describe the bug
Hello. Recently, after upgrading to scikit-learn v1.7.0, I encountered an issue when using `DecisionBoundaryDisplay.from_estimator` with the `colors` keyword argument. Specif... | 31,546 | [
-0.0027667968533933163,
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0.03615031763911247,
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0.09266838431358337,
0.008925292640924454,
0.002... |
https://github.com/scikit-learn/scikit-learn/issues/31546 | [
"Bug",
"Regression"
] | Regression in `DecisionBoundaryDisplay.from_estimator` with `colors` and `plot_method='contour'` after upgrading to v1.7.0
### Describe the bug
Hello. Recently, after upgrading to scikit-learn v1.7.0, I encountered an issue when using `DecisionBoundaryDisplay.from_estimator` with the `colors` keyword argument. Specif... | 31,546 | [
-0.0027667968533933163,
0.023622848093509674,
0.03615031763911247,
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0.016516853123903275,
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0.012485361658036709,
0.09266838431358337,
0.008925292640924454,
0.002... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.048535991460084915,
0.012275722809135914,
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0.03611382842063904,
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... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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-0.011... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.04380996152758598,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.046644002199172974,
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0.005993958562612534,
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0.033555690199136734,
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.04017331823706627,
0.008168958127498627,
0.012102887965738773,
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0.029843997210264206,
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... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.04674297571182251,
0.012719209305942059,
0.0007167204166762531,
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0.008541954681277275,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.03500929847359657,
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.03526291996240616,
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https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.03839188441634178,
0.017461596056818962,
0.014083603397011757,
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0.01664653606712818,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.043516963720321655,
0.03072665072977543,
-0.0008658057195134461,
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0.02694624476134777,
0.039598722010850906,
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-0.026994137093424797... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.04192740470170975,
0.050693999975919724,
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0.0003367815224919468,
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0.03880394250154495,
0.006568219047039747,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31542 | [
"New Feature",
"help wanted",
"Hard"
] | Huber Loss for HistGradientBoostingRegressor
### Describe the workflow you want to enable
Huber loss is available as an option for `GradientBoostingRegressor` and works great when training on data with frequent outliers (thank you!). `HistGradientBoostingRegressor` however does not support Huber loss, which may be re... | 31,542 | [
-0.04498443752527237,
0.02215578407049179,
0.0008702209452167153,
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0.01862943358719349,
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0.0403134822845459,
0.02230890281498432,
0.014907574281096458,
-0.019421417266130447,
-0.023... |
https://github.com/scikit-learn/scikit-learn/issues/31540 | [
"Enhancement",
"API",
"Needs Decision"
] | Make `sklearn.metrics._scorer._MultimetricScorer` part of the public API
### Describe the workflow you want to enable
This tool is great to run multiple scorers on a single estimator thanks to the caching mechanism. It is a bummer that it is not part of the public API.
### Describe your proposed solution
Make it pa... | 31,540 | [
-0.022357160225510597,
0.09161663055419922,
0.038232360035181046,
0.018597930669784546,
0.009506053291261196,
0.0010365904308855534,
0.09885232150554657,
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0.021007400006055832,
0.00289322342723608,
-0.03857690095901489,
0.047723736613988876,
-0.02768533304333687,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/31540 | [
"Enhancement",
"API",
"Needs Decision"
] | Make `sklearn.metrics._scorer._MultimetricScorer` part of the public API
### Describe the workflow you want to enable
This tool is great to run multiple scorers on a single estimator thanks to the caching mechanism. It is a bummer that it is not part of the public API.
### Describe your proposed solution
Make it pa... | 31,540 | [
-0.034123245626688004,
0.07705342769622803,
0.030969616025686264,
-0.021497486159205437,
0.004664113279432058,
0.0053522950038313866,
0.07668548077344894,
-0.023416943848133087,
0.01784357614815235,
-0.0010427820961922407,
-0.02163826860487461,
0.05482852831482887,
-0.026105990633368492,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31538 | [
"Bug",
"Needs Triage"
] | 当selector = VarianceThreshold(threshold=0.1)和selector = VarianceThreshold()输出的结果不一样
### Describe the bug
import numpy as np
X = np.arange(30,dtype=float).reshape((10, 3))
X[:,1] = 1
from sklearn.feature_selection import VarianceThreshold
vt = VarianceThreshold(threshold=0.01)
xt = vt.fit_transform(X)
# 未设置阈值时，可能未实际计算... | 31,538 | [
-0.0026469151489436626,
-0.09988971054553986,
-0.00464478088542819,
-0.009223589673638344,
0.07721371203660965,
-0.01967705227434635,
0.005359751172363758,
-0.009497114457190037,
0.013313879258930683,
0.009881993755698204,
0.01368363481014967,
0.09640567004680634,
0.06391695141792297,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/31538 | [
"Bug",
"Needs Triage"
] | 当selector = VarianceThreshold(threshold=0.1)和selector = VarianceThreshold()输出的结果不一样
### Describe the bug
import numpy as np
X = np.arange(30,dtype=float).reshape((10, 3))
X[:,1] = 1
from sklearn.feature_selection import VarianceThreshold
vt = VarianceThreshold(threshold=0.01)
xt = vt.fit_transform(X)
# 未设置阈值时，可能未实际计算... | 31,538 | [
-0.0026469151489436626,
-0.09988971054553986,
-0.00464478088542819,
-0.009223589673638344,
0.07721371203660965,
-0.01967705227434635,
0.005359751172363758,
-0.009497114457190037,
0.013313879258930683,
0.009881993755698204,
0.01368363481014967,
0.09640567004680634,
0.06391695141792297,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/31538 | [
"Bug",
"Needs Triage"
] | 当selector = VarianceThreshold(threshold=0.1)和selector = VarianceThreshold()输出的结果不一样
### Describe the bug
import numpy as np
X = np.arange(30,dtype=float).reshape((10, 3))
X[:,1] = 1
from sklearn.feature_selection import VarianceThreshold
vt = VarianceThreshold(threshold=0.01)
xt = vt.fit_transform(X)
# 未设置阈值时，可能未实际计算... | 31,538 | [
-0.0026469151489436626,
-0.09988971054553986,
-0.00464478088542819,
-0.009223589673638344,
0.07721371203660965,
-0.01967705227434635,
0.005359751172363758,
-0.009497114457190037,
0.013313879258930683,
0.009881993755698204,
0.01368363481014967,
0.09640567004680634,
0.06391695141792297,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
-0.043526846915483475,
0.04038310423493385,
-0.03400878235697746,
0.04479062184691429,
0.005593789741396904,
0.011283159255981445,
0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
-0.043526846915483475,
0.04038310423493385,
-0.03400878235697746,
0.04479062184691429,
0.005593789741396904,
0.011283159255981445,
0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
-0.043526846915483475,
0.04038310423493385,
-0.03400878235697746,
0.04479062184691429,
0.005593789741396904,
0.011283159255981445,
0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
-0.043526846915483475,
0.04038310423493385,
-0.03400878235697746,
0.04479062184691429,
0.005593789741396904,
0.011283159255981445,
0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
-0.043526846915483475,
0.04038310423493385,
-0.03400878235697746,
0.04479062184691429,
0.005593789741396904,
0.011283159255981445,
0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
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0.04479062184691429,
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0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31536 | [
"Enhancement"
] | Improve sample_weight handling in sag(a)
### Describe the bug
This may be more of a discussion, but overall I am not sure what treatment of weighting would preserve the convergence guarantees for the SAG(A) solver. So far as I see it, at each update step we uniformly select some index $i_j$ such that the update steps... | 31,536 | [
0.003627167083323002,
0.06154937297105789,
0.014246083796024323,
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0.04479062184691429,
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0.02249855548143387,
0.06903403252363205,
0.02067776769399643,
0.0016493480652570724,
-0.00296... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.023505523800849915,
0.12086042761802673,
0.007977081462740898,
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0.046571847051382065,
0.01726187765598297,
0.008... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.025456314906477928,
0.12427868694067001,
0.011060034856200218,
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0.02783019095659256,
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0.003550816560164094,
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0.05795757845044136,
0.03567606955766678,
0.01404997706413269,
0.01059698... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.017995933070778847,
0.10631192475557327,
0.01110838819295168,
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0.029042379930615425,
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0.011648380197584629,
0.014248392544686794,
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0.01861726865172386,
0.04223785921931267,
0.011594902724027634,
0.027927320450544357,
-0.01182... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.02744748815894127,
0.1118529736995697,
0.008443973958492279,
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0.0342230424284935,
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0.0021370016038417816,
0.0018873325316235423,
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0.02847042866051197,
0.052957821637392044,
0.024614805355668068,
0.02012208104133606,
0.01676569... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.04077407717704773,
0.1040806844830513,
0.016731228679418564,
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0.0565873458981514,
0.03043914958834648,
0.015056717209517956,
0.0044... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.0293162502348423,
0.11602824181318283,
0.011290472000837326,
-0.03336484357714653,
0.03310082107782364,
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0.002357450081035495,
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0.02624661847949028,
0.05585396662354469,
0.03253799304366112,
0.020751450210809708,
-0.0006... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.029362285509705544,
0.11642458289861679,
0.010910853743553162,
-0.033101294189691544,
0.030830545350909233,
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0.0032401576172560453,
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0.055017050355672836,
0.033065613359212875,
0.019299479201436043,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31533 | [
"RFC",
"Array API"
] | RFC: stop using scikit-learn `stable_cumsum` and instead use `np.cumsum/xp.cumulative_sum` directly
As discussed in https://github.com/scikit-learn/scikit-learn/pull/30878/files#r2142562746, our current `stable_cumsum` function brings very little value to the user: it does extra computation to check that `np.allclose(... | 31,533 | [
-0.02380344085395336,
0.11568132042884827,
0.013252127915620804,
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0.02483551762998104,
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0.0017347678076475859,
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0.02981293760240078,
0.06295468658208847,
0.040725041180849075,
0.022792955860495567,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/31527 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Jun 12, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/15601223966)** (Jun 12, 2025)
COMMENT:
The free-threaded failures are likely due to cibuildwheel 3.0.0 release, from [changelog](https://cibuildwheel.pypa.io/en/stable... | 31,527 | [
-0.05534553527832031,
0.005930350162088871,
0.012286527082324028,
-0.01833350583910942,
-0.025669004768133163,
0.045255329459905624,
0.020493101328611374,
0.024222731590270996,
-0.034197933971881866,
0.016401581466197968,
0.03939592465758324,
0.02592942677438259,
-0.029686501249670982,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31525 | [
"Bug"
] | Issue with the `RidgeCV` diagram representation with non-default alphas
It seems that we introduced a regression in the HTML representation. The following code is failing:
```python
import numpy as np
from sklearn.linear_model import RidgeCV
RidgeCV(np.logspace(-3, 3, num=10)
```
leads to the following error:
```p... | 31,525 | [
0.0579189769923687,
0.029923273250460625,
0.03298121318221092,
0.014044271782040596,
0.07219812273979187,
-0.015608718618750572,
0.027623916044831276,
0.06617040932178497,
-0.04338102787733078,
-0.03674878552556038,
-0.023100897669792175,
0.08440165966749191,
0.007333236280828714,
0.016692... |
https://github.com/scikit-learn/scikit-learn/issues/31521 | [
"Bug",
"Regression"
] | TarFile.extractall() got an unexpected keyword argument 'filter'
### Describe the bug
For the latest version `1.7.0`, it can be installed with Python 3.10, but the parameter `filter` is available starting from Python 3.12 (See: https://docs.python.org/3/library/tarfile.html#tarfile.TarFile.extractall ).
https://gith... | 31,521 | [
0.05696839466691017,
0.030541419982910156,
-0.01945788785815239,
0.015237005427479744,
0.06829768419265747,
0.039019666612148285,
-0.011113808490335941,
0.07417171448469162,
0.031216872856020927,
-0.019047560170292854,
-0.0012619690969586372,
0.011804205365478992,
-0.01871584728360176,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31520 | [
"Bug",
"Needs Investigation"
] | 32-Bit Raspberry Pi OS Installation Issues with UV
### Describe the bug
When attempting to install scikit-learn==1.4.2 - 1.6.1 on Raspberry Pi OS Lite 32-Bit (Bookworm) or Raspberry Pi OS Lit 32-Bit (Bullseye) with UV, the following error is given:
```
× Failed to download and build `scikit-learn==1.4.2`
├─▶ Fail... | 31,520 | [
0.0410008542239666,
-0.0008773438748903573,
-0.0017104516737163067,
-0.0587514229118824,
-0.008132032118737698,
0.016029709950089455,
0.017804168164730072,
0.03467065840959549,
0.04465901479125023,
-0.02490655705332756,
0.026197524741292,
0.08568243682384491,
0.00819757953286171,
0.0026710... |
https://github.com/scikit-learn/scikit-learn/issues/31520 | [
"Bug",
"Needs Investigation"
] | 32-Bit Raspberry Pi OS Installation Issues with UV
### Describe the bug
When attempting to install scikit-learn==1.4.2 - 1.6.1 on Raspberry Pi OS Lite 32-Bit (Bookworm) or Raspberry Pi OS Lit 32-Bit (Bullseye) with UV, the following error is given:
```
× Failed to download and build `scikit-learn==1.4.2`
├─▶ Fail... | 31,520 | [
0.0410008542239666,
-0.0008773438748903573,
-0.0017104516737163067,
-0.0587514229118824,
-0.008132032118737698,
0.016029709950089455,
0.017804168164730072,
0.03467065840959549,
0.04465901479125023,
-0.02490655705332756,
0.026197524741292,
0.08568243682384491,
0.00819757953286171,
0.0026710... |
https://github.com/scikit-learn/scikit-learn/issues/31520 | [
"Bug",
"Needs Investigation"
] | 32-Bit Raspberry Pi OS Installation Issues with UV
### Describe the bug
When attempting to install scikit-learn==1.4.2 - 1.6.1 on Raspberry Pi OS Lite 32-Bit (Bookworm) or Raspberry Pi OS Lit 32-Bit (Bullseye) with UV, the following error is given:
```
× Failed to download and build `scikit-learn==1.4.2`
├─▶ Fail... | 31,520 | [
0.0410008542239666,
-0.0008773438748903573,
-0.0017104516737163067,
-0.0587514229118824,
-0.008132032118737698,
0.016029709950089455,
0.017804168164730072,
0.03467065840959549,
0.04465901479125023,
-0.02490655705332756,
0.026197524741292,
0.08568243682384491,
0.00819757953286171,
0.0026710... |
https://github.com/scikit-learn/scikit-learn/issues/31520 | [
"Bug",
"Needs Investigation"
] | 32-Bit Raspberry Pi OS Installation Issues with UV
### Describe the bug
When attempting to install scikit-learn==1.4.2 - 1.6.1 on Raspberry Pi OS Lite 32-Bit (Bookworm) or Raspberry Pi OS Lit 32-Bit (Bullseye) with UV, the following error is given:
```
× Failed to download and build `scikit-learn==1.4.2`
├─▶ Fail... | 31,520 | [
0.0410008542239666,
-0.0008773438748903573,
-0.0017104516737163067,
-0.0587514229118824,
-0.008132032118737698,
0.016029709950089455,
0.017804168164730072,
0.03467065840959549,
0.04465901479125023,
-0.02490655705332756,
0.026197524741292,
0.08568243682384491,
0.00819757953286171,
0.0026710... |
https://github.com/scikit-learn/scikit-learn/issues/31520 | [
"Bug",
"Needs Investigation"
] | 32-Bit Raspberry Pi OS Installation Issues with UV
### Describe the bug
When attempting to install scikit-learn==1.4.2 - 1.6.1 on Raspberry Pi OS Lite 32-Bit (Bookworm) or Raspberry Pi OS Lit 32-Bit (Bullseye) with UV, the following error is given:
```
× Failed to download and build `scikit-learn==1.4.2`
├─▶ Fail... | 31,520 | [
0.0410008542239666,
-0.0008773438748903573,
-0.0017104516737163067,
-0.0587514229118824,
-0.008132032118737698,
0.016029709950089455,
0.017804168164730072,
0.03467065840959549,
0.04465901479125023,
-0.02490655705332756,
0.026197524741292,
0.08568243682384491,
0.00819757953286171,
0.0026710... |
https://github.com/scikit-learn/scikit-learn/issues/31512 | [
"New Feature"
] | Add free-threading wheel for Linux arm64 (aarch64)
### Describe the workflow you want to enable
I am a maintainer for the third-party package [fastcan](https://github.com/scikit-learn-contrib/fastcan). I tested the package on the free-threading Python (cp313t), and found scikit-learn missing a wheel for Linux arm64 (... | 31,512 | [
-0.04134967178106308,
-0.02092185989022255,
0.0029553703498095274,
0.014913240447640419,
-0.01323819812387228,
0.028251182287931442,
0.05867059528827667,
-0.0031833709217607975,
-0.023597851395606995,
0.008496114052832127,
0.0026401651557534933,
0.03746235370635986,
-0.027540799230337143,
... |
https://github.com/scikit-learn/scikit-learn/issues/31503 | [
"New Feature",
"help wanted",
"Hard"
] | HDBSCAN performance issues compared to original hdbscan implementation (likely because Boruvka algorithm is not implemented)
### Describe the bug
When switching from Sklearn HDBSCAN implementation to original one from `hdbscan` library, I've notice that Sklearn's implementation has much worse implementation. I've tri... | 31,503 | [
-0.027064664289355278,
-0.07314897328615189,
-0.005982648581266403,
-0.004365256056189537,
-0.04171433672308922,
-0.03054416924715042,
0.008398744277656078,
0.03071024641394615,
0.0031991133000701666,
0.003718582447618246,
0.026806432753801346,
-0.018525809049606323,
0.028491545468568802,
... |
https://github.com/scikit-learn/scikit-learn/issues/31503 | [
"New Feature",
"help wanted",
"Hard"
] | HDBSCAN performance issues compared to original hdbscan implementation (likely because Boruvka algorithm is not implemented)
### Describe the bug
When switching from Sklearn HDBSCAN implementation to original one from `hdbscan` library, I've notice that Sklearn's implementation has much worse implementation. I've tri... | 31,503 | [
-0.027064664289355278,
-0.07314897328615189,
-0.005982648581266403,
-0.004365256056189537,
-0.04171433672308922,
-0.03054416924715042,
0.008398744277656078,
0.03071024641394615,
0.0031991133000701666,
0.003718582447618246,
0.026806432753801346,
-0.018525809049606323,
0.028491545468568802,
... |
https://github.com/scikit-learn/scikit-learn/issues/31498 | [
"Bug",
"Needs Triage"
] | Doc website incorrectly flags stable as unstable
### Describe the bug
Current website gives:
I tried having a look on how to fix this, but went in a rabbit hole that the version switcher is generated by "list_versions.py" in th... | 31,498 | [
0.033129069954156876,
-0.022215979173779488,
-0.03498503193259239,
-0.025264810770750046,
0.014927203767001629,
0.025238746777176857,
-0.026306774467229843,
0.04276761785149574,
0.0411958172917366,
-0.032315853983163834,
0.043389081954956055,
0.025553962215781212,
0.004589970223605633,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31475 | [
"Needs Investigation"
] | MultiOutputRegressor can't process estimators with synchronization primitives
### Describe the bug
[MultiOutputRegressor ](https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html) can't process estimators with threading/multiprocessing synchronization primitives
I want to prop... | 31,475 | [
-0.038797758519649506,
0.06017332524061203,
0.015629366040229797,
-0.025962527841329575,
0.002099462551996112,
0.007923395372927189,
0.06292477995157242,
-0.009604738093912601,
0.008793139830231667,
0.015313228592276573,
0.01386609673500061,
0.06224028021097183,
-0.03465581685304642,
0.055... |
https://github.com/scikit-learn/scikit-learn/issues/31475 | [
"Needs Investigation"
] | MultiOutputRegressor can't process estimators with synchronization primitives
### Describe the bug
[MultiOutputRegressor ](https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html) can't process estimators with threading/multiprocessing synchronization primitives
I want to prop... | 31,475 | [
-0.038797758519649506,
0.06017332524061203,
0.015629366040229797,
-0.025962527841329575,
0.002099462551996112,
0.007923395372927189,
0.06292477995157242,
-0.009604738093912601,
0.008793139830231667,
0.015313228592276573,
0.01386609673500061,
0.06224028021097183,
-0.03465581685304642,
0.055... |
https://github.com/scikit-learn/scikit-learn/issues/31475 | [
"Needs Investigation"
] | MultiOutputRegressor can't process estimators with synchronization primitives
### Describe the bug
[MultiOutputRegressor ](https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html) can't process estimators with threading/multiprocessing synchronization primitives
I want to prop... | 31,475 | [
-0.038797758519649506,
0.06017332524061203,
0.015629366040229797,
-0.025962527841329575,
0.002099462551996112,
0.007923395372927189,
0.06292477995157242,
-0.009604738093912601,
0.008793139830231667,
0.015313228592276573,
0.01386609673500061,
0.06224028021097183,
-0.03465581685304642,
0.055... |
https://github.com/scikit-learn/scikit-learn/issues/31473 | [
"New Feature"
] | Add option to return final cross-validation score in SequentialFeatureSelector
### Describe the workflow you want to enable
Currently, when using `SequentialFeatureSelector`, it internally performs cross-validation to decide which features to select, based on the scoring function. However, the final cross-validation ... | 31,473 | [
-0.054013025015592575,
-0.018176643177866936,
0.02492656372487545,
-0.03382604569196701,
0.04306749626994133,
-0.03322262316942215,
-0.0050322734750807285,
-0.010459612123668194,
0.05110042542219162,
0.011353514157235622,
0.018491892144083977,
0.05282134562730789,
0.009181969799101353,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31473 | [
"New Feature"
] | Add option to return final cross-validation score in SequentialFeatureSelector
### Describe the workflow you want to enable
Currently, when using `SequentialFeatureSelector`, it internally performs cross-validation to decide which features to select, based on the scoring function. However, the final cross-validation ... | 31,473 | [
-0.052873581647872925,
-0.006022958550602198,
0.03335241600871086,
-0.04180791601538658,
0.054930076003074646,
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-0.028685417026281357,
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0.06560975313186646,
0.003712370991706848,
-0.011218909174203873,
0.048462655395269394,
0.009027471765875816,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31462 | [
"New Feature",
"Needs Decision - Include Feature"
] | Feat: DummyClassifier strategy that produces randomized probabilities
### Describe the workflow you want to enable
# Motivation
The `dummy` module is fantastic for testing pipelines all the way up through enterprise scales. The [strategies](https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73/sklearn/dummy.py... | 31,462 | [
-0.02686155214905739,
0.09682884812355042,
0.02482978254556656,
-0.037698861211538315,
0.01863059401512146,
-0.06505314260721207,
0.03928108140826225,
0.01052639726549387,
-0.014908790588378906,
-0.015643687918782234,
0.03482870012521744,
0.0026568348985165358,
-0.038818322122097015,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/31450 | [
"New Feature",
"Needs Decision - Include Feature"
] | Spherical K-means support (unit norm centroids and input)
### Describe the workflow you want to enable
Hi,
I was wondering if there is—or has been—any initiative to support cosine similarity in the KMeans implementation (i.e., spherical KMeans). I find the algorithm quite useful and would be happy to propose an imple... | 31,450 | [
-0.026279577985405922,
-0.01819411665201187,
-0.03783472254872322,
-0.015498669818043709,
-0.013981690630316734,
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0.06523557752370834,
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-0.009993846528232098,
0.01851876825094223,
-0.0009677062625996768,
-0.017422502860426903,... |
https://github.com/scikit-learn/scikit-learn/issues/31450 | [
"New Feature",
"Needs Decision - Include Feature"
] | Spherical K-means support (unit norm centroids and input)
### Describe the workflow you want to enable
Hi,
I was wondering if there is—or has been—any initiative to support cosine similarity in the KMeans implementation (i.e., spherical KMeans). I find the algorithm quite useful and would be happy to propose an imple... | 31,450 | [
-0.02509067952632904,
-0.017414717003703117,
-0.03804108127951622,
-0.02015763521194458,
-0.017655866220593452,
-0.0050340197049081326,
0.06341541558504105,
-0.00762644037604332,
-0.0326671339571476,
-0.008921581320464611,
0.015713578090071678,
0.002337211975827813,
-0.013213316909968853,
... |
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