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|---|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/26491 | [
"API",
"Needs Decision",
"module:inspection"
] | Rename percentiles to quantile_levels in partial_dependence
`partial_dependence` and `PartialDependenceDisplay` have a parameter `percentiles` with range (0, 1). It should be either have range (0, 100) or be named `quantiles`.
This issue proposes to rename `percentiles` to ~~`quantiles`~~ `quantile_levels`.
This... | 26,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/26491 | [
"API",
"Needs Decision",
"module:inspection"
] | Rename percentiles to quantile_levels in partial_dependence
`partial_dependence` and `PartialDependenceDisplay` have a parameter `percentiles` with range (0, 1). It should be either have range (0, 100) or be named `quantiles`.
This issue proposes to rename `percentiles` to ~~`quantiles`~~ `quantile_levels`.
This... | 26,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/26491 | [
"API",
"Needs Decision",
"module:inspection"
] | Rename percentiles to quantile_levels in partial_dependence
`partial_dependence` and `PartialDependenceDisplay` have a parameter `percentiles` with range (0, 1). It should be either have range (0, 100) or be named `quantiles`.
This issue proposes to rename `percentiles` to ~~`quantiles`~~ `quantile_levels`.
This... | 26,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/26491 | [
"API",
"Needs Decision",
"module:inspection"
] | Rename percentiles to quantile_levels in partial_dependence
`partial_dependence` and `PartialDependenceDisplay` have a parameter `percentiles` with range (0, 1). It should be either have range (0, 100) or be named `quantiles`.
This issue proposes to rename `percentiles` to ~~`quantiles`~~ `quantile_levels`.
This... | 26,491 | [
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https://github.com/scikit-learn/scikit-learn/issues/26482 | [
"Enhancement",
"Moderate",
"help wanted",
"module:decomposition",
"module:test-suite"
] | Run common test for SparseCoder and FeatureUnion
Currently, the `SparseCoder` estimator is not tested by our common test because it requires a `dictionary` parameter.
We should make sure to construct the instance in `_construct_instance` and check that the estimator runs the test. I am almost sure that it is missin... | 26,482 | [
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https://github.com/scikit-learn/scikit-learn/issues/26482 | [
"Enhancement",
"Moderate",
"help wanted",
"module:decomposition",
"module:test-suite"
] | Run common test for SparseCoder and FeatureUnion
Currently, the `SparseCoder` estimator is not tested by our common test because it requires a `dictionary` parameter.
We should make sure to construct the instance in `_construct_instance` and check that the estimator runs the test. I am almost sure that it is missin... | 26,482 | [
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https://github.com/scikit-learn/scikit-learn/issues/26482 | [
"Enhancement",
"Moderate",
"help wanted",
"module:decomposition",
"module:test-suite"
] | Run common test for SparseCoder and FeatureUnion
Currently, the `SparseCoder` estimator is not tested by our common test because it requires a `dictionary` parameter.
We should make sure to construct the instance in `_construct_instance` and check that the estimator runs the test. I am almost sure that it is missin... | 26,482 | [
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https://github.com/scikit-learn/scikit-learn/issues/26482 | [
"Enhancement",
"Moderate",
"help wanted",
"module:decomposition",
"module:test-suite"
] | Run common test for SparseCoder and FeatureUnion
Currently, the `SparseCoder` estimator is not tested by our common test because it requires a `dictionary` parameter.
We should make sure to construct the instance in `_construct_instance` and check that the estimator runs the test. I am almost sure that it is missin... | 26,482 | [
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https://github.com/scikit-learn/scikit-learn/issues/26472 | [
"Bug"
] | Conflict assigning unique session to joblib processes in GridSearchCV
### Describe the bug
Kernel is freezing when `n_jobs` is not `None` and a wrong parameter is passed to an estimator inside a grid/random search.
VSCode raises an error, but jupyter notebooks just appear to be running thought they are not.
### Ste... | 26,472 | [
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-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26472 | [
"Bug"
] | Conflict assigning unique session to joblib processes in GridSearchCV
### Describe the bug
Kernel is freezing when `n_jobs` is not `None` and a wrong parameter is passed to an estimator inside a grid/random search.
VSCode raises an error, but jupyter notebooks just appear to be running thought they are not.
### Ste... | 26,472 | [
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0.04157445952296257,
0.012348397634923458,
0.007989678531885147,
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https://github.com/scikit-learn/scikit-learn/issues/26460 | [
"Documentation",
"Pandas compatibility"
] | Pandas set_output Development and Customer Transformers Best Practice
### Describe the issue linked to the documentation
The set_output feature is amazing. However, many of my pipelines have sklearn style custom transformers. It would be really useful to have some best practices on how to write custom transformer obj... | 26,460 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26460 | [
"Documentation",
"Pandas compatibility"
] | Pandas set_output Development and Customer Transformers Best Practice
### Describe the issue linked to the documentation
The set_output feature is amazing. However, many of my pipelines have sklearn style custom transformers. It would be really useful to have some best practices on how to write custom transformer obj... | 26,460 | [
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https://github.com/scikit-learn/scikit-learn/issues/26460 | [
"Documentation",
"Pandas compatibility"
] | Pandas set_output Development and Customer Transformers Best Practice
### Describe the issue linked to the documentation
The set_output feature is amazing. However, many of my pipelines have sklearn style custom transformers. It would be really useful to have some best practices on how to write custom transformer obj... | 26,460 | [
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https://github.com/scikit-learn/scikit-learn/issues/26460 | [
"Documentation",
"Pandas compatibility"
] | Pandas set_output Development and Customer Transformers Best Practice
### Describe the issue linked to the documentation
The set_output feature is amazing. However, many of my pipelines have sklearn style custom transformers. It would be really useful to have some best practices on how to write custom transformer obj... | 26,460 | [
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https://github.com/scikit-learn/scikit-learn/issues/26456 | [
"Documentation",
"help wanted",
"module:metrics"
] | Haversine distance documentation
### Describe the issue linked to the documentation
I would like to propose to update misleading formula for haversine distance:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
It is not clear from the page if (x1, y1) is the firs... | 26,456 | [
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https://github.com/scikit-learn/scikit-learn/issues/26456 | [
"Documentation",
"help wanted",
"module:metrics"
] | Haversine distance documentation
### Describe the issue linked to the documentation
I would like to propose to update misleading formula for haversine distance:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
It is not clear from the page if (x1, y1) is the firs... | 26,456 | [
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https://github.com/scikit-learn/scikit-learn/issues/26452 | [
"Documentation",
"Needs Triage"
] | Reference of partial_fit of MLP
### Describe the issue linked to the documentation
In MLPClassifier model, I can not find reference about partial_fit() function. Could you add the reference into the document that where this function comes from or just a progress by yourself?
### Suggest a potential alternative/fix
... | 26,452 | [
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https://github.com/scikit-learn/scikit-learn/issues/26446 | [
"Documentation",
"Needs Triage"
] | Misleading parameters in docs for `confustion_matrix()` function
### Describe the issue linked to the documentation
In the documentation for the `confustion_matrix()` function, classification metrics module (see [here][confusion_matrix_docs]), it gives this example:
```py
>>> tn, fp, fn, tp = confusion_matrix([... | 26,446 | [
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https://github.com/scikit-learn/scikit-learn/issues/26444 | [
"Documentation",
"module:linear_model"
] | Improvements in documentation and tests for perceptron classifier
### Describe the issue linked to the documentation
The documentation and tests for the Perceptron classifier ```sklearn/linear_model/tests/test_perceptron.py``` require enhancements to improve clarity, completeness, and usability. Currently, the docu... | 26,444 | [
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https://github.com/scikit-learn/scikit-learn/issues/26444 | [
"Documentation",
"module:linear_model"
] | Improvements in documentation and tests for perceptron classifier
### Describe the issue linked to the documentation
The documentation and tests for the Perceptron classifier ```sklearn/linear_model/tests/test_perceptron.py``` require enhancements to improve clarity, completeness, and usability. Currently, the docu... | 26,444 | [
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https://github.com/scikit-learn/scikit-learn/issues/26444 | [
"Documentation",
"module:linear_model"
] | Improvements in documentation and tests for perceptron classifier
### Describe the issue linked to the documentation
The documentation and tests for the Perceptron classifier ```sklearn/linear_model/tests/test_perceptron.py``` require enhancements to improve clarity, completeness, and usability. Currently, the docu... | 26,444 | [
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https://github.com/scikit-learn/scikit-learn/issues/26444 | [
"Documentation",
"module:linear_model"
] | Improvements in documentation and tests for perceptron classifier
### Describe the issue linked to the documentation
The documentation and tests for the Perceptron classifier ```sklearn/linear_model/tests/test_perceptron.py``` require enhancements to improve clarity, completeness, and usability. Currently, the docu... | 26,444 | [
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https://github.com/scikit-learn/scikit-learn/issues/26443 | [
"Bug"
] | PowerTransformer returns inconsistent index when transform output is set globally
### Describe the bug
When the _global_ transform output is set to "pandas" via `sklearn.set_config()`, `PowerTransformer.transform()` overrides the original index of the `DataFrame` with a `RangeIndex`. This issue does not occur with ot... | 26,443 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/26441 | [
"New Feature",
"Needs Triage"
] | Human Readable Rules for Decision Tree
### Describe the workflow you want to enable
Decision Tree should give the human readable if then rules apart from tree plot and structure results.
### Describe your proposed solution
I want to add a function to extract the rules from a decision tree in a simple "If/Then" rule... | 26,441 | [
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https://github.com/scikit-learn/scikit-learn/issues/26439 | [
"Needs Triage"
] | missing __version__!
Hello,
I am trying to run the scikitlearn OMP package using the source code. Inside the "/scikit-learn-main/sklearn.base.py" module, I see a line "from . import __version__", but there is no file called __version__
Thank you.
COMMENT:
Something probably went wrong when you installed scikit-le... | 26,439 | [
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https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
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0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.02997334487736225,
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0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.024821531027555466,
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0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.03006628341972828,
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0.0314... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.030863771215081215,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.024017948657274246,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.024991193786263466,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.028264960274100304,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.027252783998847008,
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0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26438 | [
"RFC"
] | RFC drop support for python 3.8 ? bump min dependencies ?
The 1.3 release being scheduled for the first half of june, it's time to think about bumping the min dependencies.
Up to now we've been trying to roughly follow [NEP29](https://numpy.org/neps/nep-0029-deprecation_policy.html) regarding supported Python versi... | 26,438 | [
0.02729812078177929,
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0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26430 | [
"New Feature",
"Needs Decision - Include Feature"
] | Conformal inference
### Describe the workflow you want to enable
Conformal prediction (CP) is a statistical technique for producing prediction sets without assumptions on the predictive algorithm (often a machine learning system) and only assuming exchangeability of the data. Given this method is assumption lean and ... | 26,430 | [
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0.081... |
https://github.com/scikit-learn/scikit-learn/issues/26430 | [
"New Feature",
"Needs Decision - Include Feature"
] | Conformal inference
### Describe the workflow you want to enable
Conformal prediction (CP) is a statistical technique for producing prediction sets without assumptions on the predictive algorithm (often a machine learning system) and only assuming exchangeability of the data. Given this method is assumption lean and ... | 26,430 | [
-0.030898885801434517,
0.13344749808311462,
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0.020619696006178856,
0.048527881503105164,
0.008001402951776981,
-0.023055503144860268,
... |
https://github.com/scikit-learn/scikit-learn/issues/26430 | [
"New Feature",
"Needs Decision - Include Feature"
] | Conformal inference
### Describe the workflow you want to enable
Conformal prediction (CP) is a statistical technique for producing prediction sets without assumptions on the predictive algorithm (often a machine learning system) and only assuming exchangeability of the data. Given this method is assumption lean and ... | 26,430 | [
-0.026296518743038177,
0.11897312849760056,
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0.01459968090057373,
0.045335639268159866,
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-0.031489115208387375,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26429 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/4941425045929984)** (May 25, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/5048220582150144) on May 26, 2023 | 26,429 | [
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0.013541958294808865,
0.0304... |
https://github.com/scikit-learn/scikit-learn/issues/26428 | [
"Enhancement",
"module:ensemble"
] | `HistGradientBoosting` should shuffle features when exploring for new splits
### Summary
Our implementation of `HistGradientBoosting` does not shuffle the feature at each node to find the best split. Note that our `GradientBoosting`, `RandomForest`, and `DecisionTree` use a Fisher-Yates permutation.
Not permutin... | 26,428 | [
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0.02846759743988514... |
https://github.com/scikit-learn/scikit-learn/issues/26428 | [
"Enhancement",
"module:ensemble"
] | `HistGradientBoosting` should shuffle features when exploring for new splits
### Summary
Our implementation of `HistGradientBoosting` does not shuffle the feature at each node to find the best split. Note that our `GradientBoosting`, `RandomForest`, and `DecisionTree` use a Fisher-Yates permutation.
Not permutin... | 26,428 | [
0.0029265228658914566,
0.0023049404844641685,
0.004316095262765884,
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0.0001283205347135663,
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-0.019682956859469414,
0.00914076529443264,
-0.036096975207328796,
0.02846759743988514... |
https://github.com/scikit-learn/scikit-learn/issues/26428 | [
"Enhancement",
"module:ensemble"
] | `HistGradientBoosting` should shuffle features when exploring for new splits
### Summary
Our implementation of `HistGradientBoosting` does not shuffle the feature at each node to find the best split. Note that our `GradientBoosting`, `RandomForest`, and `DecisionTree` use a Fisher-Yates permutation.
Not permutin... | 26,428 | [
0.0029265228658914566,
0.0023049404844641685,
0.004316095262765884,
-0.014798218384385109,
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0.0001283205347135663,
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-0.026015689596533775,
-0.019682956859469414,
0.00914076529443264,
-0.036096975207328796,
0.02846759743988514... |
https://github.com/scikit-learn/scikit-learn/issues/26428 | [
"Enhancement",
"module:ensemble"
] | `HistGradientBoosting` should shuffle features when exploring for new splits
### Summary
Our implementation of `HistGradientBoosting` does not shuffle the feature at each node to find the best split. Note that our `GradientBoosting`, `RandomForest`, and `DecisionTree` use a Fisher-Yates permutation.
Not permutin... | 26,428 | [
0.0029265228658914566,
0.0023049404844641685,
0.004316095262765884,
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0.0001283205347135663,
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-0.019682956859469414,
0.00914076529443264,
-0.036096975207328796,
0.02846759743988514... |
https://github.com/scikit-learn/scikit-learn/issues/26421 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/6424992615759872)** (May 24, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/6424992615759872) on May 24, 2023 | 26,421 | [
-0.023264097049832344,
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0.011196046136319637,
0.02531... |
https://github.com/scikit-learn/scikit-learn/issues/26421 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/6424992615759872)** (May 24, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/6424992615759872) on May 24, 2023 | 26,421 | [
-0.023264097049832344,
-0.008879345841705799,
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0.02531... |
https://github.com/scikit-learn/scikit-learn/issues/26419 | [
"Bug"
] | The twitter workflow stopped working a few days ago
see https://github.com/scikit-learn/scikit-learn/actions/workflows/twitter.yml
COMMENT:
I am in favor of removing it all together, because I do not see much value in keeping https://twitter.com/sklearn_commits around.
(Personally, I do not use twitter to keep tra... | 26,419 | [
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0.09... |
https://github.com/scikit-learn/scikit-learn/issues/26419 | [
"Bug"
] | The twitter workflow stopped working a few days ago
see https://github.com/scikit-learn/scikit-learn/actions/workflows/twitter.yml
COMMENT:
Is removing it as simple as
- deleting twitter.yml
- deleting references to sklearn_commits in the README and other files
Or is there more to it? (Including removing the env s... | 26,419 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26419 | [
"Bug"
] | The twitter workflow stopped working a few days ago
see https://github.com/scikit-learn/scikit-learn/actions/workflows/twitter.yml
COMMENT:
I believe so @joshuahedlund. | 26,419 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.01732... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.0136183... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.0010569... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.015440... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.030... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.019914... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.0192511... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.024784... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.01606... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.01015251... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
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0.043355416506528854,
0.07104106992483139,
0.015091853216290474,
0.0491085909307003,
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0.06166120246052742,
0.032844047993421555,
-0.02059348113834858,
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0.020345568656921387,
-0.03036418929696083,
-0.016359470784664154,
0.015... |
https://github.com/scikit-learn/scikit-learn/issues/26418 | [
"New Feature",
"API",
"Needs Decision",
"RFC"
] | RFC Supporting `scipy.sparse.sparray`
### Context
SciPy is now favoring sparse arrays (i.e. `scipy.sparse.sparray` and its subclasses) over sparse matrices (i.e. `scipy.sparse.spmatrix` and its subclasses) to enlarge the scope of matrices to $n$-dimensional data-structures since SciPy 1.8 (see https://github.com/sc... | 26,418 | [
0.029695795848965645,
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0.05200261250138283,
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0.029894551262259483,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/26415 | [
"Bug",
"module:gaussian_process"
] | Incorrect initialization of `GaussianMixture` from `precisions_init` in the `_initialize` method
### Describe the bug
When passing `precisions_init` to a `GaussianMixture` model, a user expects to resume training the model from the provided precision matrices, which is done by calculating the `precisions_cholesky_`... | 26,415 | [
-0.016153279691934586,
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0.04116165265440941,
0.008448227308690548,
0.07504411786794662,
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0.007668627891689539,
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0.025659294798970222,
0.026271192356944084,
0.021169809624552727,
0.018464883789420128,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26415 | [
"Bug",
"module:gaussian_process"
] | Incorrect initialization of `GaussianMixture` from `precisions_init` in the `_initialize` method
### Describe the bug
When passing `precisions_init` to a `GaussianMixture` model, a user expects to resume training the model from the provided precision matrices, which is done by calculating the `precisions_cholesky_`... | 26,415 | [
-0.016153279691934586,
-0.008820953778922558,
0.04116165265440941,
0.008448227308690548,
0.07504411786794662,
0.016868334263563156,
0.007668627891689539,
-0.04239712655544281,
-0.029160114005208015,
0.025659294798970222,
0.026271192356944084,
0.021169809624552727,
0.018464883789420128,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26415 | [
"Bug",
"module:gaussian_process"
] | Incorrect initialization of `GaussianMixture` from `precisions_init` in the `_initialize` method
### Describe the bug
When passing `precisions_init` to a `GaussianMixture` model, a user expects to resume training the model from the provided precision matrices, which is done by calculating the `precisions_cholesky_`... | 26,415 | [
-0.016153279691934586,
-0.008820953778922558,
0.04116165265440941,
0.008448227308690548,
0.07504411786794662,
0.016868334263563156,
0.007668627891689539,
-0.04239712655544281,
-0.029160114005208015,
0.025659294798970222,
0.026271192356944084,
0.021169809624552727,
0.018464883789420128,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26414 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Calculation of splitting criteria to be executed in parallel threads
### Describe the workflow you want to enable
Currently the computation of splitting criteria for decision tree is single threaded, in theory this can be computed in parallel depending on features available, can the existing package be enhanced to al... | 26,414 | [
-0.029530543833971024,
-0.004137013573199511,
-0.03226205334067345,
0.010271764360368252,
-0.07338862866163254,
-0.021907787770032883,
-0.002054323675110936,
-0.01728023588657379,
-0.04377535730600357,
-0.04046209901571274,
0.027913328260183334,
-0.00888470746576786,
-0.021092304959893227,
... |
https://github.com/scikit-learn/scikit-learn/issues/26414 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Calculation of splitting criteria to be executed in parallel threads
### Describe the workflow you want to enable
Currently the computation of splitting criteria for decision tree is single threaded, in theory this can be computed in parallel depending on features available, can the existing package be enhanced to al... | 26,414 | [
-0.04433399811387062,
-0.02421746775507927,
-0.03951340541243553,
0.011903722770512104,
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0.004900265019387007,
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0.031940024346113205,
-0.0013296612305566669,
0.001176308374851942,
... |
https://github.com/scikit-learn/scikit-learn/issues/26413 | [
"New Feature",
"Needs Triage"
] | Ability to specify depth of a decision tree
### Describe the workflow you want to enable
There is no parameter currently in decision tree package (both regression and classification) to specify the depth of a tree. There are parameters to limit the number of nodes (maximum nodes) and minimum number of leaves for each... | 26,413 | [
-0.022143181413412094,
-0.009001191705465317,
-0.021122826263308525,
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0.007778124418109655,
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0.011963936500251293,
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0.01902749016880989,
0.07851863652467728,
0.05272511765360832,
-0.007975437678396702,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26413 | [
"New Feature",
"Needs Triage"
] | Ability to specify depth of a decision tree
### Describe the workflow you want to enable
There is no parameter currently in decision tree package (both regression and classification) to specify the depth of a tree. There are parameters to limit the number of nodes (maximum nodes) and minimum number of leaves for each... | 26,413 | [
-0.03709981590509415,
0.008687837980687618,
-0.027677489444613457,
-0.02187744714319706,
-0.000985967693850398,
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0.015950262546539307,
-0.03463464230298996,
0.025650590658187866,
0.08345356583595276,
0.07175079733133316,
-0.025986315682530403,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26412 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/5008160381992960)** (May 22, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/4662728275525632) on May 23, 2023 | 26,412 | [
-0.02051118016242981,
-0.008836906403303146,
-0.032526761293411255,
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0.015222901478409767,
0.03036653995513916,
0.013822229579091072,
0.04225129634141922,
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0.019719479605555534,
0.0495641827583313,
0.0058733574114739895,
0.010913370177149773,
0.0279... |
https://github.com/scikit-learn/scikit-learn/issues/26407 | [
"Needs Triage"
] | NameError Traceback (most recent call last) Cell In[23], line 1 ----> 1 sc_X = StandardScaler() 2 X = pd.DataFrame(sc_X.fit_transform(diabetes_df_copy.drop(["Outcome"],axis = 1),), columns=['Pregnancies', 3 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'Dia... | 26,407 | [
0.059726737439632416,
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0.03290703892707825,
-0.04811067879199982,
0.10020816326141357,
0.06208442524075508,
0.09273064136505127,
0.034962669014930725,
0.04307061806321144,
0.0114912623539567,
0.02787316031754017,
0.010546068660914898,
0.03648895025253296,
0.08892650157... |
https://github.com/scikit-learn/scikit-learn/issues/26406 | [
"Needs Triage"
] | ModuleNotFoundError Traceback (most recent call last) Cell In[4], line 1 ----> 1 from mlxtend.plotting import plot_decision_regions 2 import missingno as msno 3 from pandas.plotting import scatter_matrix ModuleNotFoundError: No module named 'mlxtend'
COMMENT:
Spam | 26,406 | [
0.05449085682630539,
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-0.005976488348096609,
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0.05294808745384216,
0.04074077308177948,
0.04430039972066879,
0.026665717363357544,
0.04189697653055191,
-0.016757823526859283,
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0.07904147356748581,
0.010614761151373386,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/26405 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/5939226378764288)** (May 20, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/4817068697059328) on May 21, 2023 | 26,405 | [
-0.022139033302664757,
-0.00395970419049263,
-0.033525604754686356,
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0.014569142833352089,
0.030473360791802406,
0.0132154431194067,
0.04243585467338562,
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0.022727319970726967,
0.05187203735113144,
0.006574961356818676,
0.012019096873700619,
0.0285... |
https://github.com/scikit-learn/scikit-learn/issues/26404 | [
"Needs Reproducible Code"
] | Error for not providing the optional arguments for the feature selection methods
### Describe the bug
Hi,
here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/base.py#L850 and more specifically here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca0... | 26,404 | [
0.0450991615653038,
0.024692129343748093,
0.04157803952693939,
0.005073064006865025,
0.06227332353591919,
0.045323632657527924,
0.11231666803359985,
0.016736922785639763,
0.021953968331217766,
0.005546450614929199,
-0.006451629567891359,
-0.008185582235455513,
0.05996183305978775,
0.029490... |
https://github.com/scikit-learn/scikit-learn/issues/26404 | [
"Needs Reproducible Code"
] | Error for not providing the optional arguments for the feature selection methods
### Describe the bug
Hi,
here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/base.py#L850 and more specifically here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca0... | 26,404 | [
0.0450991615653038,
0.024692129343748093,
0.04157803952693939,
0.005073064006865025,
0.06227332353591919,
0.045323632657527924,
0.11231666803359985,
0.016736922785639763,
0.021953968331217766,
0.005546450614929199,
-0.006451629567891359,
-0.008185582235455513,
0.05996183305978775,
0.029490... |
https://github.com/scikit-learn/scikit-learn/issues/26404 | [
"Needs Reproducible Code"
] | Error for not providing the optional arguments for the feature selection methods
### Describe the bug
Hi,
here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/base.py#L850 and more specifically here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca0... | 26,404 | [
0.0450991615653038,
0.024692129343748093,
0.04157803952693939,
0.005073064006865025,
0.06227332353591919,
0.045323632657527924,
0.11231666803359985,
0.016736922785639763,
0.021953968331217766,
0.005546450614929199,
-0.006451629567891359,
-0.008185582235455513,
0.05996183305978775,
0.029490... |
https://github.com/scikit-learn/scikit-learn/issues/26404 | [
"Needs Reproducible Code"
] | Error for not providing the optional arguments for the feature selection methods
### Describe the bug
Hi,
here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/base.py#L850 and more specifically here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca0... | 26,404 | [
0.0450991615653038,
0.024692129343748093,
0.04157803952693939,
0.005073064006865025,
0.06227332353591919,
0.045323632657527924,
0.11231666803359985,
0.016736922785639763,
0.021953968331217766,
0.005546450614929199,
-0.006451629567891359,
-0.008185582235455513,
0.05996183305978775,
0.029490... |
https://github.com/scikit-learn/scikit-learn/issues/26404 | [
"Needs Reproducible Code"
] | Error for not providing the optional arguments for the feature selection methods
### Describe the bug
Hi,
here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/base.py#L850 and more specifically here https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca0... | 26,404 | [
0.0450991615653038,
0.024692129343748093,
0.04157803952693939,
0.005073064006865025,
0.06227332353591919,
0.045323632657527924,
0.11231666803359985,
0.016736922785639763,
0.021953968331217766,
0.005546450614929199,
-0.006451629567891359,
-0.008185582235455513,
0.05996183305978775,
0.029490... |
https://github.com/scikit-learn/scikit-learn/issues/26402 | [
"New Feature",
"Needs Triage"
] | SGDClassifier should use sparse representation for coefficients
### Describe the workflow you want to enable
`SGDClassifier` should take a `sparse_coef` initial parameter (default false). If set to `True`, the initial `coef` will be sparse. For models like `HashVectorizer` (default 1M features), having a dense co... | 26,402 | [
-0.03079265169799328,
0.012354932725429535,
0.03457852080464363,
0.023124469444155693,
0.07208126783370972,
-0.00992436334490776,
0.03508387506008148,
0.0342416875064373,
0.009860278107225895,
-0.04561914876103401,
0.06757573038339615,
0.020084546878933907,
-0.009103829972445965,
0.0364165... |
https://github.com/scikit-learn/scikit-learn/issues/26402 | [
"New Feature",
"Needs Triage"
] | SGDClassifier should use sparse representation for coefficients
### Describe the workflow you want to enable
`SGDClassifier` should take a `sparse_coef` initial parameter (default false). If set to `True`, the initial `coef` will be sparse. For models like `HashVectorizer` (default 1M features), having a dense co... | 26,402 | [
-0.012517250142991543,
0.029403960332274437,
0.03938861936330795,
0.014307626523077488,
0.07994123548269272,
-0.02223213016986847,
0.013595900498330593,
0.04267759248614311,
0.006206061691045761,
-0.04634249582886696,
0.06262275576591492,
0.034547820687294006,
0.002298922510817647,
0.00951... |
https://github.com/scikit-learn/scikit-learn/issues/26402 | [
"New Feature",
"Needs Triage"
] | SGDClassifier should use sparse representation for coefficients
### Describe the workflow you want to enable
`SGDClassifier` should take a `sparse_coef` initial parameter (default false). If set to `True`, the initial `coef` will be sparse. For models like `HashVectorizer` (default 1M features), having a dense co... | 26,402 | [
-0.03944232687354088,
0.024300282821059227,
0.03213489055633545,
0.01944422535598278,
0.06789842993021011,
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0.032675601541996,
0.03603368252515793,
0.0019120525103062391,
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0.06856398284435272,
0.02238043025135994,
-0.020095020532608032,
0.0504314... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
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0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
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