html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 |
https://github.com/scikit-learn/scikit-learn/issues/27981 | [
"Bug",
"Needs Triage"
] | Nested Cross Validation using cross_validate does not show correct fitted model.
### Describe the bug
Hi all,
I am trying to do nested cross validation using for example `GridSearchCV` or `RandomizedSearchCV` together with `cross_validate`.
When using the cross_validate function together with the parameter sett... | 27,981 |
https://github.com/scikit-learn/scikit-learn/issues/27981 | [
"Bug",
"Needs Triage"
] | Nested Cross Validation using cross_validate does not show correct fitted model.
### Describe the bug
Hi all,
I am trying to do nested cross validation using for example `GridSearchCV` or `RandomizedSearchCV` together with `cross_validate`.
When using the cross_validate function together with the parameter sett... | 27,981 |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 |
https://github.com/scikit-learn/scikit-learn/issues/27968 | [
"Documentation",
"Needs Triage"
] | DOC doc build sphinx version link out-dated again
### Describe the issue linked to the documentation
The link to the sphinx versions for doc build at the end of [*Building the documentation*](https://scikit-learn.org/dev/developers/contributing.html#building-the-documentation) is again out-dated, with sphinx version ... | 27,968 |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 |
https://github.com/scikit-learn/scikit-learn/issues/27959 | [
"New Feature",
"Needs Triage"
] | PR: Polynomial Chaos Expansions with no responses???
### Describe the workflow you want to enable
.
### Describe your proposed solution
.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
Why no one comment this PR https://github.com/scikit-learn/scikit-learn/pull/278... | 27,959 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 |
https://github.com/scikit-learn/scikit-learn/issues/27955 | [
"New Feature",
"Needs Triage"
] | Unable to control warning logs generated by GridSearchCV fit method when setting n_jobs to >1 for parallel processing
### Describe the workflow you want to enable
I am running GridSearchCV with n_jobs set to value which is > 1. The grid search is writing log of convergence and other warnings to the console. I want to... | 27,955 |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 |
https://github.com/scikit-learn/scikit-learn/issues/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 |
https://github.com/scikit-learn/scikit-learn/issues/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 |
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