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/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32753 | [
"Bug",
"Needs Investigation"
] | LocalOutlierFactor with Mahalanobis distance returns different results based on `n_jobs` parameter
### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set... | 32,753 |
https://github.com/scikit-learn/scikit-learn/issues/32752 | [
"Documentation"
] | DOC: Clarify tie-breaking logic for equivalent splits in decision tree documentation
### Describe the issue linked to the documentation
(IA generated, but read and pruned by a human ^^)
The user guide and API reference for decision trees and extra trees do not currently document what happens when there are multiple ... | 32,752 |
https://github.com/scikit-learn/scikit-learn/issues/32752 | [
"Documentation"
] | DOC: Clarify tie-breaking logic for equivalent splits in decision tree documentation
### Describe the issue linked to the documentation
(IA generated, but read and pruned by a human ^^)
The user guide and API reference for decision trees and extra trees do not currently document what happens when there are multiple ... | 32,752 |
https://github.com/scikit-learn/scikit-learn/issues/32748 | [
"Bug"
] | LogisticRegressionCV bug when one fold has not all classes
I may be missing something but it seems like the coefficient for a given class does not stay at zero when a class is missing.
I took the Iris dataset with the well known issue that `y` is ordered with 3 classes so that if you use `cv=KFold(3)` you will get th... | 32,748 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32725 | [
"Bug",
"module:test-suite",
"OS:macOS"
] | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particula... | 32,725 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32723 | [
"Needs Triage"
] | Two possible logic errors potentially caused by typos
While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second asser... | 32,723 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32719 | [
"Bug"
] | Failure to insert instantiated class of estimator in Pipeline produces an unclear error message
### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that ma... | 32,719 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32718 | [
"Bug"
] | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output
### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
*... | 32,718 |
https://github.com/scikit-learn/scikit-learn/issues/32712 | [
"New Feature",
"Needs Info",
"Needs Triage"
] | Add Apriori Algorithm for Association Rule Mining
### Describe the workflow you want to enable
I would like scikit-learn to support association rule mining by implementing the Apriori algorithm. This would allow users to efficiently find frequent itemsets and generate association rules directly within the scikit-lear... | 32,712 |
https://github.com/scikit-learn/scikit-learn/issues/32712 | [
"New Feature",
"Needs Info",
"Needs Triage"
] | Add Apriori Algorithm for Association Rule Mining
### Describe the workflow you want to enable
I would like scikit-learn to support association rule mining by implementing the Apriori algorithm. This would allow users to efficiently find frequent itemsets and generate association rules directly within the scikit-lear... | 32,712 |
https://github.com/scikit-learn/scikit-learn/issues/32712 | [
"New Feature",
"Needs Info",
"Needs Triage"
] | Add Apriori Algorithm for Association Rule Mining
### Describe the workflow you want to enable
I would like scikit-learn to support association rule mining by implementing the Apriori algorithm. This would allow users to efficiently find frequent itemsets and generate association rules directly within the scikit-lear... | 32,712 |
https://github.com/scikit-learn/scikit-learn/issues/32707 | [
"Bug"
] | BUG: tree/forest regressor: impurity decrease calculation is wrong for criterion "friedman_mse"
### Describe the bug
Well, everything is in the title.
I noticed that while writing the issue #32700
I'm opening this issue just for the records, as we plan to remove `"friedman_mse"` criterion anyway.
### Steps/Code t... | 32,707 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32704 | [
"RFC"
] | RFC: Allow training random forests with histograms on binned feature values
This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while sciki... | 32,704 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32700 | [
"API",
"Needs Decision",
"module:tree"
] | Gradient Boosting: Tree splitting criterion`"friedman_mse"` isn't different from normal `"mse"`
### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. Thi... | 32,700 |
https://github.com/scikit-learn/scikit-learn/issues/32697 | [
"Build / CI",
"Array API"
] | CI Collect coverage results on the CUDA CI
We currently don't collect and report coverage information to codecov for the CUDA CI, see https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3503237010
The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploa... | 32,697 |
https://github.com/scikit-learn/scikit-learn/issues/32697 | [
"Build / CI",
"Array API"
] | CI Collect coverage results on the CUDA CI
We currently don't collect and report coverage information to codecov for the CUDA CI, see https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3503237010
The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploa... | 32,697 |
https://github.com/scikit-learn/scikit-learn/issues/32697 | [
"Build / CI",
"Array API"
] | CI Collect coverage results on the CUDA CI
We currently don't collect and report coverage information to codecov for the CUDA CI, see https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3503237010
The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploa... | 32,697 |
https://github.com/scikit-learn/scikit-learn/issues/32697 | [
"Build / CI",
"Array API"
] | CI Collect coverage results on the CUDA CI
We currently don't collect and report coverage information to codecov for the CUDA CI, see https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3503237010
The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploa... | 32,697 |
https://github.com/scikit-learn/scikit-learn/issues/32697 | [
"Build / CI",
"Array API"
] | CI Collect coverage results on the CUDA CI
We currently don't collect and report coverage information to codecov for the CUDA CI, see https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3503237010
The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploa... | 32,697 |
https://github.com/scikit-learn/scikit-learn/issues/32695 | [
"Needs Triage"
] | 💡 Bounty Platform for Scikit-learn
*Content promoting the user's platform removed by maintainer.*
COMMENT:
So you opened a few hundreds of similar issues in plenty of repos https://github.com/search?q=involves%3Adineshroxonn&type=issues&p=1, banning and reporting to GitHub as spammer. | 32,695 |
https://github.com/scikit-learn/scikit-learn/issues/32692 | [
"Bug",
"Needs Triage"
] | Restrictive casting check for imputer
### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I w... | 32,692 |
https://github.com/scikit-learn/scikit-learn/issues/32692 | [
"Bug",
"Needs Triage"
] | Restrictive casting check for imputer
### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I w... | 32,692 |
https://github.com/scikit-learn/scikit-learn/issues/32692 | [
"Bug",
"Needs Triage"
] | Restrictive casting check for imputer
### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I w... | 32,692 |
https://github.com/scikit-learn/scikit-learn/issues/32692 | [
"Bug",
"Needs Triage"
] | Restrictive casting check for imputer
### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I w... | 32,692 |
https://github.com/scikit-learn/scikit-learn/issues/32692 | [
"Bug",
"Needs Triage"
] | Restrictive casting check for imputer
### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I w... | 32,692 |
https://github.com/scikit-learn/scikit-learn/issues/32691 | [
"Bug",
"Needs Info",
"Needs Reproducible Code"
] | Memory Leak in Logistic Regression.
### Describe the bug
Extreme RAM spike and OOM after upgrade (0.24.2 → 1.1.1) for ultra‑wide sparse L1 Logistic Regression (liblinear & saga) – baseline 680 GB → >1.4 TB (ref #28993)
Summary
After upgrading scikit‑learn and Python, fitting an L1 Logistic Regression on a very la... | 32,691 |
https://github.com/scikit-learn/scikit-learn/issues/32691 | [
"Bug",
"Needs Info",
"Needs Reproducible Code"
] | Memory Leak in Logistic Regression.
### Describe the bug
Extreme RAM spike and OOM after upgrade (0.24.2 → 1.1.1) for ultra‑wide sparse L1 Logistic Regression (liblinear & saga) – baseline 680 GB → >1.4 TB (ref #28993)
Summary
After upgrading scikit‑learn and Python, fitting an L1 Logistic Regression on a very la... | 32,691 |
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