html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
|---|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/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 | [
-0.03717243671417236,
-0.031103134155273438,
0.01102216076105833,
0.027762290090322495,
-0.00396965304389596,
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0.032080233097076416,
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0.05176207795739174,
0.023076748475432396,
0.053008392453193665,
0.002538500353693962,
-0.03... |
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 | [
-0.03717243671417236,
-0.031103134155273438,
0.01102216076105833,
0.027762290090322495,
-0.00396965304389596,
-0.01716739870607853,
0.004984260071069002,
0.032080233097076416,
0.014333105646073818,
0.05176207795739174,
0.023076748475432396,
0.053008392453193665,
0.002538500353693962,
-0.03... |
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 | [
-0.03717243671417236,
-0.031103134155273438,
0.01102216076105833,
0.027762290090322495,
-0.00396965304389596,
-0.01716739870607853,
0.004984260071069002,
0.032080233097076416,
0.014333105646073818,
0.05176207795739174,
0.023076748475432396,
0.053008392453193665,
0.002538500353693962,
-0.03... |
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 | [
-0.012395716272294521,
0.0037673916667699814,
-0.0011684814235195518,
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-0.02838420681655407,
-0.057302676141262054,
0.05400032922625542,
0.013923308812081814,
0.05474671348929405,... |
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 | [
-0.011192279867827892,
0.003474232042208314,
-0.0005629187216982245,
-0.026468124240636826,
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-0.05725724622607231,
0.05418594181537628,
0.014957720413804054,
0.055569685995578766,... |
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 | [
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0.055494338274002075,
0.027605673298239708,
0.00355... |
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 | [
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0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
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0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
-0.01331015583127737,
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0.03368736803531647,
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0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
-0.01331015583127737,
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0.03368736803531647,
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0.0349273681640625,
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0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
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0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
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0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
-0.01331015583127737,
0.0024599707685410976,
0.03368736803531647,
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0.03244318068027496,
0.029047973453998566,
-0.007751310244202614,
0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
-0.01331015583127737,
0.0024599707685410976,
0.03368736803531647,
-0.010518857277929783,
0.03244318068027496,
0.029047973453998566,
-0.007751310244202614,
0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
-0.0363643504679203,
0.022374222055077553,
-0.01331015583127737,
0.0024599707685410976,
0.03368736803531647,
-0.010518857277929783,
0.03244318068027496,
0.029047973453998566,
-0.007751310244202614,
0.009183513931930065,
0.054296378046274185,
0.0349273681640625,
-0.026033440604805946,
0.025... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
0.005063432268798351,
0.007482502143830061,
0.005265531130135059,
0.01426774449646473,
0.0561969056725502,
0.0020548428874462843,
0.0566958412528038,
0.02997855469584465,
0.06385983526706696,
-0.024811754003167152,
0.04349717125296593,
0.01603209786117077,
0.03880966082215309,
0.0051261386... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.0022368095815181732,
0.046408191323280334,
-0.0074204737320542336,
-0.033799923956394196,
0.10595205426216125,
0.02151239663362503,
0.08280281722545624,
-0.03543954715132713,
0.01836882159113884,
0.0043625901453197,
0.05741440877318382,
0.05111109837889671,
0.024315161630511284,
0.03524... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
-0.04437357559800148,
-0.04895545169711113,
0.026758592575788498,
-0.06484605371952057,
-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
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-0.04895545169711113,
0.026758592575788498,
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-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
-0.04437357559800148,
-0.04895545169711113,
0.026758592575788498,
-0.06484605371952057,
-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
-0.04437357559800148,
-0.04895545169711113,
0.026758592575788498,
-0.06484605371952057,
-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
-0.04437357559800148,
-0.04895545169711113,
0.026758592575788498,
-0.06484605371952057,
-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.007991911843419075,
0.011789537966251373,
0.009738514199852943,
0.037425316870212555,
0.054011840373277664,
-0.04437357559800148,
-0.04895545169711113,
0.026758592575788498,
-0.06484605371952057,
-0.048909708857536316,
-0.005498999729752541,
0.036206427961587906,
0.02235707826912403,
0.... |
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 | [
-0.014307348988950253,
0.057082630693912506,
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0.024196084588766098,
0.0009458171552978456,
0.04395848140120506,
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0.039494697004556656,
0.02259192429482937,
0.041554927825927734,
0.05526144802570343,
-0.016741547733545303,
0.1... |
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 | [
-0.01135727297514677,
0.06278986483812332,
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0.01942499540746212,
0.04078027606010437,
0.04907839745283127,
-0.02048729546368122,
0.12474... |
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 | [
-0.012530670501291752,
0.06553291529417038,
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0.026084890589118004,
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0.02319776639342308,
0.04137446731328964,
0.046816181391477585,
-0.01741139404475689,
0.11367... |
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 | [
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0.009684426710009575,
-0.... |
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 | [
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0... |
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 | [
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0.019603028893470764,
-0.05300235375761986,
-0.026608729735016823... |
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 | [
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0.015209806151688099,
0.02208893373608589,
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0.03917274996638298,
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0.... |
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 | [
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-0.012765894643962383,... |
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 | [
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0.010139352641999722,
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-0.03449659422039986... |
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 | [
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0.040068622678518295,
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0... |
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 | [
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0.01592770218849182,
0.017873896285891533,
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... |
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 | [
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... |
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 | [
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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 | [
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0.0014110190095379949,
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-0.02596745826303959,... |
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 | [
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0.0069264862686395645,
0.01690218411386013,
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0.018217768520116806,
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... |
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 | [
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0.004999596159905195,
0.010767487809062004,
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0.010389321483671665,
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... |
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 | [
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... |
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 | [
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0... |
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 | [
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... |
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 | [
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... |
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 | [
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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 | [
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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 | [
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... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
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0.0034169438295066357,
0.015231193043291569,
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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 | [
-0.0074445451609790325,
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0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
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-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
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0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
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0.039837755262851715,
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-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
-0.0074445451609790325,
0.03810819983482361,
0.022364558652043343,
0.04984191060066223,
0.050715744495391846,
-0.01084410771727562,
-0.049270663410425186,
0.039837755262851715,
-0.09984306991100311,
-0.05639738216996193,
-0.007815031334757805,
0.0034169438295066357,
0.015231193043291569,
-... |
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 | [
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0.08233289420604706,
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0.08240... |
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 | [
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0.078... |
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 | [
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0.0... |
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 | [
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0.014378492720425129,
-0.010929891839623451,
0.08544... |
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 | [
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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 | [
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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 | [
-0.029032086953520775,
-0.004165132530033588,
0.011735719628632069,
-0.016091439872980118,
0.07145734131336212,
0.011486670933663845,
0.04626438021659851,
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0.008935022167861462,
0.0047622183337807655,
0.03242301940917969,
0.0... |
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 | [
-0.029032086953520775,
-0.004165132530033588,
0.011735719628632069,
-0.016091439872980118,
0.07145734131336212,
0.011486670933663845,
0.04626438021659851,
0.0435432568192482,
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-0.049388568848371506,
0.008935022167861462,
0.0047622183337807655,
0.03242301940917969,
0.0... |
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 | [
-0.029032086953520775,
-0.004165132530033588,
0.011735719628632069,
-0.016091439872980118,
0.07145734131336212,
0.011486670933663845,
0.04626438021659851,
0.0435432568192482,
-0.013943580910563469,
-0.049388568848371506,
0.008935022167861462,
0.0047622183337807655,
0.03242301940917969,
0.0... |
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 | [
-0.029032086953520775,
-0.004165132530033588,
0.011735719628632069,
-0.016091439872980118,
0.07145734131336212,
0.011486670933663845,
0.04626438021659851,
0.0435432568192482,
-0.013943580910563469,
-0.049388568848371506,
0.008935022167861462,
0.0047622183337807655,
0.03242301940917969,
0.0... |
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 | [
-0.029032086953520775,
-0.004165132530033588,
0.011735719628632069,
-0.016091439872980118,
0.07145734131336212,
0.011486670933663845,
0.04626438021659851,
0.0435432568192482,
-0.013943580910563469,
-0.049388568848371506,
0.008935022167861462,
0.0047622183337807655,
0.03242301940917969,
0.0... |
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 | [
0.018171578645706177,
0.08257859945297241,
0.04598110914230347,
-0.00910240039229393,
0.06732043623924255,
-0.008672411553561687,
-0.03802455589175224,
0.05079150199890137,
0.007362852338701487,
-0.006329366471618414,
0.05430910736322403,
0.04463843256235123,
-0.026773326098918915,
0.02974... |
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 | [
0.018171578645706177,
0.08257859945297241,
0.04598110914230347,
-0.00910240039229393,
0.06732043623924255,
-0.008672411553561687,
-0.03802455589175224,
0.05079150199890137,
0.007362852338701487,
-0.006329366471618414,
0.05430910736322403,
0.04463843256235123,
-0.026773326098918915,
0.02974... |
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