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/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
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0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
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0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
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0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28780 | [
"Bug"
] | `FunctionTransformer` need `feature_names_out` even if `func` returns DataFrame
### Describe the bug
Trying to call `transform` for `FunctionTransformer` for which `feature_names_out` is configured raises error that advises to use `set_output(transform='pandas')`. But this doesn't change anything.
### Steps/Code... | 28,780 | [
0.048394810408353806,
0.0033306265249848366,
0.024480856955051422,
-0.03225509822368622,
0.07664891332387924,
0.005254089832305908,
0.08842784911394119,
-0.023371214047074318,
-0.03623095154762268,
0.012384641915559769,
0.012729384005069733,
0.0061108930967748165,
0.08271155506372452,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28778 | [
"New Feature"
] | Implementing variations of the BIRCH clustering algorithm
### Describe the workflow you want to enable
Currently this only the basic implementation of the BIRCH clustering algorithm.
https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html
Just as there is `DBSCAN` and `HDBSCAN`, it would be ... | 28,778 | [
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0.046401385217905045,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/28778 | [
"New Feature"
] | Implementing variations of the BIRCH clustering algorithm
### Describe the workflow you want to enable
Currently this only the basic implementation of the BIRCH clustering algorithm.
https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html
Just as there is `DBSCAN` and `HDBSCAN`, it would be ... | 28,778 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
-0.004797728732228279,
0.009362149983644485,
-0.007263425271958113,
-0.03319868445396423,
0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
-0.004797728732228279,
0.009362149983644485,
-0.007263425271958113,
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0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
-0.004797728732228279,
0.009362149983644485,
-0.007263425271958113,
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0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
-0.007263425271958113,
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0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
-0.004797728732228279,
0.009362149983644485,
-0.007263425271958113,
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0.004054326098412275,
0.01514680590480566,
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-0.011304661631584167,
0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.039123523980379105,
-0.053079262375831604,
0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.039123523980379105,
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0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28772 | [
"Needs Investigation"
] | BUG(?) Missing-values in RandomForest only during inference time shouldn't send missing-values to the child with most samples
Currently, when missing-values occur only in the testing dataset for constructing a RandomForest, there is a policy that the missing values are sent to the child with the most samples. This amo... | 28,772 | [
0.027975331991910934,
0.04876445233821869,
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0.009362149983644485,
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0.004923264961689711,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28769 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=65622&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Apr 08, 2024)
Unable to find junit file. Please see link for details.
COMMENT:
L... | 28,769 | [
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https://github.com/scikit-learn/scikit-learn/issues/28769 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=65622&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Apr 08, 2024)
Unable to find junit file. Please see link for details.
COMMENT:
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https://github.com/scikit-learn/scikit-learn/issues/28769 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=65622&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Apr 08, 2024)
Unable to find junit file. Please see link for details.
COMMENT:
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0.03899312764406204,
0.02324858121573925,
0.04109468311071396,
-0.023954592645168304,
0.043881... |
https://github.com/scikit-learn/scikit-learn/issues/28758 | [
"Needs Triage"
] | Reduce ninja's verbosity from subprocesses
When importing scikit-learn, even if there's nothing to recompile, ninja will output
```
+ /home/jeremie/miniforge/envs/dev/bin/ninja
ninja: no work to do.
```
This is acceptable, but when using an estimator that uses multiprocessing, it will be printed for each sub-proc... | 28,758 | [
0.05184676870703697,
0.06108050048351288,
-0.018137190490961075,
0.003924370743334293,
0.058900415897369385,
0.0038111566100269556,
0.017883311957120895,
-0.014659537002444267,
-0.03128461912274361,
0.01836511678993702,
-0.017616119235754013,
0.07538922876119614,
-0.0276517104357481,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/28758 | [
"Needs Triage"
] | Reduce ninja's verbosity from subprocesses
When importing scikit-learn, even if there's nothing to recompile, ninja will output
```
+ /home/jeremie/miniforge/envs/dev/bin/ninja
ninja: no work to do.
```
This is acceptable, but when using an estimator that uses multiprocessing, it will be printed for each sub-proc... | 28,758 | [
0.03931194171309471,
0.05818542465567589,
-0.015149147249758244,
0.008191811852157116,
0.06323319673538208,
-0.006423620972782373,
0.015798822045326233,
-0.00012123877240810543,
-0.01938685029745102,
0.03236491233110428,
-0.019629446789622307,
0.0898919329047203,
-0.030723759904503822,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28748 | [
"Documentation"
] | Installing from source issue
When following the guidelines for **installing scikit-learn from source** (https://scikit-learn.org/dev/developers/advanced_installation.html#building-from-source), I encountered the a problem at step 5:
```bash
pip install -v --no-use-pep517 --no-build-isolation -e .
```
which lea... | 28,748 | [
0.014588547870516777,
-0.038132376968860626,
-0.01534612663090229,
-0.04573948681354523,
0.03460271283984184,
0.019281387329101562,
0.01178934145718813,
0.012455835938453674,
0.03143744543194771,
0.019895432516932487,
0.01672062836587429,
0.09655411541461945,
0.013549684546887875,
0.045651... |
https://github.com/scikit-learn/scikit-learn/issues/28733 | [
"Documentation",
"Needs Triage"
] | Please provide MAPE formula in documentation
### Describe the issue linked to the documentation
It is a bit unclear right now from the documentation if the formula used for MAPE= |y_true - y_pred|/y_pred *100/N or |y_true - y_pred|/y_pred *1/N, however on checking the code we realize it is the latter.
### Sugge... | 28,733 | [
-0.0460594967007637,
-0.041701897978782654,
0.006100712809711695,
-0.013907856307923794,
-0.00445768004283309,
0.011158316396176815,
0.009747070260345936,
0.015176556073129177,
0.04903755709528923,
-0.018210384994745255,
0.0739637017250061,
-0.004721527919173241,
0.02352430298924446,
0.033... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28732 | [
"Documentation",
"Needs Decision"
] | Docs say parameter sample_weight of LinearRegression.fit must be array but number is also valid
### Describe the issue linked to the documentation
The documentation page for the `fit` method of the `LinearRegression` class mentions that the `sample_weight` parameter must be of type `array_like` or `None` ([docs](http... | 28,732 | [
-0.007426404859870672,
0.007217273116111755,
0.021061817184090614,
-0.0011422560783103108,
0.09026936441659927,
-0.0071370708756148815,
0.05093083158135414,
0.03659295663237572,
0.03478818014264107,
-0.0021749462466686964,
0.08462034165859222,
0.02725978009402752,
0.01863984763622284,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28731 | [
"New Feature"
] | Update index handling in `PandasAdapter`
### Describe the workflow you want to enable
As noted in #27037, handling the index of an input container can be hairy. The solution implemented in #27044 works, but it excludes `pandas.Series` input types. I'd like to modify the logic in the [:method:`PandasAdapter.create_c... | 28,731 | [
-0.017816876992583275,
0.0816730335354805,
0.004753980319947004,
-0.01670217141509056,
0.014056320302188396,
0.03128298744559288,
0.05846928060054779,
-0.0066398209892213345,
-0.007450200617313385,
-0.035258445888757706,
0.011732309125363827,
0.026572613045573235,
-0.019789619371294975,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28731 | [
"New Feature"
] | Update index handling in `PandasAdapter`
### Describe the workflow you want to enable
As noted in #27037, handling the index of an input container can be hairy. The solution implemented in #27044 works, but it excludes `pandas.Series` input types. I'd like to modify the logic in the [:method:`PandasAdapter.create_c... | 28,731 | [
-0.017816876992583275,
0.0816730335354805,
0.004753980319947004,
-0.01670217141509056,
0.014056320302188396,
0.03128298744559288,
0.05846928060054779,
-0.0066398209892213345,
-0.007450200617313385,
-0.035258445888757706,
0.011732309125363827,
0.026572613045573235,
-0.019789619371294975,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28731 | [
"New Feature"
] | Update index handling in `PandasAdapter`
### Describe the workflow you want to enable
As noted in #27037, handling the index of an input container can be hairy. The solution implemented in #27044 works, but it excludes `pandas.Series` input types. I'd like to modify the logic in the [:method:`PandasAdapter.create_c... | 28,731 | [
-0.017816876992583275,
0.0816730335354805,
0.004753980319947004,
-0.01670217141509056,
0.014056320302188396,
0.03128298744559288,
0.05846928060054779,
-0.0066398209892213345,
-0.007450200617313385,
-0.035258445888757706,
0.011732309125363827,
0.026572613045573235,
-0.019789619371294975,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28731 | [
"New Feature"
] | Update index handling in `PandasAdapter`
### Describe the workflow you want to enable
As noted in #27037, handling the index of an input container can be hairy. The solution implemented in #27044 works, but it excludes `pandas.Series` input types. I'd like to modify the logic in the [:method:`PandasAdapter.create_c... | 28,731 | [
-0.017816876992583275,
0.0816730335354805,
0.004753980319947004,
-0.01670217141509056,
0.014056320302188396,
0.03128298744559288,
0.05846928060054779,
-0.0066398209892213345,
-0.007450200617313385,
-0.035258445888757706,
0.011732309125363827,
0.026572613045573235,
-0.019789619371294975,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28731 | [
"New Feature"
] | Update index handling in `PandasAdapter`
### Describe the workflow you want to enable
As noted in #27037, handling the index of an input container can be hairy. The solution implemented in #27044 works, but it excludes `pandas.Series` input types. I'd like to modify the logic in the [:method:`PandasAdapter.create_c... | 28,731 | [
-0.017816876992583275,
0.0816730335354805,
0.004753980319947004,
-0.01670217141509056,
0.014056320302188396,
0.03128298744559288,
0.05846928060054779,
-0.0066398209892213345,
-0.007450200617313385,
-0.035258445888757706,
0.011732309125363827,
0.026572613045573235,
-0.019789619371294975,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28730 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=65430&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Mar 31, 2024)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no... | 28,730 | [
-0.005299579352140427,
-0.010743246413767338,
-0.03557724505662918,
-0.08368608355522156,
0.0274916160851717,
0.0184000413864851,
0.02299426682293415,
0.038706205785274506,
0.039913076907396317,
0.04113464429974556,
0.021797750145196915,
0.03901155665516853,
-0.02214423567056656,
0.0467293... |
https://github.com/scikit-learn/scikit-learn/issues/28726 | [
"New Feature"
] | Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV
### Describe the workflow you want to enable
I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenari... | 28,726 | [
-0.050979133695364,
0.0311101283878088,
-0.01656784489750862,
0.031995657831430435,
0.028851578012108803,
-0.013837486505508423,
0.006116823758929968,
-0.034987617284059525,
-0.007390640676021576,
0.007343894802033901,
0.026632728055119514,
0.07052168250083923,
-0.04623723030090332,
0.1118... |
https://github.com/scikit-learn/scikit-learn/issues/28726 | [
"New Feature"
] | Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV
### Describe the workflow you want to enable
I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenari... | 28,726 | [
-0.042841505259275436,
0.043356869369745255,
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0.04182865098118782,
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0.0011095693334937096,
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0.004403932485729456,
0.010886204428970814,
0.08179201930761337,
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... |
https://github.com/scikit-learn/scikit-learn/issues/28726 | [
"New Feature"
] | Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV
### Describe the workflow you want to enable
I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenari... | 28,726 | [
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0.04011088237166405,
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0.0031299155671149492,
0.024872267618775368,
0.050484515726566315,
-0.044099144637584686... |
https://github.com/scikit-learn/scikit-learn/issues/28726 | [
"New Feature"
] | Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV
### Describe the workflow you want to enable
I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenari... | 28,726 | [
-0.07060360908508301,
0.040279559791088104,
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0.02911517582833767,
0.05105460807681084,
-0.04057323560118675,
... |
https://github.com/scikit-learn/scikit-learn/issues/28725 | [
"Bug",
"module:cross_decomposition"
] | RFE and RFECV allow features_to_select to be larger than available features
### Describe the bug
If the `RFE` or the `RFECV` objects are initialized with a `n_features_to_select` or a `min_features_to_select` (respectively) attribute larger than the number of features present in the `X` variable that is passed to t... | 28,725 | [
0.030818533152341843,
-0.007498068269342184,
0.030189549550414085,
0.025234796106815338,
0.07011058181524277,
-0.007226941641420126,
0.004265567287802696,
0.00445801904425025,
0.01140544842928648,
0.007583873346447945,
0.0558876171708107,
-0.007256620563566685,
0.03308374062180519,
0.01938... |
https://github.com/scikit-learn/scikit-learn/issues/28725 | [
"Bug",
"module:cross_decomposition"
] | RFE and RFECV allow features_to_select to be larger than available features
### Describe the bug
If the `RFE` or the `RFECV` objects are initialized with a `n_features_to_select` or a `min_features_to_select` (respectively) attribute larger than the number of features present in the `X` variable that is passed to t... | 28,725 | [
0.030818533152341843,
-0.007498068269342184,
0.030189549550414085,
0.025234796106815338,
0.07011058181524277,
-0.007226941641420126,
0.004265567287802696,
0.00445801904425025,
0.01140544842928648,
0.007583873346447945,
0.0558876171708107,
-0.007256620563566685,
0.03308374062180519,
0.01938... |
https://github.com/scikit-learn/scikit-learn/issues/28725 | [
"Bug",
"module:cross_decomposition"
] | RFE and RFECV allow features_to_select to be larger than available features
### Describe the bug
If the `RFE` or the `RFECV` objects are initialized with a `n_features_to_select` or a `min_features_to_select` (respectively) attribute larger than the number of features present in the `X` variable that is passed to t... | 28,725 | [
0.030818533152341843,
-0.007498068269342184,
0.030189549550414085,
0.025234796106815338,
0.07011058181524277,
-0.007226941641420126,
0.004265567287802696,
0.00445801904425025,
0.01140544842928648,
0.007583873346447945,
0.0558876171708107,
-0.007256620563566685,
0.03308374062180519,
0.01938... |
https://github.com/scikit-learn/scikit-learn/issues/28725 | [
"Bug",
"module:cross_decomposition"
] | RFE and RFECV allow features_to_select to be larger than available features
### Describe the bug
If the `RFE` or the `RFECV` objects are initialized with a `n_features_to_select` or a `min_features_to_select` (respectively) attribute larger than the number of features present in the `X` variable that is passed to t... | 28,725 | [
0.030818533152341843,
-0.007498068269342184,
0.030189549550414085,
0.025234796106815338,
0.07011058181524277,
-0.007226941641420126,
0.004265567287802696,
0.00445801904425025,
0.01140544842928648,
0.007583873346447945,
0.0558876171708107,
-0.007256620563566685,
0.03308374062180519,
0.01938... |
https://github.com/scikit-learn/scikit-learn/issues/28719 | [
"New Feature",
"Needs Triage"
] | kNN classifier - `predict`/`predict_proba` inefficient?
### Describe the workflow you want to enable
In a lot of cases, when calling `classifier.predict()` we may want probabilities as well via `classifier.predict_proba()`.
To enable that use-case, it seems [the code](https://github.com/scikit-learn/scikit-learn/b... | 28,719 | [
0.02490267902612686,
0.09610413759946823,
0.023557454347610474,
0.0004682192229665816,
-0.033059265464544296,
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0.02555675245821476,
-0.02164568193256855,
0.028543181717395782,
0.0035162644926458597,
0.018157918006181717,
0.007502928376197815,
-0.01994934491813183,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/28719 | [
"New Feature",
"Needs Triage"
] | kNN classifier - `predict`/`predict_proba` inefficient?
### Describe the workflow you want to enable
In a lot of cases, when calling `classifier.predict()` we may want probabilities as well via `classifier.predict_proba()`.
To enable that use-case, it seems [the code](https://github.com/scikit-learn/scikit-learn/b... | 28,719 | [
0.022981371730566025,
0.10362032055854797,
0.019301921129226685,
0.0013167192228138447,
-0.03864331543445587,
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0.029572298750281334,
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0.030980966985225677,
0.004883666057139635,
0.017007216811180115,
0.02003488689661026,
-0.022555969655513763,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28715 | [
"Performance",
"Needs Investigation"
] | When running the GaussianProcessClassifier on M-chips Mac takes extremely long time
### Describe the bug
I have train the Gaussian Process classifier on a 200 points dataset. But it takes 1.5 hour still not get the result. Actually it is not a problem on the intel cpu Mac, but when move the same code on M chip Mac,... | 28,715 | [
-0.010448910295963287,
-0.011670523323118687,
-0.005725490394979715,
0.012008611112833023,
0.026386700570583344,
-0.017418809235095978,
-0.0008998934645205736,
-0.0018202767241746187,
-0.03384469822049141,
0.015002821572124958,
0.04286156967282295,
0.037955597043037415,
0.009035213850438595,... |
https://github.com/scikit-learn/scikit-learn/issues/28714 | [
"Bug"
] | "NameError: name 'functools' is not defined" running `fit` method for a GridSearchCV class
### Describe the bug
I upgraded scikit-learn to latest version yesterday and rerunning an ElasticNet demo I'm getting a puzzling error message. This code works without issue in a Jupyter notebook I had saved from months ago.... | 28,714 | [
0.03115824982523918,
0.00670704385265708,
0.01867886446416378,
-0.010387945920228958,
0.04943209886550903,
0.00559610640630126,
0.052337177097797394,
0.030524635687470436,
0.047921936959028244,
-0.009534691460430622,
0.017537657171487808,
0.07620187848806381,
0.005123391281813383,
0.058309... |
https://github.com/scikit-learn/scikit-learn/issues/28713 | [
"Documentation",
"Needs Triage"
] | Node Splitting Proxy Improvement
### Describe the issue linked to the documentation
While exploring the splitter pyx file in the library's tree folder, I discovered this [current proxy improvement](https://github.com/scikit-learn/scikit-learn/blob/1f46775f7d87538fe00b38f230426c8a7371b11e/sklearn/tree/_splitter.pyx#... | 28,713 | [
-0.012173703871667385,
-0.006075077224522829,
-0.0035571539774537086,
0.007364937569946051,
-0.049763571470975876,
-0.056286465376615524,
0.04684251546859741,
0.0032536140643060207,
-0.013829263858497143,
-0.03634011745452881,
0.03191148489713669,
0.02345922403037548,
0.025319363921880722,
... |
https://github.com/scikit-learn/scikit-learn/issues/28711 | [
"API",
"RFC",
"module:linear_model"
] | RFC New parameters for penalties in LogisticRegression
Based on the comment https://github.com/scikit-learn/scikit-learn/pull/28706#discussion_r1541184840:
Currently, `LogisticRegression` uses `C` as inverse penalization strength, `penalty` to select the type of penalty and `l1_ratio` to control the ration between ... | 28,711 | [
0.01863119751214981,
0.0478457435965538,
0.022000852972269058,
-0.02733645774424076,
0.025615619495511055,
-0.0070514315739274025,
0.018396105617284775,
0.03432008624076843,
-0.02955768071115017,
-0.027845777571201324,
0.10196571797132492,
-0.004255423787981272,
-0.03952677547931671,
0.039... |
https://github.com/scikit-learn/scikit-learn/issues/28711 | [
"API",
"RFC",
"module:linear_model"
] | RFC New parameters for penalties in LogisticRegression
Based on the comment https://github.com/scikit-learn/scikit-learn/pull/28706#discussion_r1541184840:
Currently, `LogisticRegression` uses `C` as inverse penalization strength, `penalty` to select the type of penalty and `l1_ratio` to control the ration between ... | 28,711 | [
0.054454121738672256,
0.07990001887083054,
0.021944763138890266,
-0.016172820702195168,
0.022918032482266426,
0.0018499146681278944,
0.05332120880484581,
0.03234630450606346,
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-0.02467171847820282,
0.07940512895584106,
-0.006889800541102886,
-0.0403578020632267,
0.004... |
https://github.com/scikit-learn/scikit-learn/issues/28711 | [
"API",
"RFC",
"module:linear_model"
] | RFC New parameters for penalties in LogisticRegression
Based on the comment https://github.com/scikit-learn/scikit-learn/pull/28706#discussion_r1541184840:
Currently, `LogisticRegression` uses `C` as inverse penalization strength, `penalty` to select the type of penalty and `l1_ratio` to control the ration between ... | 28,711 | [
0.026266388595104218,
0.07301399856805801,
0.02050512656569481,
-0.017763881012797356,
0.015421668998897076,
-0.010630067437887192,
0.016376307234168053,
0.0330626480281353,
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-0.025313280522823334,
0.09369628876447678,
-0.015909282490611076,
-0.04758509248495102,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/28711 | [
"API",
"RFC",
"module:linear_model"
] | RFC New parameters for penalties in LogisticRegression
Based on the comment https://github.com/scikit-learn/scikit-learn/pull/28706#discussion_r1541184840:
Currently, `LogisticRegression` uses `C` as inverse penalization strength, `penalty` to select the type of penalty and `l1_ratio` to control the ration between ... | 28,711 | [
0.02496526762843132,
0.05534934997558594,
0.024470455944538116,
-0.024884415790438652,
0.029607616364955902,
-0.0048076133243739605,
0.029395002871751785,
0.03559326007962227,
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-0.021952424198389053,
0.09788557142019272,
-0.01815829612314701,
-0.041555896401405334,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28711 | [
"API",
"RFC",
"module:linear_model"
] | RFC New parameters for penalties in LogisticRegression
Based on the comment https://github.com/scikit-learn/scikit-learn/pull/28706#discussion_r1541184840:
Currently, `LogisticRegression` uses `C` as inverse penalization strength, `penalty` to select the type of penalty and `l1_ratio` to control the ration between ... | 28,711 | [
0.03935745358467102,
0.07249096035957336,
0.019219715148210526,
-0.013811386190354824,
0.029533861204981804,
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0.045335154980421066,
0.03605993092060089,
-0.032226450741291046,
-0.02666562609374523,
0.09066596627235413,
-0.00370237254537642,
-0.041449375450611115,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28710 | [
"Bug",
"Build / CI"
] | Misleading OpenMP warning on MacOS when building with Meson
### Describe the bug
Compiling on MacOS with openmp works the old way, see https://scikit-learn.org/dev/developers/advanced_installation.html#macos:
- `brew install libomp`
- ```
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS... | 28,710 | [
-0.0029793037101626396,
-0.019085900858044624,
-0.049083076417446136,
-0.02032460644841194,
0.04963124543428421,
0.03741713613271713,
-0.007839705795049667,
-0.009489190764725208,
0.003766705049201846,
-0.014020749367773533,
0.026776272803544998,
0.06548578292131424,
0.000840873341076076,
... |
https://github.com/scikit-learn/scikit-learn/issues/28707 | [
"Build / CI",
"Performance"
] | Fetchers docstring examples trigger dataset fetch in CI
Docstring examples were recently added to the fetchers. This makes the doc tests executed by pytest actually fetch the datasets.
In the fetcher tests we took some precaution to not fetch the real datasets, see https://github.com/scikit-learn/scikit-learn/blob/1... | 28,707 | [
-0.009599287062883377,
0.05397574603557587,
-0.010337737388908863,
0.03985545039176941,
-0.002756368601694703,
-0.014555374160408974,
0.05882786214351654,
0.07622376084327698,
0.020056961104273796,
0.020295219495892525,
0.011539933271706104,
0.02024105377495289,
-0.01496292557567358,
0.026... |
https://github.com/scikit-learn/scikit-learn/issues/28707 | [
"Build / CI",
"Performance"
] | Fetchers docstring examples trigger dataset fetch in CI
Docstring examples were recently added to the fetchers. This makes the doc tests executed by pytest actually fetch the datasets.
In the fetcher tests we took some precaution to not fetch the real datasets, see https://github.com/scikit-learn/scikit-learn/blob/1... | 28,707 | [
-0.0023211913648992777,
0.05659158155322075,
-0.013123774901032448,
0.053551092743873596,
0.008020389825105667,
-0.0085042305290699,
0.06077815219759941,
0.06355691701173782,
0.029286375269293785,
0.02285804972052574,
0.015674928203225136,
0.013834305107593536,
-0.01597154326736927,
0.0248... |
https://github.com/scikit-learn/scikit-learn/issues/28707 | [
"Build / CI",
"Performance"
] | Fetchers docstring examples trigger dataset fetch in CI
Docstring examples were recently added to the fetchers. This makes the doc tests executed by pytest actually fetch the datasets.
In the fetcher tests we took some precaution to not fetch the real datasets, see https://github.com/scikit-learn/scikit-learn/blob/1... | 28,707 | [
0.004824535921216011,
0.042148616164922714,
-0.011325718834996223,
0.04118631035089493,
-0.000051164715841878206,
-0.002785513876006007,
0.05902079492807388,
0.07083107531070709,
0.016186296939849854,
0.020351288840174675,
0.017719769850373268,
0.009090789593756199,
-0.008953950367867947,
... |
https://github.com/scikit-learn/scikit-learn/issues/28707 | [
"Build / CI",
"Performance"
] | Fetchers docstring examples trigger dataset fetch in CI
Docstring examples were recently added to the fetchers. This makes the doc tests executed by pytest actually fetch the datasets.
In the fetcher tests we took some precaution to not fetch the real datasets, see https://github.com/scikit-learn/scikit-learn/blob/1... | 28,707 | [
-0.00010739004937931895,
0.05704157426953316,
-0.011759807355701923,
0.05365869775414467,
0.013951047323644161,
-0.010037464089691639,
0.05788875371217728,
0.0754605084657669,
0.03267810866236687,
0.010804970748722553,
0.0011391082080081105,
0.014550680294632912,
-0.01798788271844387,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
https://github.com/scikit-learn/scikit-learn/issues/28700 | [
"Bug",
"module:linear_model",
"Numerical Stability"
] | BUG loss of precision in LogisticRegression as of version 1.4
### Describe the bug
Between version 1.3.2 and 1.4.0, LogisticRegression became less accurate.
### Steps/Code to Reproduce
```python
import numpy as np
import pandas as pd
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.linear_... | 28,700 | [
-0.008627655915915966,
0.015925640240311623,
0.015532776713371277,
0.007858101278543472,
0.06803285330533981,
0.012002742849290371,
0.004776539281010628,
0.03580213710665703,
0.008557397872209549,
0.015150022692978382,
0.07260611653327942,
0.003017678391188383,
0.010765519924461842,
0.0496... |
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