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/31020 | [
"Needs Triage"
] | ⚠️ CI failed on Check sdist (last failure: Mar 20, 2025) ⚠️
**CI is still failing on [Check sdist](https://github.com/scikit-learn/scikit-learn/actions/runs/13959330746)** (Mar 20, 2025)
COMMENT:
Need to bump from 3.9 to 3.10 here:
https://github.com/scikit-learn/scikit-learn/blob/5cdbbf15e3fade7cc2462ef66dc4ea0f37f3... | 31,020 | [
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https://github.com/scikit-learn/scikit-learn/issues/31019 | [
"New Feature",
"Needs Triage"
] | Allow column names to pass through when fitting `narwhals` dataframes
### Describe the workflow you want to enable
Currently when fitting with a `narwhals` DataFrame, the feature names do not pass through because it does not implement a `__dataframe__` method.
Example:
```python
import narwhals as nw
import pandas ... | 31,019 | [
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0.04032614827156067,
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0.025540020316839218,
0.000385140476282686,
0.030096227303147316,
0.0512852817773819,
0.031328238... |
https://github.com/scikit-learn/scikit-learn/issues/31019 | [
"New Feature",
"Needs Triage"
] | Allow column names to pass through when fitting `narwhals` dataframes
### Describe the workflow you want to enable
Currently when fitting with a `narwhals` DataFrame, the feature names do not pass through because it does not implement a `__dataframe__` method.
Example:
```python
import narwhals as nw
import pandas ... | 31,019 | [
0.01243000291287899,
0.04032614827156067,
0.04513237252831459,
-0.03321415185928345,
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0.07524224370718002,
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0.025540020316839218,
0.000385140476282686,
0.030096227303147316,
0.0512852817773819,
0.031328238... |
https://github.com/scikit-learn/scikit-learn/issues/31019 | [
"New Feature",
"Needs Triage"
] | Allow column names to pass through when fitting `narwhals` dataframes
### Describe the workflow you want to enable
Currently when fitting with a `narwhals` DataFrame, the feature names do not pass through because it does not implement a `__dataframe__` method.
Example:
```python
import narwhals as nw
import pandas ... | 31,019 | [
0.01243000291287899,
0.04032614827156067,
0.04513237252831459,
-0.03321415185928345,
0.05374332144856453,
0.022411469370126724,
0.07524224370718002,
-0.0168987438082695,
0.0027574014384299517,
0.025540020316839218,
0.000385140476282686,
0.030096227303147316,
0.0512852817773819,
0.031328238... |
https://github.com/scikit-learn/scikit-learn/issues/31019 | [
"New Feature",
"Needs Triage"
] | Allow column names to pass through when fitting `narwhals` dataframes
### Describe the workflow you want to enable
Currently when fitting with a `narwhals` DataFrame, the feature names do not pass through because it does not implement a `__dataframe__` method.
Example:
```python
import narwhals as nw
import pandas ... | 31,019 | [
0.01243000291287899,
0.04032614827156067,
0.04513237252831459,
-0.03321415185928345,
0.05374332144856453,
0.022411469370126724,
0.07524224370718002,
-0.0168987438082695,
0.0027574014384299517,
0.025540020316839218,
0.000385140476282686,
0.030096227303147316,
0.0512852817773819,
0.031328238... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
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0.03705164045095444,
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0.025901902467012405,
0.08893447369337082,
0.005967640317976475,
-0.03701542690396309,
0.02969... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.016483653336763382,
0.07110380381345749,
0.040289975702762604,
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0.08221078664064407,
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0.027823762968182564,
0.08847676217556,
0.004241404589265585,
-0.031066399067640305,
0.0242... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.012339428998529911,
0.06785840541124344,
0.04184965416789055,
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0.02728893607854843,
0.08588074892759323,
0.010515662841498852,
-0.031180106103420258,
0.020671... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.008873607963323593,
0.05443082004785538,
0.0446985624730587,
-0.02308524213731289,
0.09364193677902222,
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0.02575252577662468,
0.017659006640315056,
0.0019005904905498028,
0.03759726509451866,
0.07965865731239319,
-0.01420760340988636,
-0.04024821147322655,
0.008434... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.013332116417586803,
0.05590927600860596,
0.04277832806110382,
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0.07990744709968567,
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0.033003780990839005,
0.020166566595435143,
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0.02399747632443905,
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-0.007964679971337318,
-0.02303367480635643,
0.021... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.00948254857212305,
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0.03817901387810707,
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0.009216... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.012139520607888699,
0.0596713125705719,
0.03494260460138321,
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0.027264436706900597,
0.0918368399143219,
-0.000007149715202103835,
-0.020575648173689842,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/31010 | [
"API",
"RFC"
] | RFC Make all conditional/optional attributes raise a meaningful error when missing
Related: https://github.com/scikit-learn/scikit-learn/issues/10525, https://github.com/scikit-learn/scikit-learn/issues/30999
Right now accessing attributes which are added to the instances when a method is called (like `coef_` in `fit... | 31,010 | [
-0.0024112456012517214,
0.06170748546719551,
0.04315933212637901,
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-0.033463336527347565,
0.01517... |
https://github.com/scikit-learn/scikit-learn/issues/31007 | [
"Documentation"
] | load_iris documentation target_names name wrong type
### Describe the issue linked to the documentation
In the documentation of load_iris (https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) the type of target_names is list but in code it's a numpyarray.
**Version Checked**
Version: 1.... | 31,007 | [
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0.033546555787324905,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31007 | [
"Documentation"
] | load_iris documentation target_names name wrong type
### Describe the issue linked to the documentation
In the documentation of load_iris (https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) the type of target_names is list but in code it's a numpyarray.
**Version Checked**
Version: 1.... | 31,007 | [
0.02738834358751774,
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0.0207715... |
https://github.com/scikit-learn/scikit-learn/issues/30999 | [
"Documentation",
"Needs Triage"
] | Attributes decleared in document and does not exist in ConfusionMatrixDisplay class
### Describe the issue linked to the documentation
In the class `ConfusionMatrixDisplay` in the file `sklearn/metrics/_plot/confusion_matrix.py`
There are extra attributes that does not exist in the class
Attributes:
```
im_: matplot... | 30,999 | [
0.019562523812055588,
-0.008561971597373486,
0.003843744518235326,
0.05111733451485634,
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0.03970474749803543,
0.02610965073108673,
-0.0213... |
https://github.com/scikit-learn/scikit-learn/issues/30999 | [
"Documentation",
"Needs Triage"
] | Attributes decleared in document and does not exist in ConfusionMatrixDisplay class
### Describe the issue linked to the documentation
In the class `ConfusionMatrixDisplay` in the file `sklearn/metrics/_plot/confusion_matrix.py`
There are extra attributes that does not exist in the class
Attributes:
```
im_: matplot... | 30,999 | [
0.020737841725349426,
-0.022695742547512054,
0.010377305559813976,
0.043736957013607025,
0.07135981321334839,
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0.010565345175564289,
0.006392553448677063,
0.04596986249089241,
0.0010881777852773666,
-0.038... |
https://github.com/scikit-learn/scikit-learn/issues/30992 | [
"New Feature"
] | UID-based Stable Train-Test Split
### Describe the workflow you want to enable
During model development, it's common to perform train-test splits multiple times on a dataset. This may occur during development iterations, when the dataset evolves over time, or when working with different data subsets. However, the cur... | 30,992 | [
-0.015811925753951073,
0.11092782765626907,
-0.0177964735776186,
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-0.004883198533207178,
-0.02382611110806465,
0.11378812789916992,
0.008615904487669468,
0.021922027692198753,
-0.02785899117588997,
0.0556771345436573,
0.004473096691071987,
-0.05525635927915573,
0.0484... |
https://github.com/scikit-learn/scikit-learn/issues/30992 | [
"New Feature"
] | UID-based Stable Train-Test Split
### Describe the workflow you want to enable
During model development, it's common to perform train-test splits multiple times on a dataset. This may occur during development iterations, when the dataset evolves over time, or when working with different data subsets. However, the cur... | 30,992 | [
-0.015811925753951073,
0.11092782765626907,
-0.0177964735776186,
-0.015802836045622826,
-0.004883198533207178,
-0.02382611110806465,
0.11378812789916992,
0.008615904487669468,
0.021922027692198753,
-0.02785899117588997,
0.0556771345436573,
0.004473096691071987,
-0.05525635927915573,
0.0484... |
https://github.com/scikit-learn/scikit-learn/issues/30992 | [
"New Feature"
] | UID-based Stable Train-Test Split
### Describe the workflow you want to enable
During model development, it's common to perform train-test splits multiple times on a dataset. This may occur during development iterations, when the dataset evolves over time, or when working with different data subsets. However, the cur... | 30,992 | [
-0.015811925753951073,
0.11092782765626907,
-0.0177964735776186,
-0.015802836045622826,
-0.004883198533207178,
-0.02382611110806465,
0.11378812789916992,
0.008615904487669468,
0.021922027692198753,
-0.02785899117588997,
0.0556771345436573,
0.004473096691071987,
-0.05525635927915573,
0.0484... |
https://github.com/scikit-learn/scikit-learn/issues/30992 | [
"New Feature"
] | UID-based Stable Train-Test Split
### Describe the workflow you want to enable
During model development, it's common to perform train-test splits multiple times on a dataset. This may occur during development iterations, when the dataset evolves over time, or when working with different data subsets. However, the cur... | 30,992 | [
-0.015811925753951073,
0.11092782765626907,
-0.0177964735776186,
-0.015802836045622826,
-0.004883198533207178,
-0.02382611110806465,
0.11378812789916992,
0.008615904487669468,
0.021922027692198753,
-0.02785899117588997,
0.0556771345436573,
0.004473096691071987,
-0.05525635927915573,
0.0484... |
https://github.com/scikit-learn/scikit-learn/issues/30992 | [
"New Feature"
] | UID-based Stable Train-Test Split
### Describe the workflow you want to enable
During model development, it's common to perform train-test splits multiple times on a dataset. This may occur during development iterations, when the dataset evolves over time, or when working with different data subsets. However, the cur... | 30,992 | [
-0.015811925753951073,
0.11092782765626907,
-0.0177964735776186,
-0.015802836045622826,
-0.004883198533207178,
-0.02382611110806465,
0.11378812789916992,
0.008615904487669468,
0.021922027692198753,
-0.02785899117588997,
0.0556771345436573,
0.004473096691071987,
-0.05525635927915573,
0.0484... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
-0.03087112307548523,
-0.048728059977293015,
0.017554737627506256,
-0.04530847445130348,
0.07191264629364014,
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0.0023130085319280624,
0.04391578957438469,
-0.010875498875975609,
-0.020909719169139862,
0.06819938868284225,
-0.004140180069953203,
-0.04093479737639427,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
-0.03087112307548523,
-0.048728059977293015,
0.017554737627506256,
-0.04530847445130348,
0.07191264629364014,
-0.05967186018824577,
0.0023130085319280624,
0.04391578957438469,
-0.010875498875975609,
-0.020909719169139862,
0.06819938868284225,
-0.004140180069953203,
-0.04093479737639427,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
-0.03087112307548523,
-0.048728059977293015,
0.017554737627506256,
-0.04530847445130348,
0.07191264629364014,
-0.05967186018824577,
0.0023130085319280624,
0.04391578957438469,
-0.010875498875975609,
-0.020909719169139862,
0.06819938868284225,
-0.004140180069953203,
-0.04093479737639427,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
-0.03087112307548523,
-0.048728059977293015,
0.017554737627506256,
-0.04530847445130348,
0.07191264629364014,
-0.05967186018824577,
0.0023130085319280624,
0.04391578957438469,
-0.010875498875975609,
-0.020909719169139862,
0.06819938868284225,
-0.004140180069953203,
-0.04093479737639427,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
-0.03087112307548523,
-0.048728059977293015,
0.017554737627506256,
-0.04530847445130348,
0.07191264629364014,
-0.05967186018824577,
0.0023130085319280624,
0.04391578957438469,
-0.010875498875975609,
-0.020909719169139862,
0.06819938868284225,
-0.004140180069953203,
-0.04093479737639427,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30991 | [
"Bug"
] | Halving searches crash when using a PredefinedSplit as cv
### Describe the bug
In some cases, it might be necessary to use a predefined split with an explicit training and testing set instead of cross-validation (for example if the data has a specific distribution of properties that should be the same in a training a... | 30,991 | [
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https://github.com/scikit-learn/scikit-learn/issues/30988 | [
"New Feature",
"Needs Decision - Close",
"Needs Investigation"
] | Make the halving searches scoring parameter also accept single value containers
Currently, the HalvingRandomSearchCV and the HalvingGridSearchCV support only a single scoring metric. Because of that, only a single string or callable is accepted as scoring parameter.
However, this causes it to not accept a single metri... | 30,988 | [
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https://github.com/scikit-learn/scikit-learn/issues/30988 | [
"New Feature",
"Needs Decision - Close",
"Needs Investigation"
] | Make the halving searches scoring parameter also accept single value containers
Currently, the HalvingRandomSearchCV and the HalvingGridSearchCV support only a single scoring metric. Because of that, only a single string or callable is accepted as scoring parameter.
However, this causes it to not accept a single metri... | 30,988 | [
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https://github.com/scikit-learn/scikit-learn/issues/30988 | [
"New Feature",
"Needs Decision - Close",
"Needs Investigation"
] | Make the halving searches scoring parameter also accept single value containers
Currently, the HalvingRandomSearchCV and the HalvingGridSearchCV support only a single scoring metric. Because of that, only a single string or callable is accepted as scoring parameter.
However, this causes it to not accept a single metri... | 30,988 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/30988 | [
"New Feature",
"Needs Decision - Close",
"Needs Investigation"
] | Make the halving searches scoring parameter also accept single value containers
Currently, the HalvingRandomSearchCV and the HalvingGridSearchCV support only a single scoring metric. Because of that, only a single string or callable is accepted as scoring parameter.
However, this causes it to not accept a single metri... | 30,988 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/30988 | [
"New Feature",
"Needs Decision - Close",
"Needs Investigation"
] | Make the halving searches scoring parameter also accept single value containers
Currently, the HalvingRandomSearchCV and the HalvingGridSearchCV support only a single scoring metric. Because of that, only a single string or callable is accepted as scoring parameter.
However, this causes it to not accept a single metri... | 30,988 | [
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30986 | [
"New Feature"
] | enh: support aggregation/bagging functions other than mean
### Describe the workflow you want to enable
Currently KNNRegressor, BaggingRegressor and ForestRegressor only support mean
https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73a86f5f11781a0e21f24c8f47979ec67/sklearn/neighbors/_regression.py#L254-L262
... | 30,986 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30986 | [
"New Feature"
] | enh: support aggregation/bagging functions other than mean
### Describe the workflow you want to enable
Currently KNNRegressor, BaggingRegressor and ForestRegressor only support mean
https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73a86f5f11781a0e21f24c8f47979ec67/sklearn/neighbors/_regression.py#L254-L262
... | 30,986 | [
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0.02853575348854065,
0.008639665320515633,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30986 | [
"New Feature"
] | enh: support aggregation/bagging functions other than mean
### Describe the workflow you want to enable
Currently KNNRegressor, BaggingRegressor and ForestRegressor only support mean
https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73a86f5f11781a0e21f24c8f47979ec67/sklearn/neighbors/_regression.py#L254-L262
... | 30,986 | [
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0.03880311921238899,
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0.02853575348854065,
0.008639665320515633,
-0.034158386290073395,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30986 | [
"New Feature"
] | enh: support aggregation/bagging functions other than mean
### Describe the workflow you want to enable
Currently KNNRegressor, BaggingRegressor and ForestRegressor only support mean
https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73a86f5f11781a0e21f24c8f47979ec67/sklearn/neighbors/_regression.py#L254-L262
... | 30,986 | [
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0.030213775113224983,
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0.02853575348854065,
0.008639665320515633,
-0.034158386290073395,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30984 | [
"Bug",
"Needs Triage"
] | The estimators_ attribute can no longer be accessed for the AdaBoostClassifier class
### Describe the bug
The [documentation of the AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) reads that there is a `estimators_` attribute. However, if I try to access... | 30,984 | [
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0.010738113895058632,
0.010413977317512035,
-0.015308081172406673,
0.10321729630231857,
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0.09066513925790787,
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0.018813451752066612,
0.03170688822865486,
0.014003017917275429,
0.04930906742811203,
0.0030076883267611265,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30984 | [
"Bug",
"Needs Triage"
] | The estimators_ attribute can no longer be accessed for the AdaBoostClassifier class
### Describe the bug
The [documentation of the AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) reads that there is a `estimators_` attribute. However, if I try to access... | 30,984 | [
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0.010738113895058632,
0.010413977317512035,
-0.015308081172406673,
0.10321729630231857,
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0.09066513925790787,
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0.018813451752066612,
0.03170688822865486,
0.014003017917275429,
0.04930906742811203,
0.0030076883267611265,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30983 | [
"Bug",
"Needs Investigation"
] | Error in `ColumnTransformer` when `x` is a pandas dataframe with `int` feature names
### Describe the bug
Hello, I've encountered an unexpected behavior when using `ColumnTransformer` with input `x` being a pandas dataframe with column names having int dtype. I give an example below, and an example use case can be fo... | 30,983 | [
0.01526311319321394,
0.046336084604263306,
0.03760901838541031,
-0.0049638147465884686,
0.07974709570407867,
0.020894009619951248,
0.07924310863018036,
0.02120804600417614,
-0.016765950247645378,
-0.027184052392840385,
0.023585552349686623,
-0.005370251834392548,
0.026924297213554382,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30981 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Mar 12, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/13803264391)** (Mar 12, 2025)
COMMENT:
Seems like an issue with Cython and free-threaded. We are using the Cython nightly wheel so this may have been introduced recent... | 30,981 | [
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0.001938079483807087,
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0.006785504519939423,
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0.04911411926150322,
-0.02313890866935253,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30981 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Mar 12, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/13803264391)** (Mar 12, 2025)
COMMENT:
I can not reproduce this locally for some reason, let's wait and see if it happens again tomorrow ...
It may be a scipy dev whe... | 30,981 | [
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0.01728243939578533,
0.0022830672096461058,
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0.08... |
https://github.com/scikit-learn/scikit-learn/issues/30981 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Mar 12, 2025) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/13803264391)** (Mar 12, 2025)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/13826650719) on Mar 13... | 30,981 | [
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0.0840783... |
https://github.com/scikit-learn/scikit-learn/issues/30973 | [
"Needs Triage"
] | Support for PPC64LE in Scikit-Learn CI
### **Proposed new feature or change:**
Hi Team,
We would like to upstream support for the Power (PPC64LE) architecture in scikit-learn by adding a new CI job in wheels.yml. This will enable continuous integration (CI) support for PPC64LE using a GitHub Actions self-hosted runne... | 30,973 | [
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0.06871852278709412,
0.06076289713382721,
-0.02051892690360546,
0.0920... |
https://github.com/scikit-learn/scikit-learn/issues/30973 | [
"Needs Triage"
] | Support for PPC64LE in Scikit-Learn CI
### **Proposed new feature or change:**
Hi Team,
We would like to upstream support for the Power (PPC64LE) architecture in scikit-learn by adding a new CI job in wheels.yml. This will enable continuous integration (CI) support for PPC64LE using a GitHub Actions self-hosted runne... | 30,973 | [
-0.009909423999488354,
0.03767138347029686,
-0.008626497350633144,
0.028464578092098236,
0.01509648747742176,
0.023930497467517853,
0.04882184416055679,
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0.017608201131224632,
0.06871852278709412,
0.06076289713382721,
-0.02051892690360546,
0.0920... |
https://github.com/scikit-learn/scikit-learn/issues/30973 | [
"Needs Triage"
] | Support for PPC64LE in Scikit-Learn CI
### **Proposed new feature or change:**
Hi Team,
We would like to upstream support for the Power (PPC64LE) architecture in scikit-learn by adding a new CI job in wheels.yml. This will enable continuous integration (CI) support for PPC64LE using a GitHub Actions self-hosted runne... | 30,973 | [
-0.009909423999488354,
0.03767138347029686,
-0.008626497350633144,
0.028464578092098236,
0.01509648747742176,
0.023930497467517853,
0.04882184416055679,
0.020671280100941658,
-0.034680839627981186,
0.017608201131224632,
0.06871852278709412,
0.06076289713382721,
-0.02051892690360546,
0.0920... |
https://github.com/scikit-learn/scikit-learn/issues/30972 | [
"New Feature",
"Needs Triage"
] | auto clusters selection of n_clusters with elbow method
### Describe the workflow you want to enable
In, sklearn.cluster the KMeans algorithm.
the feature suggestion is to add the elbow method cluster selection
with n_cluster="auto"
calculates the best no of cluster based on mse
add trains the models based on the r... | 30,972 | [
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0.006566545460373163,
0.02564287930727005,
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0.02621103823184967,
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0.05938856303691864,
0.0016581520903855562,
0.02324138954281807,
0.10114782303571701,
-0.029904406517744064,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30972 | [
"New Feature",
"Needs Triage"
] | auto clusters selection of n_clusters with elbow method
### Describe the workflow you want to enable
In, sklearn.cluster the KMeans algorithm.
the feature suggestion is to add the elbow method cluster selection
with n_cluster="auto"
calculates the best no of cluster based on mse
add trains the models based on the r... | 30,972 | [
-0.02121458202600479,
-0.03731657937169075,
-0.0332261398434639,
-0.005582970567047596,
0.01209932379424572,
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0.026954248547554016,
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0.06513935327529907,
0.006177172996103764,
0.018313610926270485,
0.10327611118555069,
-0.021748622879385948,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/30970 | [
"New Feature",
"module:model_selection",
"module:multiclass"
] | Allow for multiclass cost matrix in FixedThresholdClassifier and TunedThresholdClassifierCV
### Describe the workflow you want to enable
With #26120, we got `FixedThresholdClassifier` and `TunedThresholdClassifierCV` but only for binary classification. The next logical step would be to extend it to the multiclass set... | 30,970 | [
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https://github.com/scikit-learn/scikit-learn/issues/30970 | [
"New Feature",
"module:model_selection",
"module:multiclass"
] | Allow for multiclass cost matrix in FixedThresholdClassifier and TunedThresholdClassifierCV
### Describe the workflow you want to enable
With #26120, we got `FixedThresholdClassifier` and `TunedThresholdClassifierCV` but only for binary classification. The next logical step would be to extend it to the multiclass set... | 30,970 | [
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0.0082... |
https://github.com/scikit-learn/scikit-learn/issues/30969 | [
"Bug",
"Needs Investigation"
] | KNN tie breakers changing based on the subset of the train
### Describe the bug
According to https://scikit-learn.org/stable/modules/neighbors.html#unsupervised-nearest-neighbors:
**Regarding the Nearest Neighbors algorithms, if two neighbors k and k+1
have identical distances but different labels, the result will... | 30,969 | [
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0.021679481491446495,
-0.017043... |
https://github.com/scikit-learn/scikit-learn/issues/30969 | [
"Bug",
"Needs Investigation"
] | KNN tie breakers changing based on the subset of the train
### Describe the bug
According to https://scikit-learn.org/stable/modules/neighbors.html#unsupervised-nearest-neighbors:
**Regarding the Nearest Neighbors algorithms, if two neighbors k and k+1
have identical distances but different labels, the result will... | 30,969 | [
0.048329517245292664,
0.017544109374284744,
0.013454239815473557,
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0.022095730528235435,
0.021448472514748573,
0.021679481491446495,
-0.017043... |
https://github.com/scikit-learn/scikit-learn/issues/30969 | [
"Bug",
"Needs Investigation"
] | KNN tie breakers changing based on the subset of the train
### Describe the bug
According to https://scikit-learn.org/stable/modules/neighbors.html#unsupervised-nearest-neighbors:
**Regarding the Nearest Neighbors algorithms, if two neighbors k and k+1
have identical distances but different labels, the result will... | 30,969 | [
0.048329517245292664,
0.017544109374284744,
0.013454239815473557,
0.015816878527402878,
0.007071040570735931,
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0.0646144300699234,
0.0298193097114563,
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-0.03537893295288086,
0.022095730528235435,
0.021448472514748573,
0.021679481491446495,
-0.017043... |
https://github.com/scikit-learn/scikit-learn/issues/30964 | [
"Documentation",
"Metadata Routing"
] | DOC better visibility in navigation of metadata routing
The section about [metadata_routing](https://scikit-learn.org/stable/metadata_routing.html) in the [user guide](https://scikit-learn.org/stable/user_guide.html) is hard to find, in particular because there is no entry in the navigation bar, see
<img width="1104"... | 30,964 | [
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https://github.com/scikit-learn/scikit-learn/issues/30964 | [
"Documentation",
"Metadata Routing"
] | DOC better visibility in navigation of metadata routing
The section about [metadata_routing](https://scikit-learn.org/stable/metadata_routing.html) in the [user guide](https://scikit-learn.org/stable/user_guide.html) is hard to find, in particular because there is no entry in the navigation bar, see
<img width="1104"... | 30,964 | [
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https://github.com/scikit-learn/scikit-learn/issues/30961 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Mar 10, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=74605&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Mar 10, 2025)
- test_multicl... | 30,961 | [
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0.0593... |
https://github.com/scikit-learn/scikit-learn/issues/30960 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Mar 10, 2025) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=74605&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Mar 10, 2025)
- test_multiclass_plot_max... | 30,960 | [
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-0.02501598745584488,
0.069... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
0.001503298059105873,
0.12343741953372955,
0.0336163155734539,
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0.04711100831627846,
-0.00535850552842021,
0.040... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
0.010014235973358154,
0.09730726480484009,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
-0.015056106261909008,
0.12861214578151703,
0.022395966574549675,
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0.0036648481618613005,
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0.03387237712740898,
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0.050787169486284256,
-0.010005410760641098,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
0.008887198753654957,
0.10016582161188126,
0.0406307615339756,
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0.07712062448263168,
0.005113527644425631,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
-0.011209746822714806,
0.12805742025375366,
0.0206440519541502,
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0.012182080186903477,
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0.04391861334443092,
0.029885143041610718,
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0.04575421288609505,
-0.018887748941779137,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
-0.008000511676073074,
0.11700303107500076,
0.017873691394925117,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30958 | [
"New Feature"
] | Request: base class with HTML repr but without being an 'Estimator'
### Describe the workflow you want to enable
Creating third-party packages that offer objects that are meant to be passed to estimators, but which aren't estimators themselves.
### Describe your proposed solution
Would be nice if there could be som... | 30,958 | [
-0.005943913944065571,
0.12729515135288239,
0.02712511271238327,
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... |
https://github.com/scikit-learn/scikit-learn/issues/30957 | [
"Documentation",
"help wanted"
] | Docs duplication between attributes and properties
### Describe the issue linked to the documentation
Docs for some classes mention some fitted-model attributes twice: first as 'attribute', then as 'property'.
For example, class SVC here:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
Shows ... | 30,957 | [
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0.042843908071517944,
-0.02825... |
https://github.com/scikit-learn/scikit-learn/issues/30954 | [
"Bug",
"Needs Investigation"
] | QDA is not reproducible
### Describe the bug
We are running QDA with default hyperparameters on the same dataset, on 2 different machines (linux). We find that the results change significantly when ran on a different machine. For more details, please see this Gist:
[https://gist.github.com/tomviering/43519a1f2a0e0ffe... | 30,954 | [
-0.054961711168289185,
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0.019346890971064568,
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0.0005641503375954926,
0.04472264274954796,
0.04923633113503456,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30954 | [
"Bug",
"Needs Investigation"
] | QDA is not reproducible
### Describe the bug
We are running QDA with default hyperparameters on the same dataset, on 2 different machines (linux). We find that the results change significantly when ran on a different machine. For more details, please see this Gist:
[https://gist.github.com/tomviering/43519a1f2a0e0ffe... | 30,954 | [
-0.027046410366892815,
0.03738024830818176,
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0.012409842573106289,
0.058122020214796066,
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0.01627894677221775,
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0.013596612960100174,
0.005158628802746534,
0.05578198283910751,
0.03385864198207855,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/30954 | [
"Bug",
"Needs Investigation"
] | QDA is not reproducible
### Describe the bug
We are running QDA with default hyperparameters on the same dataset, on 2 different machines (linux). We find that the results change significantly when ran on a different machine. For more details, please see this Gist:
[https://gist.github.com/tomviering/43519a1f2a0e0ffe... | 30,954 | [
-0.023507051169872284,
0.04026962071657181,
-0.0015254989266395569,
0.02819780260324478,
0.06631007790565491,
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0.025381606072187424,
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0.005731408949941397,
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0.06025467440485954,
0.033332459628582,
0.0072... |
https://github.com/scikit-learn/scikit-learn/issues/30954 | [
"Bug",
"Needs Investigation"
] | QDA is not reproducible
### Describe the bug
We are running QDA with default hyperparameters on the same dataset, on 2 different machines (linux). We find that the results change significantly when ran on a different machine. For more details, please see this Gist:
[https://gist.github.com/tomviering/43519a1f2a0e0ffe... | 30,954 | [
-0.03767351061105728,
0.05303426831960678,
-0.015002347528934479,
0.0501122809946537,
0.06330681592226028,
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0.006710167042911053,
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0.011331208050251007,
0.008865263313055038,
0.05056194216012955,
0.036462362855672836,
-0.026807... |
https://github.com/scikit-learn/scikit-learn/issues/30952 | [
"Performance",
"module:preprocessing"
] | Improve TargetEncoder predict time for single rows and many categories
As reported [here](https://tiago.rio.br/work/willbank/account/patching-scikit-learn-improve-api-performance/), `TargetEncoder.transform` is optimized for large `n_samples`. But in deployment mode, it might be single rows that matter. Combined with ... | 30,952 | [
0.028528425842523575,
0.11079805344343185,
-0.000004038426141050877,
-0.03721605986356735,
0.043344274163246155,
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0.01659529283642769,
0.050142426043748856,
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-0.0285560954362154,
0.013772652484476566,
0.005916487891227007,
-0.012676860205829144,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30952 | [
"Performance",
"module:preprocessing"
] | Improve TargetEncoder predict time for single rows and many categories
As reported [here](https://tiago.rio.br/work/willbank/account/patching-scikit-learn-improve-api-performance/), `TargetEncoder.transform` is optimized for large `n_samples`. But in deployment mode, it might be single rows that matter. Combined with ... | 30,952 | [
-0.004299850203096867,
0.11959503591060638,
-0.004283951595425606,
-0.02454531565308571,
0.031585078686475754,
-0.019625216722488403,
0.026095911860466003,
0.046743616461753845,
-0.10674764215946198,
-0.023225439712405205,
0.04693739116191864,
0.03304452449083328,
-0.02050604112446308,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30950 | [
"Bug"
] | Potential Problem in the Computation of Adjusted Mutual Info Score
### Describe the bug
It seems to me that for clusters of size 2 and 4, the AMI yields unexpected results of 0 instead of 1, if all items belong to different clusters.
### Steps/Code to Reproduce
Sample Code to Reproduce:
```python
>>> from sklea... | 30,950 | [
0.0030466087628155947,
-0.10292875021696091,
-0.0005693244165740907,
0.047564081847667694,
0.05179494991898537,
0.010227754712104797,
0.03760707750916481,
-0.006432644557207823,
0.05593021586537361,
-0.01715165376663208,
0.0023730015382170677,
0.030260929837822914,
0.022683680057525635,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30950 | [
"Bug"
] | Potential Problem in the Computation of Adjusted Mutual Info Score
### Describe the bug
It seems to me that for clusters of size 2 and 4, the AMI yields unexpected results of 0 instead of 1, if all items belong to different clusters.
### Steps/Code to Reproduce
Sample Code to Reproduce:
```python
>>> from sklea... | 30,950 | [
0.0030466087628155947,
-0.10292875021696091,
-0.0005693244165740907,
0.047564081847667694,
0.05179494991898537,
0.010227754712104797,
0.03760707750916481,
-0.006432644557207823,
0.05593021586537361,
-0.01715165376663208,
0.0023730015382170677,
0.030260929837822914,
0.022683680057525635,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30950 | [
"Bug"
] | Potential Problem in the Computation of Adjusted Mutual Info Score
### Describe the bug
It seems to me that for clusters of size 2 and 4, the AMI yields unexpected results of 0 instead of 1, if all items belong to different clusters.
### Steps/Code to Reproduce
Sample Code to Reproduce:
```python
>>> from sklea... | 30,950 | [
0.0030466087628155947,
-0.10292875021696091,
-0.0005693244165740907,
0.047564081847667694,
0.05179494991898537,
0.010227754712104797,
0.03760707750916481,
-0.006432644557207823,
0.05593021586537361,
-0.01715165376663208,
0.0023730015382170677,
0.030260929837822914,
0.022683680057525635,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30950 | [
"Bug"
] | Potential Problem in the Computation of Adjusted Mutual Info Score
### Describe the bug
It seems to me that for clusters of size 2 and 4, the AMI yields unexpected results of 0 instead of 1, if all items belong to different clusters.
### Steps/Code to Reproduce
Sample Code to Reproduce:
```python
>>> from sklea... | 30,950 | [
0.0030466087628155947,
-0.10292875021696091,
-0.0005693244165740907,
0.047564081847667694,
0.05179494991898537,
0.010227754712104797,
0.03760707750916481,
-0.006432644557207823,
0.05593021586537361,
-0.01715165376663208,
0.0023730015382170677,
0.030260929837822914,
0.022683680057525635,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30941 | [
"Needs Reproducible Code"
] | RANSAC randomly raises UndefinedMetricWarning
RANSAC randomly raises undefined R2 warning for non-default `min_samples` (say 0.1) even if X is sufficiently large.
```
UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. warnings.warn(msg, UndefinedMetricWarning)
```
It seems after applyi... | 30,941 | [
-0.018446093425154686,
-0.015633003786206245,
0.03397255018353462,
0.03106498531997204,
0.075098417699337,
0.023468168452382088,
-0.00208336696960032,
0.06306556612253189,
0.05869231000542641,
0.018941516056656837,
0.05242548882961273,
0.02331339381635189,
-0.01226846780627966,
0.037594076... |
https://github.com/scikit-learn/scikit-learn/issues/30941 | [
"Needs Reproducible Code"
] | RANSAC randomly raises UndefinedMetricWarning
RANSAC randomly raises undefined R2 warning for non-default `min_samples` (say 0.1) even if X is sufficiently large.
```
UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. warnings.warn(msg, UndefinedMetricWarning)
```
It seems after applyi... | 30,941 | [
-0.018123863264918327,
-0.012304616160690784,
0.030862146988511086,
0.029965467751026154,
0.07935433834791183,
0.02412124164402485,
-0.007559818681329489,
0.06185160577297211,
0.05628887191414833,
0.016206426545977592,
0.052616629749536514,
0.021617906168103218,
-0.01397056132555008,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30938 | [
"Bug",
"Regression"
] | Partial dependence broken in sklearn 1.6.1 when grid has only two values
### Describe the bug
When our input feature has two possible values (and that the grid built in that function hence has two values), partial_dependence will raise an error `ValueError: cannot reshape array of size 1 into shape (2)`
What I suspe... | 30,938 | [
0.018025022000074387,
0.034678053110837936,
0.01771906018257141,
0.01905284821987152,
0.05640549212694168,
-0.04480050876736641,
-0.01864485628902912,
0.03289060294628143,
0.05334977060556412,
-0.017218846827745438,
0.05632546916604042,
0.04047971963882446,
-0.009596994146704674,
-0.013803... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30937 | [
"Bug",
"Metadata Routing"
] | Pipeline score asks to explicitly request sample_weight
### Describe the bug
When using `Pipeline` with metadata routing enabled, an error is thrown unless we explicitly request `sample_weight` for the `score` method (see example below). But `Pipeline` is just a router (for both the `fit` and `score` methods) and no... | 30,937 | [
-0.0019198617665097117,
0.02672385238111019,
0.02774244174361229,
-0.036828603595495224,
0.06353112310171127,
-0.01142344530671835,
-0.01013143453747034,
-0.009305758401751518,
0.03421014919877052,
-0.004323054105043411,
0.06333307921886444,
0.04073738306760788,
-0.015005702152848244,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30936 | [
"Bug"
] | SelectFromModel does not work when ElasticNetCV has multiple l1 ratios
### Describe the bug
Using `SelectFromModel` with the automatic `ElasticNetCV` does not work if the `l1_ratio` is estimated from the data, i.e., if the user provides a list of floats.
### Steps/Code to Reproduce
```py
from sklearn.datasets impo... | 30,936 | [
0.03270287439227104,
0.00643250672146678,
0.0104349534958601,
0.002942444756627083,
0.06561644375324249,
-0.010943413712084293,
0.05947407707571983,
0.06921342760324478,
0.035141345113515854,
-0.017376122996211052,
0.027372047305107117,
0.030588874593377113,
-0.01847311481833458,
0.0126163... |
https://github.com/scikit-learn/scikit-learn/issues/30936 | [
"Bug"
] | SelectFromModel does not work when ElasticNetCV has multiple l1 ratios
### Describe the bug
Using `SelectFromModel` with the automatic `ElasticNetCV` does not work if the `l1_ratio` is estimated from the data, i.e., if the user provides a list of floats.
### Steps/Code to Reproduce
```py
from sklearn.datasets impo... | 30,936 | [
0.03270287439227104,
0.00643250672146678,
0.0104349534958601,
0.002942444756627083,
0.06561644375324249,
-0.010943413712084293,
0.05947407707571983,
0.06921342760324478,
0.035141345113515854,
-0.017376122996211052,
0.027372047305107117,
0.030588874593377113,
-0.01847311481833458,
0.0126163... |
https://github.com/scikit-learn/scikit-learn/issues/30935 | [
"Bug"
] | The default token pattern in CountVectorizer breaks Indic sentences into non-sensical tokens
### Describe the bug
The default `token_pattern` in `CountVectorizer` is `r"(?u)\b\w\w+\b"` which tokenizes Indic texts in a wrong way - breaks whitespace tokenized words into multiple chunks and even omits several valid char... | 30,935 | [
0.05598331615328789,
0.018539490178227425,
0.015863319858908653,
0.03269343450665474,
0.05982275679707527,
0.0020870945882052183,
-0.006617846433073282,
-0.02814786322414875,
-0.06362085789442062,
0.0017951849149540067,
0.03316454216837883,
-0.05242127552628517,
0.03224828466773033,
0.0612... |
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