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/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 | [
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0.008452997542917728,
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-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 | [
0.025173455476760864,
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0.008399038575589657,
0.051415301859378815,
0.008452997542917728,
-0.013153290376067162,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25206 | [
"Documentation",
"module:ensemble"
] | Unclear train_score_ attribute description for GradientBoostingClassifier
### Describe the issue linked to the documentation
I recently trained a binary gradient boosting model using the GradientBoostingClassifier. I wanted to plot the intermediate losses at each iteration on the test set while evaluating my results ... | 25,206 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/25206 | [
"Documentation",
"module:ensemble"
] | Unclear train_score_ attribute description for GradientBoostingClassifier
### Describe the issue linked to the documentation
I recently trained a binary gradient boosting model using the GradientBoostingClassifier. I wanted to plot the intermediate losses at each iteration on the test set while evaluating my results ... | 25,206 | [
-0.05146009474992752,
-0.05863471329212189,
0.012166067957878113,
0.00660144817084074,
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0.033275000751018524,
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0.015721285715699196,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25206 | [
"Documentation",
"module:ensemble"
] | Unclear train_score_ attribute description for GradientBoostingClassifier
### Describe the issue linked to the documentation
I recently trained a binary gradient boosting model using the GradientBoostingClassifier. I wanted to plot the intermediate losses at each iteration on the test set while evaluating my results ... | 25,206 | [
-0.05146009474992752,
-0.05863471329212189,
0.012166067957878113,
0.00660144817084074,
0.020446226000785828,
-0.012877799570560455,
0.022270573303103447,
0.045242276042699814,
-0.04803817719221115,
-0.006010409444570541,
0.033275000751018524,
-0.057679690420627594,
0.015721285715699196,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25202 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=51438&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jan 27, 2023)
- test_estimators[FeatureAgglomeration()-check_paramete... | 25,202 | [
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0.015426122583448887,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/25202 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=51438&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jan 27, 2023)
- test_estimators[FeatureAgglomeration()-check_paramete... | 25,202 | [
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0.016234254464507103,
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0.005689210258424282,
0.079... |
https://github.com/scikit-learn/scikit-learn/issues/25198 | [
"Bug",
"module:linear_model"
] | Sparse data representations results in worse models than dense data for some classifiers
### Describe the bug
Using scipy sparse matrices with sklearn LogisticRegression greatly improves speed and therefore is desirable in many scenarios.
However, it appears that sparse versus dense data representations yield di... | 25,198 | [
-0.003993635065853596,
0.023200012743473053,
0.06297582387924194,
0.0230405256152153,
0.06007082760334015,
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0.0429004468023777,
0.05303560942411423,
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-0.003977641928941011,
0.027386168017983437,
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0.032061997801065445,
0.0152815... |
https://github.com/scikit-learn/scikit-learn/issues/25198 | [
"Bug",
"module:linear_model"
] | Sparse data representations results in worse models than dense data for some classifiers
### Describe the bug
Using scipy sparse matrices with sklearn LogisticRegression greatly improves speed and therefore is desirable in many scenarios.
However, it appears that sparse versus dense data representations yield di... | 25,198 | [
-0.003993635065853596,
0.023200012743473053,
0.06297582387924194,
0.0230405256152153,
0.06007082760334015,
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0.0429004468023777,
0.05303560942411423,
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-0.003977641928941011,
0.027386168017983437,
-0.0120844142511487,
0.032061997801065445,
0.0152815... |
https://github.com/scikit-learn/scikit-learn/issues/25198 | [
"Bug",
"module:linear_model"
] | Sparse data representations results in worse models than dense data for some classifiers
### Describe the bug
Using scipy sparse matrices with sklearn LogisticRegression greatly improves speed and therefore is desirable in many scenarios.
However, it appears that sparse versus dense data representations yield di... | 25,198 | [
-0.003993635065853596,
0.023200012743473053,
0.06297582387924194,
0.0230405256152153,
0.06007082760334015,
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0.0429004468023777,
0.05303560942411423,
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-0.003977641928941011,
0.027386168017983437,
-0.0120844142511487,
0.032061997801065445,
0.0152815... |
https://github.com/scikit-learn/scikit-learn/issues/25198 | [
"Bug",
"module:linear_model"
] | Sparse data representations results in worse models than dense data for some classifiers
### Describe the bug
Using scipy sparse matrices with sklearn LogisticRegression greatly improves speed and therefore is desirable in many scenarios.
However, it appears that sparse versus dense data representations yield di... | 25,198 | [
-0.003993635065853596,
0.023200012743473053,
0.06297582387924194,
0.0230405256152153,
0.06007082760334015,
-0.020091097801923752,
0.0429004468023777,
0.05303560942411423,
-0.02407154254615307,
-0.003977641928941011,
0.027386168017983437,
-0.0120844142511487,
0.032061997801065445,
0.0152815... |
https://github.com/scikit-learn/scikit-learn/issues/25198 | [
"Bug",
"module:linear_model"
] | Sparse data representations results in worse models than dense data for some classifiers
### Describe the bug
Using scipy sparse matrices with sklearn LogisticRegression greatly improves speed and therefore is desirable in many scenarios.
However, it appears that sparse versus dense data representations yield di... | 25,198 | [
-0.003993635065853596,
0.023200012743473053,
0.06297582387924194,
0.0230405256152153,
0.06007082760334015,
-0.020091097801923752,
0.0429004468023777,
0.05303560942411423,
-0.02407154254615307,
-0.003977641928941011,
0.027386168017983437,
-0.0120844142511487,
0.032061997801065445,
0.0152815... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.022548099979758263,
0.04151884466409683,
-0.014028995297849178,
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0.10869938135147095,
0.03692640736699104,
0.0023656957782804966,
-0.05201341584324837,
-0.013083847239613533,
0.017624322324991226,
-0.029117288067936897,... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.024574998766183853,
0.036025963723659515,
-0.011205136775970459,
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-0.012783356010913849,
0.022031864151358604,
-0.02657985873520374... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.030413122847676277,
0.03341027721762657,
-0.007268059998750687,
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-0.000152490014443174,
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0.11559578776359558,
0.03229090943932533,
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0.019716927781701088,
-0.011285536922514439,
... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.019580963999032974,
0.043923649936914444,
-0.011800428852438927,
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-0.010457497090101242,
0.023348301649093628,
-0.02564375475049019,
... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.023627687245607376,
0.042695458978414536,
-0.015620402060449123,
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0.0008529109763912857,
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-0.013207796029746532,
0.018846305087208748,
-0.02944585308432579... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.019841304048895836,
0.04223233833909035,
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0.019220927730202675,
-0.02826484851539135,
... |
https://github.com/scikit-learn/scikit-learn/issues/25193 | [
"New Feature",
"module:model_selection"
] | StratifiedKFold and StratifiedGroupKFold for multilabel classification
### Describe the workflow you want to enable
Currently, these two functionalities only support binary/multiclass classification. I am looking for similar functionalities (maybe not even k fold, a train_test_split also suffices) in multilabel class... | 25,193 | [
-0.02199328877031803,
0.04131121188402176,
-0.011975553818047047,
-0.005230364855378866,
0.0016250074841082096,
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0.10523340851068497,
0.035596054047346115,
0.000497660890687257,
-0.052397746592760086,
-0.010711697861552238,
0.019696149975061417,
-0.025653820484876633,
... |
https://github.com/scikit-learn/scikit-learn/issues/25187 | [
"New Feature",
"module:model_selection"
] | Early Stopping for GridSearchCV, RandomizedSearchCV
### Describe the workflow you want to enable
- I have a custom model implementing the BaseEstimator, for which I am using scikit-learn's hyperparameter searches.
- I am running an exhaustive grid search, all possible parameters for my model.
- If one parameter set... | 25,187 | [
-0.050107236951589584,
0.0734279677271843,
0.021107831969857216,
-0.032116834074258804,
0.025546232238411903,
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-0.006637537851929665,
0.004790743812918663,
0.03167375922203064,
0.01441270299255848,
0.036066602915525436,
0.040601033717393875,
-0.05888403579592705,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/25187 | [
"New Feature",
"module:model_selection"
] | Early Stopping for GridSearchCV, RandomizedSearchCV
### Describe the workflow you want to enable
- I have a custom model implementing the BaseEstimator, for which I am using scikit-learn's hyperparameter searches.
- I am running an exhaustive grid search, all possible parameters for my model.
- If one parameter set... | 25,187 | [
-0.04290015250444412,
0.07055320590734482,
0.02451573871076107,
-0.0415748693048954,
0.02953019179403782,
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-0.010747005231678486,
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0.008536694571375847,
0.01731426827609539,
0.03936378285288811,
0.0625692680478096,
-0.05613767355680466,
0.02204781... |
https://github.com/scikit-learn/scikit-learn/issues/25187 | [
"New Feature",
"module:model_selection"
] | Early Stopping for GridSearchCV, RandomizedSearchCV
### Describe the workflow you want to enable
- I have a custom model implementing the BaseEstimator, for which I am using scikit-learn's hyperparameter searches.
- I am running an exhaustive grid search, all possible parameters for my model.
- If one parameter set... | 25,187 | [
-0.0382751002907753,
0.07994237542152405,
0.02706175111234188,
-0.03576606139540672,
0.020462894812226295,
-0.02085663564503193,
-0.0019639190286397934,
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0.03000793606042862,
0.01685037463903427,
0.048119496554136276,
0.05012517794966698,
-0.06928066909313202,
0.01922... |
https://github.com/scikit-learn/scikit-learn/issues/25187 | [
"New Feature",
"module:model_selection"
] | Early Stopping for GridSearchCV, RandomizedSearchCV
### Describe the workflow you want to enable
- I have a custom model implementing the BaseEstimator, for which I am using scikit-learn's hyperparameter searches.
- I am running an exhaustive grid search, all possible parameters for my model.
- If one parameter set... | 25,187 | [
-0.03845931962132454,
0.07281296700239182,
0.02623078227043152,
-0.03986483812332153,
0.022084517404437065,
-0.017323780804872513,
-0.0027437966782599688,
-0.0031669524032622576,
0.027083968743681908,
0.014669031836092472,
0.04251996800303459,
0.04859409108757973,
-0.06243593990802765,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25187 | [
"New Feature",
"module:model_selection"
] | Early Stopping for GridSearchCV, RandomizedSearchCV
### Describe the workflow you want to enable
- I have a custom model implementing the BaseEstimator, for which I am using scikit-learn's hyperparameter searches.
- I am running an exhaustive grid search, all possible parameters for my model.
- If one parameter set... | 25,187 | [
-0.04292921721935272,
0.075091153383255,
0.02600128948688507,
-0.036999594420194626,
0.021008746698498726,
-0.011868918314576149,
-0.00740513252094388,
-0.005849137436598539,
0.022872108966112137,
0.015873238444328308,
0.04756989702582359,
0.04310452193021774,
-0.06119884178042412,
0.00467... |
https://github.com/scikit-learn/scikit-learn/issues/25179 | [
"New Feature",
"module:metrics"
] | Improve error handling in _mutual_info.py
### Describe the workflow you want to enable
Hi all,
I am currently trying to run the following code:
```python
from sklearn.feature_selection import mutual_info_classif
a = [[1,0,1],[0,1,1]]
b = [0,1]
mutual_info_classif(a,b)
```
Which fails with:
```
Tra... | 25,179 | [
-0.0004829554818570614,
-0.0013111146399751306,
0.01449994370341301,
-0.007030843757092953,
0.06822697073221207,
0.024526745080947876,
0.03858464956283569,
0.024737169966101646,
0.035831741988658905,
-0.04860895872116089,
0.003919385839253664,
0.05327128991484642,
-0.0040909950621426105,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25178 | [
"Bug",
"Needs Triage"
] | RandomForestClassifier outputs float instead of bool class-labels when fit on a pd.Series since 1.2.0
### Describe the bug
```
import numpy as np
import pandas as pd
import sklearn
from sklearn.ensemble import RandomForestClassifier
# 1.23.3 1.5.0 1.2.0
print(np.__version__, pd.__version__, sklearn.__version_... | 25,178 | [
-0.011799106374382973,
-0.01606430485844612,
0.036855634301900864,
-0.020840633660554886,
0.06023738905787468,
-0.011846999637782574,
0.04492945969104767,
0.03819353133440018,
0.03257714584469795,
0.0023745503276586533,
0.04820738732814789,
0.00932046864181757,
0.028607839718461037,
0.0086... |
https://github.com/scikit-learn/scikit-learn/issues/25178 | [
"Bug",
"Needs Triage"
] | RandomForestClassifier outputs float instead of bool class-labels when fit on a pd.Series since 1.2.0
### Describe the bug
```
import numpy as np
import pandas as pd
import sklearn
from sklearn.ensemble import RandomForestClassifier
# 1.23.3 1.5.0 1.2.0
print(np.__version__, pd.__version__, sklearn.__version_... | 25,178 | [
-0.011799106374382973,
-0.01606430485844612,
0.036855634301900864,
-0.020840633660554886,
0.06023738905787468,
-0.011846999637782574,
0.04492945969104767,
0.03819353133440018,
0.03257714584469795,
0.0023745503276586533,
0.04820738732814789,
0.00932046864181757,
0.028607839718461037,
0.0086... |
https://github.com/scikit-learn/scikit-learn/issues/25171 | [
"Bug"
] | OneHotEncoder cuts predefined classes
### Describe the bug
When having predefined categories for the OneHotEncoder the categories get cut off. This lead to an error when trying to transform samples with the categories present....
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import OneHotE... | 25,171 | [
-0.0005478099919855595,
0.028410036116838455,
0.011470766738057137,
0.00541128683835268,
0.09987736493349075,
0.01880790665745735,
0.021508030593395233,
0.031297069042921066,
-0.03478458896279335,
-0.052093829959630966,
0.044901665300130844,
0.011150558479130268,
0.03712455555796623,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25171 | [
"Bug"
] | OneHotEncoder cuts predefined classes
### Describe the bug
When having predefined categories for the OneHotEncoder the categories get cut off. This lead to an error when trying to transform samples with the categories present....
### Steps/Code to Reproduce
```python
from sklearn.preprocessing import OneHotE... | 25,171 | [
-0.0005478099919855595,
0.028410036116838455,
0.011470766738057137,
0.00541128683835268,
0.09987736493349075,
0.01880790665745735,
0.021508030593395233,
0.031297069042921066,
-0.03478458896279335,
-0.052093829959630966,
0.044901665300130844,
0.011150558479130268,
0.03712455555796623,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25169 | [
"Bug",
"Needs Triage"
] | DOC: type annotation for model returned from fit methods is "object"
### Describe the bug
I noticed that the type annotation for model returned from fit methods is "object". This makes IDE like pycharm unable to perform type hints:
![image](https://user-images.githubusercontent.com/48577571/206979644-9185bff3-e22f... | 25,169 | [
0.014003925025463104,
-0.014384916983544827,
0.012782885693013668,
0.06573570519685745,
0.1046120896935463,
-0.004650912247598171,
0.03502046689391136,
0.0327417328953743,
0.025637974962592125,
-0.04551582783460617,
-0.0008163291495293379,
0.0698319673538208,
0.024872854351997375,
0.060308... |
https://github.com/scikit-learn/scikit-learn/issues/25169 | [
"Bug",
"Needs Triage"
] | DOC: type annotation for model returned from fit methods is "object"
### Describe the bug
I noticed that the type annotation for model returned from fit methods is "object". This makes IDE like pycharm unable to perform type hints:
![image](https://user-images.githubusercontent.com/48577571/206979644-9185bff3-e22f... | 25,169 | [
0.014003925025463104,
-0.014384916983544827,
0.012782885693013668,
0.06573570519685745,
0.1046120896935463,
-0.004650912247598171,
0.03502046689391136,
0.0327417328953743,
0.025637974962592125,
-0.04551582783460617,
-0.0008163291495293379,
0.0698319673538208,
0.024872854351997375,
0.060308... |
https://github.com/scikit-learn/scikit-learn/issues/25169 | [
"Bug",
"Needs Triage"
] | DOC: type annotation for model returned from fit methods is "object"
### Describe the bug
I noticed that the type annotation for model returned from fit methods is "object". This makes IDE like pycharm unable to perform type hints:
![image](https://user-images.githubusercontent.com/48577571/206979644-9185bff3-e22f... | 25,169 | [
0.014003925025463104,
-0.014384916983544827,
0.012782885693013668,
0.06573570519685745,
0.1046120896935463,
-0.004650912247598171,
0.03502046689391136,
0.0327417328953743,
0.025637974962592125,
-0.04551582783460617,
-0.0008163291495293379,
0.0698319673538208,
0.024872854351997375,
0.060308... |
https://github.com/scikit-learn/scikit-learn/issues/25169 | [
"Bug",
"Needs Triage"
] | DOC: type annotation for model returned from fit methods is "object"
### Describe the bug
I noticed that the type annotation for model returned from fit methods is "object". This makes IDE like pycharm unable to perform type hints:
![image](https://user-images.githubusercontent.com/48577571/206979644-9185bff3-e22f... | 25,169 | [
0.014003925025463104,
-0.014384916983544827,
0.012782885693013668,
0.06573570519685745,
0.1046120896935463,
-0.004650912247598171,
0.03502046689391136,
0.0327417328953743,
0.025637974962592125,
-0.04551582783460617,
-0.0008163291495293379,
0.0698319673538208,
0.024872854351997375,
0.060308... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25165 | [
"Bug",
"module:decomposition"
] | ValueError: buffer source array is read-only In DictionaryLearning using coordinate decent, numworkers = 15
### Describe the bug
I get "source array is read only" error in Dictionary Learning when I try to fit it to the Cifar10 Dataset.
### Steps/Code to Reproduce
```python
from sklearn.decomposition import... | 25,165 | [
-0.028808629140257835,
-0.01791415549814701,
0.0006041532615199685,
0.03830572962760925,
0.039146993309259415,
-0.007143049035221338,
0.034000594168901443,
0.0242695864289999,
-0.007383518852293491,
0.034757308661937714,
-0.02227586880326271,
0.057966478168964386,
-0.006853146944195032,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25164 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=49978&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 15, 2022)
- test_kernel_pca
- test_kernel_pca_consistent_transfor... | 25,164 | [
-0.01233625691384077,
0.03183869644999504,
-0.046048302203416824,
-0.017293868586421013,
0.041820235550403595,
-0.001437105587683618,
0.0186306219547987,
0.03769540786743164,
-0.01709725894033909,
0.02284202165901661,
0.05761507526040077,
0.030922235921025276,
0.00012544433411676437,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/25164 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=49978&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 15, 2022)
- test_kernel_pca
- test_kernel_pca_consistent_transfor... | 25,164 | [
-0.01233625691384077,
0.03183869644999504,
-0.046048302203416824,
-0.017293868586421013,
0.041820235550403595,
-0.001437105587683618,
0.0186306219547987,
0.03769540786743164,
-0.01709725894033909,
0.02284202165901661,
0.05761507526040077,
0.030922235921025276,
0.00012544433411676437,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/25164 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=49978&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 15, 2022)
- test_kernel_pca
- test_kernel_pca_consistent_transfor... | 25,164 | [
-0.01233625691384077,
0.03183869644999504,
-0.046048302203416824,
-0.017293868586421013,
0.041820235550403595,
-0.001437105587683618,
0.0186306219547987,
0.03769540786743164,
-0.01709725894033909,
0.02284202165901661,
0.05761507526040077,
0.030922235921025276,
0.00012544433411676437,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/25161 | [
"Bug",
"good first issue",
"help wanted"
] | Possible unintended behavior in sklearn.feature_extraction.image.extract_patches_2d
### Describe the bug
When running extract_patches_2d with `max_patches = 0`, it appears that the max number of patches is returned (same as calling with `max_patches = None`). I believe this is unintended since in the docs it's stat... | 25,161 | [
-0.00883757509291172,
-0.04297048971056938,
0.012143908999860287,
0.04736290127038956,
0.03849555179476738,
-0.016131149604916573,
-0.0031964918598532677,
0.011558988131582737,
0.010766085237264633,
-0.024348851293325424,
0.0806025043129921,
0.011131137609481812,
0.003808689070865512,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25161 | [
"Bug",
"good first issue",
"help wanted"
] | Possible unintended behavior in sklearn.feature_extraction.image.extract_patches_2d
### Describe the bug
When running extract_patches_2d with `max_patches = 0`, it appears that the max number of patches is returned (same as calling with `max_patches = None`). I believe this is unintended since in the docs it's stat... | 25,161 | [
-0.00883757509291172,
-0.04297048971056938,
0.012143908999860287,
0.04736290127038956,
0.03849555179476738,
-0.016131149604916573,
-0.0031964918598532677,
0.011558988131582737,
0.010766085237264633,
-0.024348851293325424,
0.0806025043129921,
0.011131137609481812,
0.003808689070865512,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25161 | [
"Bug",
"good first issue",
"help wanted"
] | Possible unintended behavior in sklearn.feature_extraction.image.extract_patches_2d
### Describe the bug
When running extract_patches_2d with `max_patches = 0`, it appears that the max number of patches is returned (same as calling with `max_patches = None`). I believe this is unintended since in the docs it's stat... | 25,161 | [
-0.00883757509291172,
-0.04297048971056938,
0.012143908999860287,
0.04736290127038956,
0.03849555179476738,
-0.016131149604916573,
-0.0031964918598532677,
0.011558988131582737,
0.010766085237264633,
-0.024348851293325424,
0.0806025043129921,
0.011131137609481812,
0.003808689070865512,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25159 | [
"Bug",
"Needs Triage"
] | scikit-learn 1.2.0 compiled against numpy ABI version 0x10 but this version of numpy is 0xf
### Describe the bug
Using numpy 1.22.0 from my OS (Fedora 37, x86_64), I get this error on `import sklearn`:
```
RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf
```
Does scikit-lear... | 25,159 | [
0.007917718961834908,
0.04062815383076668,
0.016242584213614464,
-0.05248909443616867,
0.024424904957413673,
0.03417038172483444,
0.06166483089327812,
0.06682080030441284,
0.06027458235621452,
-0.01695110835134983,
0.038380641490221024,
0.07814456522464752,
0.011795648373663425,
0.02757577... |
https://github.com/scikit-learn/scikit-learn/issues/25150 | [
"Performance",
"module:tree"
] | Improving IsolationForest predict time
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25142
<div type='discussions-op-text'>
<sup>Originally posted by **fsiola** December 8, 2022</sup>
Hi,
When using [IsolationForest predict](https://github.com/scikit-learn/scikit-learn/blob/main... | 25,150 | [
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0.013077999465167522,
0.011126887053251266,
-0.001443929155357182... |
https://github.com/scikit-learn/scikit-learn/issues/25145 | [
"Bug"
] | `check_array` unexpectedly upcasts numeric types in pandas Series
### Describe the bug
This is an unexpected (and I would argue undesirable) behavior change introduced in 1.2.0 by https://github.com/scikit-learn/scikit-learn/pull/25080
The issue is that `check_array` applied to a pandas series of dtype `bool` upca... | 25,145 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/25145 | [
"Bug"
] | `check_array` unexpectedly upcasts numeric types in pandas Series
### Describe the bug
This is an unexpected (and I would argue undesirable) behavior change introduced in 1.2.0 by https://github.com/scikit-learn/scikit-learn/pull/25080
The issue is that `check_array` applied to a pandas series of dtype `bool` upca... | 25,145 | [
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0.010113682597875595,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25144 | [
"Bug",
"Needs Triage"
] | RandomizedSearchCV does not stratify folds
### Describe the bug
The documentation states that supplying an integer to cv will, by default, use stratified folds: "For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, [StratifiedKFold] is used."
I am using this as an estimat... | 25,144 | [
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0.01847... |
https://github.com/scikit-learn/scikit-learn/issues/25126 | [
"New Feature",
"Needs Triage"
] | Wrap tabular data in a new dataclass to simplify ML pipelines
### Describe the workflow you want to enable
In my dreams, a new `InferenceData` class would simplify training and prediction to look more like
```
import ... as learner
data = InferenceData(
df=..., # a data frame
meta=Meta(
y... | 25,126 | [
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0.06526951... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no l... | 25,117 | [
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0.055959... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
Until tomorro... | 25,117 | [
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0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
Looking over ... | 25,117 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
The weird stu... | 25,117 | [
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0.03991... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
In a recent r... | 25,117 | [
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0.028... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
> Looking ove... | 25,117 | [
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0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
Here is the o... | 25,117 | [
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0.04138... |
https://github.com/scikit-learn/scikit-learn/issues/25117 | [
"Bug",
"Needs Investigation"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59274&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Sep 18, 2023)
Unable to find junit file. Please see link for details.
COMMENT:
Let me close ... | 25,117 | [
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0.04303... |
https://github.com/scikit-learn/scikit-learn/issues/25116 | [
"Bug",
"Needs Triage"
] | Error when attempting to passthrough transformer step if tuning transformer
### Describe the bug
I am using an imblearn pipeline to perform dimensionality reduction before model training. I would like try either a PCA or skipping the dimensionality reduction step completely (setting step to None). I am also tuning ... | 25,116 | [
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0.0169... |
https://github.com/scikit-learn/scikit-learn/issues/25116 | [
"Bug",
"Needs Triage"
] | Error when attempting to passthrough transformer step if tuning transformer
### Describe the bug
I am using an imblearn pipeline to perform dimensionality reduction before model training. I would like try either a PCA or skipping the dimensionality reduction step completely (setting step to None). I am also tuning ... | 25,116 | [
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0.017362309619784355,
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https://github.com/scikit-learn/scikit-learn/issues/25109 | [
"Documentation",
"Needs Triage"
] | Add link to license in the readme
### Describe the issue linked to the documentation
Thought it would be helpful/convenient to link the license from the readme
### Suggest a potential alternative/fix
It is already available in the top right hand corner of the repository but this is often missed
COMMENT:
The licenc... | 25,109 | [
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-0... |
https://github.com/scikit-learn/scikit-learn/issues/25107 | [
"Needs Decision - Include Feature",
"Validation"
] | Make it easier to overload the `fit_methods` in `check_param_validation`
We introduced `check_param_validation`. This check could be useful to other third-party libraries. However, it will be limited to the fit methods defined in the scikit-learn API.
In imbalanced-learn, we define `fit_resample`. Such a method wou... | 25,107 | [
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0.00619451... |
https://github.com/scikit-learn/scikit-learn/issues/25107 | [
"Needs Decision - Include Feature",
"Validation"
] | Make it easier to overload the `fit_methods` in `check_param_validation`
We introduced `check_param_validation`. This check could be useful to other third-party libraries. However, it will be limited to the fit methods defined in the scikit-learn API.
In imbalanced-learn, we define `fit_resample`. Such a method wou... | 25,107 | [
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0.022417942062020302,
-0.007... |
https://github.com/scikit-learn/scikit-learn/issues/25107 | [
"Needs Decision - Include Feature",
"Validation"
] | Make it easier to overload the `fit_methods` in `check_param_validation`
We introduced `check_param_validation`. This check could be useful to other third-party libraries. However, it will be limited to the fit methods defined in the scikit-learn API.
In imbalanced-learn, we define `fit_resample`. Such a method wou... | 25,107 | [
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0.0052... |
https://github.com/scikit-learn/scikit-learn/issues/25105 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/3606843351)** (Dec 03, 2022)
COMMENT:
This should be solved by https://github.com/scikit-learn/scikit-learn/pull/25104 | 25,105 | [
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0.0656428... |
https://github.com/scikit-learn/scikit-learn/issues/25096 | [
"New Feature",
"Needs Triage"
] | drop correlated features as a pipeline step
### Describe the workflow you want to enable
Doing FeatureSelection droping correlated features is standard ml proc that sklearn covers.
But, as i interpret the documentation, sklearn treats the featureSelection based on correlations as a previous step, outside the pipel... | 25,096 | [
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-0.015137129463255405,
0.01510701235383749,
0.05975774675607681,
-0.04016061872243881,
0.08845648914575577,
0.024014022201299667,
0.012256846763193607,
0.07799514383077621,
0.003825922030955553,
0.06638... |
https://github.com/scikit-learn/scikit-learn/issues/25096 | [
"New Feature",
"Needs Triage"
] | drop correlated features as a pipeline step
### Describe the workflow you want to enable
Doing FeatureSelection droping correlated features is standard ml proc that sklearn covers.
But, as i interpret the documentation, sklearn treats the featureSelection based on correlations as a previous step, outside the pipel... | 25,096 | [
-0.012299920432269573,
0.06247552111744881,
-0.005861037410795689,
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0.0270641278475523,
0.013821649365127087,
0.07081035524606705,
-0.0034377763513475657,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/25095 | [
"Bug",
"Build / CI"
] | ⚠️ 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=49545&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Dec 02, 2022)
- test_balance_property[74-False-LogisticRegressionCV]
COMMENT:
This might n... | 25,095 | [
-0.029428891837596893,
-0.026853829622268677,
0.010835268534719944,
-0.016595477238297462,
0.046683572232723236,
0.00794805958867073,
0.029253976419568062,
0.015758048743009567,
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0.031199688091874123,
0.05365584418177605,
0.009701083414256573,
-0.007764650508761406,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25095 | [
"Bug",
"Build / CI"
] | ⚠️ 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=49545&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Dec 02, 2022)
- test_balance_property[74-False-LogisticRegressionCV]
COMMENT:
I can reprod... | 25,095 | [
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-0.0060957251116633415,
0.00026438251370564103,
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0.021214688196778297,
0.0398271307349205,
0.02688724920153618,
-0.03236996382474899,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25079 | [
"RFC",
"module:inspection"
] | RFC refactoring of PartialDependenceDisplay
Related to https://github.com/scikit-learn/scikit-learn/issues/15641
I would like to discuss the possibility to refactor `PartialDependenceDisplay` to reduce its usability. The idea behind this refactoring is:
- make it consistent with other displays: calling `PartialD... | 25,079 | [
0.02638138271868229,
0.0679243952035904,
0.00794853176921606,
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0.08362229913473129,
0.027743155136704445,
0.023680035024881363,
-0.005847094114869833,
-0.019489748403429985,
0.019444622099399567,
0.010760951787233353,
0.0664... |
https://github.com/scikit-learn/scikit-learn/issues/25079 | [
"RFC",
"module:inspection"
] | RFC refactoring of PartialDependenceDisplay
Related to https://github.com/scikit-learn/scikit-learn/issues/15641
I would like to discuss the possibility to refactor `PartialDependenceDisplay` to reduce its usability. The idea behind this refactoring is:
- make it consistent with other displays: calling `PartialD... | 25,079 | [
0.02638138271868229,
0.0679243952035904,
0.00794853176921606,
-0.01533015351742506,
-0.021689491346478462,
0.003700199304148555,
0.08362229913473129,
0.027743155136704445,
0.023680035024881363,
-0.005847094114869833,
-0.019489748403429985,
0.019444622099399567,
0.010760951787233353,
0.0664... |
https://github.com/scikit-learn/scikit-learn/issues/25079 | [
"RFC",
"module:inspection"
] | RFC refactoring of PartialDependenceDisplay
Related to https://github.com/scikit-learn/scikit-learn/issues/15641
I would like to discuss the possibility to refactor `PartialDependenceDisplay` to reduce its usability. The idea behind this refactoring is:
- make it consistent with other displays: calling `PartialD... | 25,079 | [
0.02638138271868229,
0.0679243952035904,
0.00794853176921606,
-0.01533015351742506,
-0.021689491346478462,
0.003700199304148555,
0.08362229913473129,
0.027743155136704445,
0.023680035024881363,
-0.005847094114869833,
-0.019489748403429985,
0.019444622099399567,
0.010760951787233353,
0.0664... |
https://github.com/scikit-learn/scikit-learn/issues/25079 | [
"RFC",
"module:inspection"
] | RFC refactoring of PartialDependenceDisplay
Related to https://github.com/scikit-learn/scikit-learn/issues/15641
I would like to discuss the possibility to refactor `PartialDependenceDisplay` to reduce its usability. The idea behind this refactoring is:
- make it consistent with other displays: calling `PartialD... | 25,079 | [
0.02638138271868229,
0.0679243952035904,
0.00794853176921606,
-0.01533015351742506,
-0.021689491346478462,
0.003700199304148555,
0.08362229913473129,
0.027743155136704445,
0.023680035024881363,
-0.005847094114869833,
-0.019489748403429985,
0.019444622099399567,
0.010760951787233353,
0.0664... |
https://github.com/scikit-learn/scikit-learn/issues/25078 | [
"Bug"
] | check_array does not gracefully fail with pd.NA
When a NumPy array or a pandas series contains `pd.NA`, it will not gracefully fail.
```python
In [1]: import pandas as pd
In [2]: s = pd.Series([1, 2, None], dtype="Int64")
In [3]: from sklearn.utils.validation import check_array
In [4]: check_array(s, ensu... | 25,078 | [
-0.0032619249541312456,
0.026644103229045868,
0.018156467005610466,
-0.011326746083796024,
0.07893750071525574,
0.03991943225264549,
0.06163749843835831,
0.007080403156578541,
0.010113708674907684,
0.035021428018808365,
0.03222838044166565,
-0.014326835982501507,
-0.011670751497149467,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25078 | [
"Bug"
] | check_array does not gracefully fail with pd.NA
When a NumPy array or a pandas series contains `pd.NA`, it will not gracefully fail.
```python
In [1]: import pandas as pd
In [2]: s = pd.Series([1, 2, None], dtype="Int64")
In [3]: from sklearn.utils.validation import check_array
In [4]: check_array(s, ensu... | 25,078 | [
-0.0032619249541312456,
0.026644103229045868,
0.018156467005610466,
-0.011326746083796024,
0.07893750071525574,
0.03991943225264549,
0.06163749843835831,
0.007080403156578541,
0.010113708674907684,
0.035021428018808365,
0.03222838044166565,
-0.014326835982501507,
-0.011670751497149467,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25075 | [
"Documentation",
"help wanted"
] | Wrong description for the `n_jobs` in mean_shift docs
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25059
<div type='discussions-op-text'>
<sup>Originally posted by **gittar** November 28, 2022</sup>
Should in these doc fragments the variable `n_init` be replaced by `n_jobs`? It appe... | 25,075 | [
-0.020532213151454926,
-0.026357140392065048,
-0.009685484692454338,
0.01607172191143036,
-0.047815486788749695,
0.026513734832406044,
0.09196673333644867,
-0.04856395721435547,
-0.04491884261369705,
-0.005729696713387966,
0.08234520256519318,
0.023690115660429,
0.014732280746102333,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/25075 | [
"Documentation",
"help wanted"
] | Wrong description for the `n_jobs` in mean_shift docs
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25059
<div type='discussions-op-text'>
<sup>Originally posted by **gittar** November 28, 2022</sup>
Should in these doc fragments the variable `n_init` be replaced by `n_jobs`? It appe... | 25,075 | [
-0.026367835700511932,
-0.007764179725199938,
-0.000019390476154512726,
-0.0012263469398021698,
-0.04710741713643074,
0.011160940863192081,
0.09228675067424774,
-0.04791998863220215,
-0.046273373067379,
-0.004521983675658703,
0.09539953619241714,
0.010830814018845558,
0.017452377825975418,
... |
https://github.com/scikit-learn/scikit-learn/issues/25074 | [
"Documentation"
] | Output is missing in this particular codeblock in "sklearn.compose.ColumnTransformer"
### Describe the issue linked to the documentation
At the last line of the code that is X_trans = ct.fit_transform(X) the output(X_trans variable) is not specified which is confusing.
This particular code block is present in
... | 25,074 | [
-0.01124954130500555,
-0.03320424631237984,
-0.0016953235026448965,
0.03241344913840294,
0.038692254573106766,
-0.004423463251441717,
0.049359314143657684,
0.01588219776749611,
-0.03400201350450516,
0.027718165889382362,
0.03684971481561661,
0.04533730819821358,
0.03940419852733612,
0.0597... |
https://github.com/scikit-learn/scikit-learn/issues/25074 | [
"Documentation"
] | Output is missing in this particular codeblock in "sklearn.compose.ColumnTransformer"
### Describe the issue linked to the documentation
At the last line of the code that is X_trans = ct.fit_transform(X) the output(X_trans variable) is not specified which is confusing.
This particular code block is present in
... | 25,074 | [
0.005469434894621372,
-0.036715466529130936,
0.008040101267397404,
0.03135424107313156,
0.0663730800151825,
0.00952216237783432,
0.04919179156422615,
0.039087455719709396,
-0.04184602200984955,
0.0057558040134608746,
0.031178291887044907,
0.040879394859075546,
0.0411505401134491,
0.0446731... |
https://github.com/scikit-learn/scikit-learn/issues/25074 | [
"Documentation"
] | Output is missing in this particular codeblock in "sklearn.compose.ColumnTransformer"
### Describe the issue linked to the documentation
At the last line of the code that is X_trans = ct.fit_transform(X) the output(X_trans variable) is not specified which is confusing.
This particular code block is present in
... | 25,074 | [
-0.010172744281589985,
-0.03111153654754162,
-0.0029405392706394196,
0.03456658869981766,
0.038912076503038406,
-0.0053308517672121525,
0.05015969276428223,
0.01594392955303192,
-0.0358559750020504,
0.028740614652633667,
0.03408462554216385,
0.045254532247781754,
0.03762795776128769,
0.058... |
https://github.com/scikit-learn/scikit-learn/issues/25074 | [
"Documentation"
] | Output is missing in this particular codeblock in "sklearn.compose.ColumnTransformer"
### Describe the issue linked to the documentation
At the last line of the code that is X_trans = ct.fit_transform(X) the output(X_trans variable) is not specified which is confusing.
This particular code block is present in
... | 25,074 | [
-0.006987307220697403,
-0.03603486716747284,
-0.003918876871466637,
0.04222315177321434,
0.043943051248788834,
0.010178268887102604,
0.05413133278489113,
0.03292069584131241,
-0.03835531324148178,
0.021421998739242554,
0.04473132640123367,
0.035414569079875946,
0.042324818670749664,
0.0485... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
https://github.com/scikit-learn/scikit-learn/issues/25073 | [
"Bug"
] | ValueError: "Unknown label type: 'unknown'" when class column has Pandas type like Int64
### Describe the bug
I use Pandas to load data from CSV and transform it.
Pandas often parses integer columns as float, so I usually use `df = df.convert_dtypes()` to bring those columsn back to int.
It looks like this causes P... | 25,073 | [
-0.006638310384005308,
0.03654571250081062,
0.048246368765830994,
0.02610507234930992,
0.12454091012477875,
0.037175584584474564,
0.06197855621576309,
0.05616217851638794,
0.02192394807934761,
-0.03558148071169853,
0.03291179612278938,
0.027423663064837456,
-0.004946049768477678,
0.0467560... |
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