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/29929 | [
"Bug",
"Needs Reproducible Code"
] | Custom estimator's fit() method throws "RuntimeWarning: invalid value encountered in cast" in Linux Python 3.11/3.12
### Describe the bug
We have a custom estimator class that inherits from `sklearn.base.BaseEstimator` and `RegressorMixin`. We run automated unit tests in Azure DevOps pipelines on both Windows Serve... | 29,929 | [
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0.05141863971948624,
0.04935675859451294,
0.006345127243548632,
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https://github.com/scikit-learn/scikit-learn/issues/29927 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 25, 2024) ⚠️
**CI failed on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70481&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 25, 2024)
Unable to find junit file. Please se... | 29,927 | [
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0.060518... |
https://github.com/scikit-learn/scikit-learn/issues/29927 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 25, 2024) ⚠️
**CI failed on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70481&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 25, 2024)
Unable to find junit file. Please se... | 29,927 | [
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https://github.com/scikit-learn/scikit-learn/issues/29925 | [
"API",
"module:metrics"
] | Remove sokalmichener from distance metrics
SciPy is planning to remove `sokalmichener`: https://github.com/scipy/scipy/pull/21572
We reimplement `SokalMichenerDistance` in the distance metric, and it's exactly the same as the implementation `RogersTanimotoDistance`. We can follow SciPy's lead and remove `sokalmiche... | 29,925 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
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0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
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0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
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0.08544471859931946,
-0.014201820828020573,
-0.02365328185260296,
0.019100338220596313,
0.009139295667409897,
0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
-0.00982373021543026,
0.08544471859931946,
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-0.02365328185260296,
0.019100338220596313,
0.009139295667409897,
0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
-0.00982373021543026,
0.08544471859931946,
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-0.02365328185260296,
0.019100338220596313,
0.009139295667409897,
0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
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0.08544471859931946,
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0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
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0.08544471859931946,
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0.019100338220596313,
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0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29922 | [
"Enhancement"
] | Random forest regression fails when calling data: probably a numerical error
### Describe the bug
It is known that random forrest regression (as well as many decision tree-based methods) are not affected by the scale of the data and don't require any scaling in the feature matrix or response vector. This includes a... | 29,922 | [
0.02171395719051361,
-0.01631312444806099,
0.06609132885932922,
-0.00982373021543026,
0.08544471859931946,
-0.014201820828020573,
-0.02365328185260296,
0.019100338220596313,
0.009139295667409897,
0.017938213422894478,
0.007377500645816326,
0.0030314119067043066,
0.019705673679709435,
0.011... |
https://github.com/scikit-learn/scikit-learn/issues/29917 | [
"Easy",
"Documentation",
"help wanted"
] | `**params` documentation for `GridSearchCV.fit` is ambiguous
[`GridSearchCV.fit`](https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit)
### Describe the issue linked to the documentation
The documentation for the `**params` parameter to... | 29,917 | [
0.031243043020367622,
-0.04315980151295662,
0.02094031311571598,
0.014930916018784046,
0.05235429108142853,
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0.054123591631650925,
0.00890662707388401,
0.015638047829270363,
-0.021944120526313782,
0.03912736847996712,
0.004734449554234743,
0.049372270703315735,
-0.0130... |
https://github.com/scikit-learn/scikit-learn/issues/29917 | [
"Easy",
"Documentation",
"help wanted"
] | `**params` documentation for `GridSearchCV.fit` is ambiguous
[`GridSearchCV.fit`](https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit)
### Describe the issue linked to the documentation
The documentation for the `**params` parameter to... | 29,917 | [
0.031243043020367622,
-0.04315980151295662,
0.02094031311571598,
0.014930916018784046,
0.05235429108142853,
-0.02872161753475666,
0.054123591631650925,
0.00890662707388401,
0.015638047829270363,
-0.021944120526313782,
0.03912736847996712,
0.004734449554234743,
0.049372270703315735,
-0.0130... |
https://github.com/scikit-learn/scikit-learn/issues/29906 | [
"Bug"
] | Incorrect sample weight handling in `KBinsDiscretizer`
### Describe the bug
Sample weights are not properly passed through when specifying subsample within KBinsDiscretizer.
### Steps/Code to Reproduce
```python
from sklearn.datasets import make_blobs
from sklearn.preprocessing import KBinsDiscretizer
impo... | 29,906 | [
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-0.09018127620220184,
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0.05252108350396156,
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0.020221488550305367,
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0.039954256266355515,
0.04555586352944374,
0.04060006141662598,
0.013578195124864578,
0.011329790577292442,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29905 | [
"New Feature",
"Needs Info"
] | Training final model with cross validation and using it to get unbiased probabilities
### Describe the workflow you want to enable
I want to use crossvalidation with let's say k=4 in order to get four models. That means that each sample in my dataset was used to train 3 of the four models. Thus, if I want to get a pr... | 29,905 | [
-0.015552226454019547,
0.058418869972229004,
0.029895810410380363,
-0.012797951698303223,
0.009890304878354073,
0.0033241198398172855,
0.06677082180976868,
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0.07724761217832565,
-0.03504855930805206,
-0.00729198195040226,
0.04008419066667557,
-0.033676449209451675,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29905 | [
"New Feature",
"Needs Info"
] | Training final model with cross validation and using it to get unbiased probabilities
### Describe the workflow you want to enable
I want to use crossvalidation with let's say k=4 in order to get four models. That means that each sample in my dataset was used to train 3 of the four models. Thus, if I want to get a pr... | 29,905 | [
-0.015552226454019547,
0.058418869972229004,
0.029895810410380363,
-0.012797951698303223,
0.009890304878354073,
0.0033241198398172855,
0.06677082180976868,
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0.07724761217832565,
-0.03504855930805206,
-0.00729198195040226,
0.04008419066667557,
-0.033676449209451675,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29905 | [
"New Feature",
"Needs Info"
] | Training final model with cross validation and using it to get unbiased probabilities
### Describe the workflow you want to enable
I want to use crossvalidation with let's say k=4 in order to get four models. That means that each sample in my dataset was used to train 3 of the four models. Thus, if I want to get a pr... | 29,905 | [
-0.015552226454019547,
0.058418869972229004,
0.029895810410380363,
-0.012797951698303223,
0.009890304878354073,
0.0033241198398172855,
0.06677082180976868,
-0.010817011818289757,
0.07724761217832565,
-0.03504855930805206,
-0.00729198195040226,
0.04008419066667557,
-0.033676449209451675,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29902 | [
"Bug",
"Needs Triage"
] | ImportError: cannot import name 'InconsistentVersionWarning' in sklearn.exceptions
### Describe the bug
The error message "ImportError: cannot import name 'InconsistentVersionWarning'“ occurs when there is an attempt to import the sklearn
### Steps/Code to Reproduce
import sklearn
### Expected Results
successful ... | 29,902 | [
0.027139795944094658,
-0.044683653861284256,
0.01671445183455944,
-0.019651437178254128,
0.06570567935705185,
0.04586632922291756,
0.014387295581400394,
0.01758689247071743,
0.06474824994802475,
-0.011738070286810398,
0.06789802759885788,
0.056746382266283035,
-0.04081498086452484,
0.01495... |
https://github.com/scikit-learn/scikit-learn/issues/29902 | [
"Bug",
"Needs Triage"
] | ImportError: cannot import name 'InconsistentVersionWarning' in sklearn.exceptions
### Describe the bug
The error message "ImportError: cannot import name 'InconsistentVersionWarning'“ occurs when there is an attempt to import the sklearn
### Steps/Code to Reproduce
import sklearn
### Expected Results
successful ... | 29,902 | [
0.03420966491103172,
-0.019260626286268234,
0.018084004521369934,
-0.025439806282520294,
0.08421891927719116,
0.05168255791068077,
0.012511544860899448,
0.03606026619672775,
0.04328092560172081,
-0.02363455295562744,
0.04049655422568321,
0.0594446025788784,
-0.050735313445329666,
-0.036005... |
https://github.com/scikit-learn/scikit-learn/issues/29902 | [
"Bug",
"Needs Triage"
] | ImportError: cannot import name 'InconsistentVersionWarning' in sklearn.exceptions
### Describe the bug
The error message "ImportError: cannot import name 'InconsistentVersionWarning'“ occurs when there is an attempt to import the sklearn
### Steps/Code to Reproduce
import sklearn
### Expected Results
successful ... | 29,902 | [
0.023611299693584442,
-0.01542409136891365,
0.011906582862138748,
-0.019177161157131195,
0.07851310819387436,
0.0437990203499794,
0.011124090291559696,
0.03197464719414711,
0.06124204769730568,
-0.019935907796025276,
0.06560657173395157,
0.056282125413417816,
-0.07046141475439072,
-0.00404... |
https://github.com/scikit-learn/scikit-learn/issues/29901 | [
"New Feature",
"module:linear_model"
] | proper sparse support in glm's with newton-cholesky
### Describe the workflow you want to enable
When a user fits a glm with a sparse X, I believe the newton-cholesky solver ultimately creates a dense hessian, and the newton step is solved using scipy's dense symmetric linear solve. Instead I think SKL should create... | 29,901 | [
0.005168176256120205,
0.022560833021998405,
0.06967592239379883,
-0.007061539683490992,
0.04130888730287552,
-0.008767097257077694,
0.010781900957226753,
0.034707751125097275,
0.023611057549715042,
-0.030613092705607414,
0.01910007931292057,
0.02480064146220684,
-0.0034475894644856453,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29901 | [
"New Feature",
"module:linear_model"
] | proper sparse support in glm's with newton-cholesky
### Describe the workflow you want to enable
When a user fits a glm with a sparse X, I believe the newton-cholesky solver ultimately creates a dense hessian, and the newton step is solved using scipy's dense symmetric linear solve. Instead I think SKL should create... | 29,901 | [
0.0044347005896270275,
0.02260829694569111,
0.06879735738039017,
-0.006519857328385115,
0.040983106940984726,
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0.014115078374743462,
0.034814704209566116,
0.022819330915808678,
-0.03027273528277874,
0.0202605202794075,
0.025256870314478874,
-0.003184582106769085,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29901 | [
"New Feature",
"module:linear_model"
] | proper sparse support in glm's with newton-cholesky
### Describe the workflow you want to enable
When a user fits a glm with a sparse X, I believe the newton-cholesky solver ultimately creates a dense hessian, and the newton step is solved using scipy's dense symmetric linear solve. Instead I think SKL should create... | 29,901 | [
0.005697214975953102,
0.021664384752511978,
0.0691976323723793,
-0.0076404111459851265,
0.040415357798337936,
-0.008254876360297203,
0.014433667995035648,
0.033825989812612534,
0.022679148241877556,
-0.03053921088576317,
0.02010238543152809,
0.025163592770695686,
-0.0027584435883909464,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29900 | [
"Easy",
"Documentation"
] | Docs for estimator types do not list all possible estimator types
### Describe the issue linked to the documentation
The docs for 'Developing scikit-learn estimators' mention that one should specify the estimator type:
https://scikit-learn.org/stable/developers/develop.html#estimator-types
It lists the options as... | 29,900 | [
-0.02730107493698597,
-0.036898206919431686,
0.01759280264377594,
-0.0003735792706720531,
0.04714355617761612,
0.0030567676294595003,
0.09029259532690048,
0.011231807991862297,
0.03863435611128807,
-0.00671045295894146,
0.06960645318031311,
0.08804040402173996,
-0.006768375169485807,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29900 | [
"Easy",
"Documentation"
] | Docs for estimator types do not list all possible estimator types
### Describe the issue linked to the documentation
The docs for 'Developing scikit-learn estimators' mention that one should specify the estimator type:
https://scikit-learn.org/stable/developers/develop.html#estimator-types
It lists the options as... | 29,900 | [
-0.011855142191052437,
-0.05233180150389671,
0.017111683264374733,
0.0024262985680252314,
0.043885063380002975,
0.0071250866167247295,
0.09196596592664719,
0.013662398792803288,
0.0453980378806591,
-0.008670206181704998,
0.06652843207120895,
0.07780922949314117,
0.008952230215072632,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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0.04332611709833145,
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0.016123199835419655,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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0.04332611709833145,
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0.016123199835419655,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29893 | [
"API",
"RFC"
] | Implications of FrozenEstimator on our API
With https://github.com/scikit-learn/scikit-learn/pull/29705, we have a simple way to freeze estimators, which means there is no need for `cv="prefit"`. This also opens the door for https://github.com/scikit-learn/scikit-learn/pull/8350 to make `Pipeline` and `FeatureUnion` f... | 29,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/29891 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10978032969)** (Sep 22, 2024)
COMMENT:
The same tests are failing here: https://github.com/scikit-learn/scikit-learn/issues/29889 | 29,891 | [
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https://github.com/scikit-learn/scikit-learn/issues/29891 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10978032969)** (Sep 22, 2024)
COMMENT:
The root cause is likely numpy-dev or scipy-dev https://github.com/scikit-learn/scikit-learn/issues/29864 | 29,891 | [
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https://github.com/scikit-learn/scikit-learn/issues/29891 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10978032969)** (Sep 22, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10987327652)... | 29,891 | [
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https://github.com/scikit-learn/scikit-learn/issues/29889 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 22, 2024)
- test... | 29,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/29889 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 22, 2024)
- test... | 29,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/29889 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 22, 2024)
- test... | 29,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/29873 | [
"Bug",
"Needs Triage"
] | sklearn.neighbors.NearestNeighbors may have a bug
### Describe the bug
I found a suspected error in NearestNeighbors:
``` python
from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=2).fit(yields[if_predict == -1][:130])
distances, indices = nbrs.kneighbors(yields[if_predict == -1][:... | 29,873 | [
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https://github.com/scikit-learn/scikit-learn/issues/29873 | [
"Bug",
"Needs Triage"
] | sklearn.neighbors.NearestNeighbors may have a bug
### Describe the bug
I found a suspected error in NearestNeighbors:
``` python
from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=2).fit(yields[if_predict == -1][:130])
distances, indices = nbrs.kneighbors(yields[if_predict == -1][:... | 29,873 | [
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https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
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0.09352... |
https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
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https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
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0.0034510046243667603,
0.07521874457597733,
0.03416866064071655,
-0.04163843020796776,
-0.019072119146585464,
0.01857456937432289,
0.0673912987112999,
-0.03960341215133667,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
-0.00585208972916007,
0.05127286911010742,
-0.010155375115573406,
-0.026779262349009514,
0.006116779521107674,
0.002236374421045184,
0.06102761626243591,
0.03982715308666229,
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-0.004973534028977156,
0.01855139434337616,
0.08255916833877563,
-0.050755470991134644,
0.092... |
https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
-0.005138786043971777,
0.0529065877199173,
-0.010531121864914894,
-0.02681642584502697,
0.005276969633996487,
0.006289733108133078,
0.055644676089286804,
0.03590765967965126,
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-0.005299513693898916,
0.019045377150177956,
0.07883937656879425,
-0.04541619494557381,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/29870 | [
"Build / CI"
] | Publish Python 3.13 wheels on PyPI for 1.5.2
### Describe the workflow you want to enable
Hello,
Could you please release CPython 3.13 manylinux wheels on PyPI?
Python 3.13.0~rc2 has already been released and there will be no ABI changes even for bug fixes at this point.
It will help projects starts using scikit-l... | 29,870 | [
-0.0028411769308149815,
0.04568883776664734,
-0.013379515148699284,
-0.026622086763381958,
0.004968549590557814,
0.0032211975194513798,
0.06231321766972542,
0.040334321558475494,
-0.0325218103826046,
-0.0031856780406087637,
0.01672970876097679,
0.07885634154081345,
-0.04535645619034767,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
0.013019449077546597,
0.04817011207342148,
0.006711489986628294,
-0.014805687591433525,
0.042963121086359024,
0.017355388030409813,
0.03617006912827492,
0.061109405010938644,
0.003764261957257986,
0.010908330790698528,
0.058260247111320496,
0.035554394125938416,
-0.010521231219172478,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
0.012830574065446854,
0.03783278912305832,
-0.005021087825298309,
-0.03985098376870155,
0.04656948894262314,
0.009162289090454578,
0.033487431704998016,
0.05906614288687706,
0.008799456991255283,
0.01984824799001217,
0.06463117152452469,
0.03184482082724571,
-0.006464047357439995,
0.074464... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
-0.0017438868526369333,
0.056660134345293045,
-0.000010970533367071766,
-0.04475686699151993,
0.05792045593261719,
0.014109228737652302,
0.03602391481399536,
0.0709492564201355,
0.02121751382946968,
0.02613927237689495,
0.04936056584119797,
0.038804180920124054,
-0.019731612876057625,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
-0.0025632716715335846,
0.04251381754875183,
-0.007949030958116055,
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0.051108717918395996,
0.01762995682656765,
0.037098370492458344,
0.05507393181324005,
0.006461434066295624,
0.0325726717710495,
0.07072372734546661,
0.03187716007232666,
-0.013978736475110054,
0.0833... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
0.0010643129935488105,
0.028871458023786545,
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0.04941604658961296,
0.018549565225839615,
0.03608347475528717,
0.059168003499507904,
0.019102023914456367,
0.022763332352042198,
0.06527703255414963,
0.03630511090159416,
-0.009750736877322197,
0.0798... |
https://github.com/scikit-learn/scikit-learn/issues/29864 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Sep 22, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70432&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Sep 22, 2024)
- test_lbfgs_solver_consis... | 29,864 | [
-0.0015958023723214865,
0.03591883182525635,
-0.007921977899968624,
-0.04792198911309242,
0.056317124515771866,
0.015779288485646248,
0.032356198877096176,
0.05624351277947426,
0.014355351217091084,
0.02921881526708603,
0.05755789205431938,
0.030890991911292076,
-0.011335590854287148,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29862 | [
"Needs Info"
] | "int64 indices" error in fit_predict function even with 32-bit integer
### Describe the bug
I'm trying to apply spectral clustering on a sparse adjacency matrix of a surface mesh. Although the matrix's entries are using 32-bit integer indices, the `fit_predict` function gives me the following error:
```
ValueErr... | 29,862 | [
-0.026402726769447327,
-0.06188088282942772,
0.01379098929464817,
0.047692347317934036,
0.0617000088095665,
-0.0052220928482711315,
0.0405535064637661,
0.0466129444539547,
0.07814466953277588,
-0.020352229475975037,
-0.01369692012667656,
0.03832836449146271,
0.0026195640675723553,
0.043951... |
https://github.com/scikit-learn/scikit-learn/issues/29858 | [
"Bug",
"Needs Triage"
] | Sklearn train_test_split gives incorrect array outputs.
### Describe the bug
I suspect this is because I give the function more than one array to split, but according to the documentation train_test_split should be able to take any number of arrays?
Code to reproduce:
```
test_numerical = np.random.rand(2509, 9)... | 29,858 | [
-0.0036843318957835436,
-0.03710363432765007,
-0.00016405376663897187,
0.05446115881204605,
0.07290981709957123,
-0.02241622842848301,
0.07350335270166397,
0.0249328576028347,
-0.021755559369921684,
-0.018187008798122406,
0.008248373866081238,
0.007327968254685402,
-0.019219446927309036,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29858 | [
"Bug",
"Needs Triage"
] | Sklearn train_test_split gives incorrect array outputs.
### Describe the bug
I suspect this is because I give the function more than one array to split, but according to the documentation train_test_split should be able to take any number of arrays?
Code to reproduce:
```
test_numerical = np.random.rand(2509, 9)... | 29,858 | [
-0.0036843318957835436,
-0.03710363432765007,
-0.00016405376663897187,
0.05446115881204605,
0.07290981709957123,
-0.02241622842848301,
0.07350335270166397,
0.0249328576028347,
-0.021755559369921684,
-0.018187008798122406,
0.008248373866081238,
0.007327968254685402,
-0.019219446927309036,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29856 | [
"Bug"
] | ClassifierChain does not accept NaN values even when base estimator supports them
### Describe the bug
I am working on a multilabel classification problem using ClassifierChain with RandomForestClassifier as the base estimator.
I have encountered an issue where ClassifierChain raises a ValueError when the input da... | 29,856 | [
0.02511005476117134,
0.060849446803331375,
0.044339798390865326,
-0.014726982451975346,
0.05139165371656418,
-0.01984560117125511,
0.03518490493297577,
0.02205188013613224,
-0.012974781915545464,
0.009908098727464676,
0.02419087663292885,
0.013269759714603424,
0.01444808766245842,
-0.01948... |
https://github.com/scikit-learn/scikit-learn/issues/29856 | [
"Bug"
] | ClassifierChain does not accept NaN values even when base estimator supports them
### Describe the bug
I am working on a multilabel classification problem using ClassifierChain with RandomForestClassifier as the base estimator.
I have encountered an issue where ClassifierChain raises a ValueError when the input da... | 29,856 | [
0.02511005476117134,
0.060849446803331375,
0.044339798390865326,
-0.014726982451975346,
0.05139165371656418,
-0.01984560117125511,
0.03518490493297577,
0.02205188013613224,
-0.012974781915545464,
0.009908098727464676,
0.02419087663292885,
0.013269759714603424,
0.01444808766245842,
-0.01948... |
https://github.com/scikit-learn/scikit-learn/issues/29856 | [
"Bug"
] | ClassifierChain does not accept NaN values even when base estimator supports them
### Describe the bug
I am working on a multilabel classification problem using ClassifierChain with RandomForestClassifier as the base estimator.
I have encountered an issue where ClassifierChain raises a ValueError when the input da... | 29,856 | [
0.02511005476117134,
0.060849446803331375,
0.044339798390865326,
-0.014726982451975346,
0.05139165371656418,
-0.01984560117125511,
0.03518490493297577,
0.02205188013613224,
-0.012974781915545464,
0.009908098727464676,
0.02419087663292885,
0.013269759714603424,
0.01444808766245842,
-0.01948... |
https://github.com/scikit-learn/scikit-learn/issues/29856 | [
"Bug"
] | ClassifierChain does not accept NaN values even when base estimator supports them
### Describe the bug
I am working on a multilabel classification problem using ClassifierChain with RandomForestClassifier as the base estimator.
I have encountered an issue where ClassifierChain raises a ValueError when the input da... | 29,856 | [
0.02511005476117134,
0.060849446803331375,
0.044339798390865326,
-0.014726982451975346,
0.05139165371656418,
-0.01984560117125511,
0.03518490493297577,
0.02205188013613224,
-0.012974781915545464,
0.009908098727464676,
0.02419087663292885,
0.013269759714603424,
0.01444808766245842,
-0.01948... |
https://github.com/scikit-learn/scikit-learn/issues/29856 | [
"Bug"
] | ClassifierChain does not accept NaN values even when base estimator supports them
### Describe the bug
I am working on a multilabel classification problem using ClassifierChain with RandomForestClassifier as the base estimator.
I have encountered an issue where ClassifierChain raises a ValueError when the input da... | 29,856 | [
0.02511005476117134,
0.060849446803331375,
0.044339798390865326,
-0.014726982451975346,
0.05139165371656418,
-0.01984560117125511,
0.03518490493297577,
0.02205188013613224,
-0.012974781915545464,
0.009908098727464676,
0.02419087663292885,
0.013269759714603424,
0.01444808766245842,
-0.01948... |
https://github.com/scikit-learn/scikit-learn/issues/29850 | [
"Bug",
"Needs Triage"
] | `cross_validate` accepts `sample_weight` in fitted estimator, but should raise or warn
### Describe the bug
When we pass a fitted estimator into `cross_validate` it will fit this estimator again on the given train-validation splits.
However, users can pass `sample_weight` to the fitted estimator without being warn... | 29,850 | [
-0.002972740912809968,
-0.014577544294297695,
0.04413960501551628,
0.02658996172249317,
0.08472239971160889,
-0.02643417939543724,
0.02177748642861843,
0.027387574315071106,
0.019656281918287277,
0.0012680364307016134,
0.01258785743266344,
0.10838167369365692,
0.001538348849862814,
-0.0120... |
https://github.com/scikit-learn/scikit-learn/issues/29850 | [
"Bug",
"Needs Triage"
] | `cross_validate` accepts `sample_weight` in fitted estimator, but should raise or warn
### Describe the bug
When we pass a fitted estimator into `cross_validate` it will fit this estimator again on the given train-validation splits.
However, users can pass `sample_weight` to the fitted estimator without being warn... | 29,850 | [
-0.002972740912809968,
-0.014577544294297695,
0.04413960501551628,
0.02658996172249317,
0.08472239971160889,
-0.02643417939543724,
0.02177748642861843,
0.027387574315071106,
0.019656281918287277,
0.0012680364307016134,
0.01258785743266344,
0.10838167369365692,
0.001538348849862814,
-0.0120... |
https://github.com/scikit-learn/scikit-learn/issues/29850 | [
"Bug",
"Needs Triage"
] | `cross_validate` accepts `sample_weight` in fitted estimator, but should raise or warn
### Describe the bug
When we pass a fitted estimator into `cross_validate` it will fit this estimator again on the given train-validation splits.
However, users can pass `sample_weight` to the fitted estimator without being warn... | 29,850 | [
-0.002972740912809968,
-0.014577544294297695,
0.04413960501551628,
0.02658996172249317,
0.08472239971160889,
-0.02643417939543724,
0.02177748642861843,
0.027387574315071106,
0.019656281918287277,
0.0012680364307016134,
0.01258785743266344,
0.10838167369365692,
0.001538348849862814,
-0.0120... |
https://github.com/scikit-learn/scikit-learn/issues/29850 | [
"Bug",
"Needs Triage"
] | `cross_validate` accepts `sample_weight` in fitted estimator, but should raise or warn
### Describe the bug
When we pass a fitted estimator into `cross_validate` it will fit this estimator again on the given train-validation splits.
However, users can pass `sample_weight` to the fitted estimator without being warn... | 29,850 | [
-0.002972740912809968,
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0.04413960501551628,
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0.0012680364307016134,
0.01258785743266344,
0.10838167369365692,
0.001538348849862814,
-0.0120... |
https://github.com/scikit-learn/scikit-learn/issues/29849 | [
"Documentation",
"RFC"
] | Adding scikit-learn to the pydata-sphinx-theme gallery of sites
As described in the title, I wonder if we want to add scikit-learn to the list of pydata-sphinx-theme gallery of sites: https://pydata-sphinx-theme.readthedocs.io/en/stable/examples/gallery.html. If we do I can ask pydata-sphinx-theme about it.
COMMENT:
... | 29,849 | [
0.05358000472187996,
0.0225851908326149,
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0.0071808947250247,
0.019953057169914246,
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-0.027313804253935814,
0.019662901759147644,
-0.01602180115878582,
0.0462... |
https://github.com/scikit-learn/scikit-learn/issues/29849 | [
"Documentation",
"RFC"
] | Adding scikit-learn to the pydata-sphinx-theme gallery of sites
As described in the title, I wonder if we want to add scikit-learn to the list of pydata-sphinx-theme gallery of sites: https://pydata-sphinx-theme.readthedocs.io/en/stable/examples/gallery.html. If we do I can ask pydata-sphinx-theme about it.
COMMENT:
... | 29,849 | [
0.06021498888731003,
0.0161930900067091,
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0.012611848302185535,
0.01789812371134758,
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0.06025438755750656,
0.006845066789537668,
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-0.008532840758562088,
-0.02730991132557392,
0.018988370895385742,
-0.007129061967134476,
0.06336... |
https://github.com/scikit-learn/scikit-learn/issues/29849 | [
"Documentation",
"RFC"
] | Adding scikit-learn to the pydata-sphinx-theme gallery of sites
As described in the title, I wonder if we want to add scikit-learn to the list of pydata-sphinx-theme gallery of sites: https://pydata-sphinx-theme.readthedocs.io/en/stable/examples/gallery.html. If we do I can ask pydata-sphinx-theme about it.
COMMENT:
... | 29,849 | [
0.05455087870359421,
0.01958087645471096,
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0.009487232193350792,
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0.0721876472234726,
0.0012935902923345566,
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-0.001273446367122233,
-0.03275076299905777,
0.017075587064027786,
-0.012174014933407307,
0.0427... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.05518261343240738,
0.028271090239286423,
-0.0028014418203383684,
-0.04579554498195648,
-0.012854608707129955,
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0.03608844801783562,
-0.0005906142177991569,
0.038818828761577606,
0.011594682931900024,
0.04701658710837364,
0.05043087899684906,
-0.04663961008191109,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.04525560140609741,
0.021844785660505295,
-0.020012490451335907,
-0.04045616462826729,
-0.015916522592306137,
-0.027802908793091774,
0.030280960723757744,
-0.0005755700403824449,
0.020338816568255424,
0.012292859144508839,
0.0284570325165987,
0.030404839664697647,
-0.03936726227402687,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.03577057272195816,
-0.01271925400942564,
0.0025415674317628145,
-0.03867674618959427,
0.00012735491327475756,
-0.041175276041030884,
0.009510785341262817,
-0.00020059238886460662,
0.038186609745025635,
0.004681763239204884,
0.055280376225709915,
0.01054950337857008,
-0.05277511104941368,
... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.04189149662852287,
0.0007656619418412447,
-0.0011429499136283994,
-0.035704825073480606,
0.01005532220005989,
-0.03227580338716507,
0.03188652545213699,
-0.00395608926191926,
0.03510509058833122,
0.009755550883710384,
0.04892927408218384,
0.026456112042069435,
-0.037066977471113205,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.03661039099097252,
0.012281919829547405,
-0.020406486466526985,
-0.0288906991481781,
0.01677854359149933,
-0.04082014784216881,
0.004145683720707893,
0.010289863683283329,
0.047907955944538116,
0.006303084548562765,
0.024451760575175285,
0.04437074437737465,
-0.04919195920228958,
0.0284... |
https://github.com/scikit-learn/scikit-learn/issues/29837 | [
"New Feature",
"Needs Decision"
] | Add float as acceptable input for n_jobs
### Describe the workflow you want to enable
Float may be used as possible input for n_jobs. That is, allowing selection of set percentage of the machine's CPU core count.
### Describe your proposed solution
When n_jobs is a float (in the range `(0.0, 1.0]`), the numb... | 29,837 | [
-0.0570673793554306,
0.0030233385041356087,
-0.007360326591879129,
-0.04767575114965439,
0.005972891114652157,
-0.03607665374875069,
0.02445847913622856,
0.00057000364176929,
0.03614044561982155,
0.011601686477661133,
0.044685568660497665,
0.04528260603547096,
-0.044715847820043564,
0.0373... |
https://github.com/scikit-learn/scikit-learn/issues/29836 | [
"Bug",
"Needs Triage"
] | Incorrect calculation of Precision and Recall score from
### Describe the bug
The values calculated for the precision and recall seems to be in opposite of each other.
### Steps/Code to Reproduce
```
from sklearn.metrics import accuracy_score # Accuracy = (TP + TN) / (TP + TN + FP + FN)
from sklearn.metrics impo... | 29,836 | [
0.017810456454753876,
-0.06787417829036713,
0.011903203092515469,
0.037707407027482986,
0.04983691871166229,
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-0.020490264520049095,
-0.03181825950741768,
-0.015171746723353863,
0.06827723234891891,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29830 | [
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: Sep 13, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10842683629)** (Sep 13, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10859271523)... | 29,830 | [
-0.036612287163734436,
0.044185295701026917,
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0.006730820517987013,
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0.032874930649995804,
0.08874090015888214,
0.03405612334609032,
-0.016347920522093773,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/29829 | [
"Build / CI"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 13, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70215&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 13, 2024)
Unable... | 29,829 | [
-0.029525306075811386,
-0.023074403405189514,
-0.03521737828850746,
-0.04753159359097481,
0.01439988985657692,
0.01876450516283512,
0.0163812804967165,
0.05966532602906227,
0.027110785245895386,
0.021190045401453972,
0.03477020189166069,
0.0700450912117958,
-0.03381193056702614,
0.03755799... |
https://github.com/scikit-learn/scikit-learn/issues/29829 | [
"Build / CI"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 13, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70215&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 13, 2024)
Unable... | 29,829 | [
-0.019478173926472664,
-0.03045966476202011,
-0.03963697701692581,
-0.06709173321723938,
0.012581408955156803,
0.016840847209095955,
0.015165498480200768,
0.0368242971599102,
0.05482829362154007,
0.004223398398607969,
0.02165413647890091,
0.05383070185780525,
-0.03880321979522705,
0.018790... |
https://github.com/scikit-learn/scikit-learn/issues/29829 | [
"Build / CI"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 13, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70215&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 13, 2024)
Unable... | 29,829 | [
-0.013866015709936619,
-0.008235237561166286,
-0.047099724411964417,
-0.06075073033571243,
-0.018770894035696983,
0.02705245278775692,
0.0033726717811077833,
0.038494691252708435,
-0.0017276456346735358,
-0.003302834229543805,
0.04394874349236488,
0.029294852167367935,
-0.037332385778427124,... |
https://github.com/scikit-learn/scikit-learn/issues/29829 | [
"Build / CI"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Sep 13, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=70215&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Sep 13, 2024)
Unable... | 29,829 | [
-0.02025671862065792,
-0.02209145948290825,
-0.03824225440621376,
-0.0740421786904335,
0.023838017135858536,
0.02857513353228569,
0.011897221207618713,
0.050588760524988174,
0.0469927042722702,
0.030757742002606392,
0.022296521812677383,
0.049621742218732834,
-0.03240182250738144,
0.049537... |
https://github.com/scikit-learn/scikit-learn/issues/29827 | [
"Bug",
"API"
] | SimpleImputer does not drop a column full of `np.nan` even when `keep_empty_feature=False`
The following code snippet lead to some surprises:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer
X, y = load_iris(return_X_y=True)
X[:, 0] = np.nan
impu... | 29,827 | [
-0.02364782616496086,
-0.024091394618153572,
0.017213284969329834,
-0.01616688258945942,
0.0511457659304142,
-0.02536967024207115,
0.09110564738512039,
0.04303543642163277,
0.01536172442138195,
0.03217851370573044,
0.014501363970339298,
0.010402797721326351,
0.044310539960861206,
0.0408422... |
https://github.com/scikit-learn/scikit-learn/issues/29827 | [
"Bug",
"API"
] | SimpleImputer does not drop a column full of `np.nan` even when `keep_empty_feature=False`
The following code snippet lead to some surprises:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer
X, y = load_iris(return_X_y=True)
X[:, 0] = np.nan
impu... | 29,827 | [
-0.02364782616496086,
-0.024091394618153572,
0.017213284969329834,
-0.01616688258945942,
0.0511457659304142,
-0.02536967024207115,
0.09110564738512039,
0.04303543642163277,
0.01536172442138195,
0.03217851370573044,
0.014501363970339298,
0.010402797721326351,
0.044310539960861206,
0.0408422... |
https://github.com/scikit-learn/scikit-learn/issues/29827 | [
"Bug",
"API"
] | SimpleImputer does not drop a column full of `np.nan` even when `keep_empty_feature=False`
The following code snippet lead to some surprises:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer
X, y = load_iris(return_X_y=True)
X[:, 0] = np.nan
impu... | 29,827 | [
-0.02364782616496086,
-0.024091394618153572,
0.017213284969329834,
-0.01616688258945942,
0.0511457659304142,
-0.02536967024207115,
0.09110564738512039,
0.04303543642163277,
0.01536172442138195,
0.03217851370573044,
0.014501363970339298,
0.010402797721326351,
0.044310539960861206,
0.0408422... |
https://github.com/scikit-learn/scikit-learn/issues/29827 | [
"Bug",
"API"
] | SimpleImputer does not drop a column full of `np.nan` even when `keep_empty_feature=False`
The following code snippet lead to some surprises:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer
X, y = load_iris(return_X_y=True)
X[:, 0] = np.nan
impu... | 29,827 | [
-0.02364782616496086,
-0.024091394618153572,
0.017213284969329834,
-0.01616688258945942,
0.0511457659304142,
-0.02536967024207115,
0.09110564738512039,
0.04303543642163277,
0.01536172442138195,
0.03217851370573044,
0.014501363970339298,
0.010402797721326351,
0.044310539960861206,
0.0408422... |
https://github.com/scikit-learn/scikit-learn/issues/29827 | [
"Bug",
"API"
] | SimpleImputer does not drop a column full of `np.nan` even when `keep_empty_feature=False`
The following code snippet lead to some surprises:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer
X, y = load_iris(return_X_y=True)
X[:, 0] = np.nan
impu... | 29,827 | [
-0.02364782616496086,
-0.024091394618153572,
0.017213284969329834,
-0.01616688258945942,
0.0511457659304142,
-0.02536967024207115,
0.09110564738512039,
0.04303543642163277,
0.01536172442138195,
0.03217851370573044,
0.014501363970339298,
0.010402797721326351,
0.044310539960861206,
0.0408422... |
https://github.com/scikit-learn/scikit-learn/issues/29823 | [
"Documentation"
] | Misleading variable name for the example of AUC calculation
### Describe the issue linked to the documentation
In the [example of AUC calculation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html), it was given that:
```python
import numpy as np
from sklearn import metrics
y = np.arra... | 29,823 | [
0.029222052544355392,
-0.03332842141389847,
0.012006174772977829,
-0.01602700725197792,
0.04683418944478035,
0.006855114828795195,
0.09277678281068802,
-0.02204846777021885,
-0.010316700674593449,
0.000888465263415128,
0.04292157664895058,
0.014332101680338383,
0.034751277416944504,
0.0137... |
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