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/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
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0.033445071429014206,
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0.075... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
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0.064... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.023015161976218224,
0.031891245394945145,
0.057213447988033295,
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0.08352576941251755,
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0.024439798668026924,
0.009208355098962784,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
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0.06... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.038250625133514404,
0.03126049414277077,
0.04190906882286072,
-0.014520342461764812,
0.06371001899242401,
0.0020256221760064363,
0.01849217899143696,
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0.015419851988554,
-0.010067476890981197,
-0.05176619440317154,
0.070... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.04308106005191803,
0.028263486921787262,
0.036938201636075974,
-0.01348013523966074,
0.06995423138141632,
0.005287481006234884,
0.013011780567467213,
0.02006577141582966,
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0.00627778097987175,
-0.006181060336530209,
-0.048271335661411285,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.02218838781118393,
0.0292072631418705,
0.055369336158037186,
-0.010547706857323647,
0.07785045355558395,
0.009541093371808529,
0.028185147792100906,
0.021366290748119354,
0.007347810082137585,
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0.023180386051535606,
-0.005780692212283611,
-0.037550851702690125,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.03786492347717285,
0.051357682794332504,
0.04486750438809395,
-0.0005461652181111276,
0.06793073564767838,
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0.010518169030547142,
0.028774522244930267,
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0.016884714365005493,
-0.00966262724250555,
-0.05858556181192398,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29514 | [
"New Feature",
"Needs Triage"
] | Allow missing values in multioutput metrics
### Describe the workflow you want to enable
In many multioutput problems, for example in chemoinformatics, there are missing values in target values, because only some properties are actually measured. Currently, scikit-learn requires all values to be present, leading to a... | 29,514 | [
-0.025506149977445602,
0.07008074223995209,
0.050411876291036606,
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0.0677337720990181,
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0.03752092272043228,
0.011913190595805645,
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0.0558... |
https://github.com/scikit-learn/scikit-learn/issues/29509 | [
"Documentation",
"spam",
"Needs Triage"
] | An inconsistency between the document of `LogisticRegression` and code implementation
### Describe the issue linked to the documentation
Hi,
I may find a potential condition missing in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegre... | 29,509 | [
0.033390969038009644,
-0.016310838982462883,
-0.00438494561240077,
0.02407674677670002,
0.028647825121879578,
0.010176665149629116,
0.062409430742263794,
0.0032894539181143045,
0.004012136720120907,
-0.007921474054455757,
0.11226978898048401,
0.0001857574679888785,
-0.005025070626288652,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29509 | [
"Documentation",
"spam",
"Needs Triage"
] | An inconsistency between the document of `LogisticRegression` and code implementation
### Describe the issue linked to the documentation
Hi,
I may find a potential condition missing in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegre... | 29,509 | [
0.033390969038009644,
-0.016310838982462883,
-0.00438494561240077,
0.02407674677670002,
0.028647825121879578,
0.010176665149629116,
0.062409430742263794,
0.0032894539181143045,
0.004012136720120907,
-0.007921474054455757,
0.11226978898048401,
0.0001857574679888785,
-0.005025070626288652,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29509 | [
"Documentation",
"spam",
"Needs Triage"
] | An inconsistency between the document of `LogisticRegression` and code implementation
### Describe the issue linked to the documentation
Hi,
I may find a potential condition missing in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegre... | 29,509 | [
0.033390969038009644,
-0.016310838982462883,
-0.00438494561240077,
0.02407674677670002,
0.028647825121879578,
0.010176665149629116,
0.062409430742263794,
0.0032894539181143045,
0.004012136720120907,
-0.007921474054455757,
0.11226978898048401,
0.0001857574679888785,
-0.005025070626288652,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29508 | [
"New Feature"
] | Add "ensure_positive" to check_array for non-negative value validation
### Describe the workflow you want to enable
Adding an option to `ensure_positive` to the `sklearn.utils.validation.check_array` function.
Currently, to ensure that an input array contains only positive values `check_non_negative` is used. Most ... | 29,508 | [
-0.06597021222114563,
-0.0030048401094973087,
0.026406273245811462,
-0.04223038628697395,
0.042039934545755386,
-0.012449790723621845,
-0.009987861849367619,
-0.009274194948375225,
0.04124500975012779,
0.007524686865508556,
0.03344538435339928,
0.04024934023618698,
-0.024601992219686508,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29508 | [
"New Feature"
] | Add "ensure_positive" to check_array for non-negative value validation
### Describe the workflow you want to enable
Adding an option to `ensure_positive` to the `sklearn.utils.validation.check_array` function.
Currently, to ensure that an input array contains only positive values `check_non_negative` is used. Most ... | 29,508 | [
-0.06436669081449509,
0.0002587095950730145,
0.028054647147655487,
-0.040708329528570175,
0.04275089129805565,
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0.041453104466199875,
0.005567969288676977,
0.031718093901872635,
0.03921980410814285,
-0.023526014760136604,
... |
https://github.com/scikit-learn/scikit-learn/issues/29507 | [
"New Feature",
"Needs Triage"
] | In gaussian_process/kernels.py, the Tanimoto kernel would be welcome
### Describe the workflow you want to enable
Here is a formula:
x*y / (||x||^2 + ||y||^2 - x*Y)
### Describe your proposed solution
In the context of Gaussian Process Regression, maybe this should be multiplied by the variance,
so the formula ... | 29,507 | [
-0.008595364168286324,
0.08719900250434875,
0.018865147605538368,
0.027866872027516365,
0.014976863749325275,
-0.025702912360429764,
0.010860424488782883,
0.01480076089501381,
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0.004362774547189474,
0.01744501292705536,
0.030422989279031754,
-0.04187445342540741,
0.1014... |
https://github.com/scikit-learn/scikit-learn/issues/29507 | [
"New Feature",
"Needs Triage"
] | In gaussian_process/kernels.py, the Tanimoto kernel would be welcome
### Describe the workflow you want to enable
Here is a formula:
x*y / (||x||^2 + ||y||^2 - x*Y)
### Describe your proposed solution
In the context of Gaussian Process Regression, maybe this should be multiplied by the variance,
so the formula ... | 29,507 | [
-0.017381947487592697,
0.07948174327611923,
0.03414415568113327,
0.015707295387983322,
0.008711717091500759,
-0.030206378549337387,
0.0010717688128352165,
0.013153991661965847,
0.025325804948806763,
0.022445907816290855,
0.023981543257832527,
0.039091892540454865,
-0.029887473210692406,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29507 | [
"New Feature",
"Needs Triage"
] | In gaussian_process/kernels.py, the Tanimoto kernel would be welcome
### Describe the workflow you want to enable
Here is a formula:
x*y / (||x||^2 + ||y||^2 - x*Y)
### Describe your proposed solution
In the context of Gaussian Process Regression, maybe this should be multiplied by the variance,
so the formula ... | 29,507 | [
-0.026410892605781555,
0.10015951842069626,
0.03425201028585434,
0.028191763907670975,
0.014498482458293438,
-0.018116217106580734,
0.00290043861605227,
0.024124324321746826,
0.009491215460002422,
0.006892814300954342,
0.04047859087586403,
0.013464603573083878,
-0.03927678242325783,
0.0765... |
https://github.com/scikit-learn/scikit-learn/issues/29507 | [
"New Feature",
"Needs Triage"
] | In gaussian_process/kernels.py, the Tanimoto kernel would be welcome
### Describe the workflow you want to enable
Here is a formula:
x*y / (||x||^2 + ||y||^2 - x*Y)
### Describe your proposed solution
In the context of Gaussian Process Regression, maybe this should be multiplied by the variance,
so the formula ... | 29,507 | [
-0.020804796367883682,
0.09508911520242691,
0.023843033239245415,
0.031031256541609764,
0.018959317356348038,
-0.029669739305973053,
0.003978419117629528,
0.023710433393716812,
-0.0017821404617279768,
0.017242485657334328,
0.01912328228354454,
0.024583684280514717,
-0.04507625475525856,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29507 | [
"New Feature",
"Needs Triage"
] | In gaussian_process/kernels.py, the Tanimoto kernel would be welcome
### Describe the workflow you want to enable
Here is a formula:
x*y / (||x||^2 + ||y||^2 - x*Y)
### Describe your proposed solution
In the context of Gaussian Process Regression, maybe this should be multiplied by the variance,
so the formula ... | 29,507 | [
-0.01665499433875084,
0.09268251806497574,
0.03163691610097885,
0.024200905114412308,
0.027433661743998528,
-0.029268691316246986,
0.004025651607662439,
0.022665953263640404,
0.014722089283168316,
0.01151382178068161,
0.024817608296871185,
0.030359238386154175,
-0.03706120699644089,
0.0766... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29504 | [
"Bug",
"help wanted"
] | Get error "ValueError: Input contains NaN" when MLP regression model is exploding numerically and when early_stopping=True
### Describe the bug
Hello,
I was doing dummy tests with a very basic MLP regressor, and I got an error I was not expecting: "ValueError Input contains NaN".
Full traceback:
```
File ... | 29,504 | [
-0.029622478410601616,
0.02809600904583931,
0.04077726602554321,
-0.016684086993336678,
0.1342598795890808,
0.0046691386960446835,
0.032202232629060745,
0.045835889875888824,
-0.016064707189798355,
0.01668010652065277,
0.04141420125961304,
0.03318946808576584,
-0.008157414384186268,
0.0680... |
https://github.com/scikit-learn/scikit-learn/issues/29503 | [
"New Feature",
"module:tree",
"cython"
] | pruning trees
### Describe the workflow you want to enable
I would like a more general `prune_tree` function which would allow the user to specify criteria on pruning a DecisionTree _posthoc_, i.e. without refitting it.
Criteria could be minimum leaf samples per class, min variance, etc...
Ideally, this function co... | 29,503 | [
-0.02429213374853134,
0.008504603058099747,
-0.02434978261590004,
-0.01612275093793869,
-0.021828854456543922,
-0.05622590333223343,
-0.10916824638843536,
0.043549612164497375,
-0.07834220677614212,
-0.00937742181122303,
0.0313585065305233,
0.06773600727319717,
-0.003716174280270934,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29503 | [
"New Feature",
"module:tree",
"cython"
] | pruning trees
### Describe the workflow you want to enable
I would like a more general `prune_tree` function which would allow the user to specify criteria on pruning a DecisionTree _posthoc_, i.e. without refitting it.
Criteria could be minimum leaf samples per class, min variance, etc...
Ideally, this function co... | 29,503 | [
-0.019314780831336975,
0.007131625898182392,
-0.027140149846673012,
-0.022228475660085678,
-0.015035252086818218,
-0.061428505927324295,
-0.10965736955404282,
0.03821149468421936,
-0.08634745329618454,
-0.0016762296436354518,
0.019034793600440025,
0.05945255607366562,
-0.014854194596409798,
... |
https://github.com/scikit-learn/scikit-learn/issues/29498 | [
"Bug",
"Needs Triage"
] | I want to import kerasregressor I have tried this from scikeras.wrappers import KerasClassifier , but I am getting the error cannot import name '_deprecate_Xt_in_inverse_transform' from 'sklearn.utils.deprecation' (C:\Users\hp\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\deprecation.py)
###... | 29,498 | [
0.027049651369452477,
0.018305091187357903,
-0.006502831354737282,
-0.053485166281461716,
0.02085651271045208,
0.023178476840257645,
0.0332031287252903,
0.05014490336179733,
0.07906474173069,
-0.003659625304862857,
0.022080782800912857,
0.052421897649765015,
-0.011502491310238838,
0.072803... |
https://github.com/scikit-learn/scikit-learn/issues/29498 | [
"Bug",
"Needs Triage"
] | I want to import kerasregressor I have tried this from scikeras.wrappers import KerasClassifier , but I am getting the error cannot import name '_deprecate_Xt_in_inverse_transform' from 'sklearn.utils.deprecation' (C:\Users\hp\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\deprecation.py)
###... | 29,498 | [
0.027049651369452477,
0.018305091187357903,
-0.006502831354737282,
-0.053485166281461716,
0.02085651271045208,
0.023178476840257645,
0.0332031287252903,
0.05014490336179733,
0.07906474173069,
-0.003659625304862857,
0.022080782800912857,
0.052421897649765015,
-0.011502491310238838,
0.072803... |
https://github.com/scikit-learn/scikit-learn/issues/29497 | [
"Build / CI",
"RFC"
] | RFC Make creating a development environment easier
Our environment creating is becoming increasingly complicated. I re-thought of this in the context of https://github.com/scikit-learn/scikit-learn/pull/29012
xref: https://github.com/scikit-learn/scikit-learn/pull/29012#discussion_r1679046385
Quoting @lesteve :
... | 29,497 | [
0.014917351305484772,
0.023073647171258926,
-0.025388482958078384,
-0.04339006543159485,
-0.005327519960701466,
-0.03604155033826828,
0.05935871601104736,
-0.005854498129338026,
0.010900131426751614,
0.020822223275899887,
0.007377755828201771,
0.03297075256705284,
-0.025190377607941628,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29497 | [
"Build / CI",
"RFC"
] | RFC Make creating a development environment easier
Our environment creating is becoming increasingly complicated. I re-thought of this in the context of https://github.com/scikit-learn/scikit-learn/pull/29012
xref: https://github.com/scikit-learn/scikit-learn/pull/29012#discussion_r1679046385
Quoting @lesteve :
... | 29,497 | [
0.014917351305484772,
0.023073647171258926,
-0.025388482958078384,
-0.04339006543159485,
-0.005327519960701466,
-0.03604155033826828,
0.05935871601104736,
-0.005854498129338026,
0.010900131426751614,
0.020822223275899887,
0.007377755828201771,
0.03297075256705284,
-0.025190377607941628,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29497 | [
"Build / CI",
"RFC"
] | RFC Make creating a development environment easier
Our environment creating is becoming increasingly complicated. I re-thought of this in the context of https://github.com/scikit-learn/scikit-learn/pull/29012
xref: https://github.com/scikit-learn/scikit-learn/pull/29012#discussion_r1679046385
Quoting @lesteve :
... | 29,497 | [
0.014917351305484772,
0.023073647171258926,
-0.025388482958078384,
-0.04339006543159485,
-0.005327519960701466,
-0.03604155033826828,
0.05935871601104736,
-0.005854498129338026,
0.010900131426751614,
0.020822223275899887,
0.007377755828201771,
0.03297075256705284,
-0.025190377607941628,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29497 | [
"Build / CI",
"RFC"
] | RFC Make creating a development environment easier
Our environment creating is becoming increasingly complicated. I re-thought of this in the context of https://github.com/scikit-learn/scikit-learn/pull/29012
xref: https://github.com/scikit-learn/scikit-learn/pull/29012#discussion_r1679046385
Quoting @lesteve :
... | 29,497 | [
0.014917351305484772,
0.023073647171258926,
-0.025388482958078384,
-0.04339006543159485,
-0.005327519960701466,
-0.03604155033826828,
0.05935871601104736,
-0.005854498129338026,
0.010900131426751614,
0.020822223275899887,
0.007377755828201771,
0.03297075256705284,
-0.025190377607941628,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29495 | [
"Bug"
] | GroupKFold inconsistent under ties in group sizes.
### Describe the bug
Due to the use of argsort (a non stable sort withthout the stable parameter introduced in numpy 2.0), GroupKFold is not always reproducible when there are ties in group sizes.
### Steps/Code to Reproduce
You may need to run this on different ma... | 29,495 | [
-0.017569085583090782,
0.025762289762496948,
0.009553102776408195,
0.03339565172791481,
0.016198987141251564,
-0.01792476512491703,
0.00889018177986145,
0.024472391232848167,
0.021669870242476463,
-0.05325615033507347,
-0.015445321798324585,
0.019399184733629227,
0.04029682278633118,
0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29491 | [
"Bug",
"Needs Triage"
] | Intersphinx duplicate definition warning
### Describe the bug
When using intersphinx with the scikit-learn docs, the build warns about duplicate definitions:
```
loading intersphinx inventory 'sklearn' from https://scikit-learn.org/stable/objects.inv...
WARNING: inventory <https://scikit-learn.org/stable/> conta... | 29,491 | [
0.02668800763785839,
-0.042286213487386703,
-0.008472495712339878,
0.02119748294353485,
0.05361620709300041,
0.022641688585281372,
0.0669044703245163,
0.011561496183276176,
-0.019780568778514862,
-0.03759552910923958,
-0.020718300715088844,
0.022737974300980568,
0.016786905005574226,
0.021... |
https://github.com/scikit-learn/scikit-learn/issues/29491 | [
"Bug",
"Needs Triage"
] | Intersphinx duplicate definition warning
### Describe the bug
When using intersphinx with the scikit-learn docs, the build warns about duplicate definitions:
```
loading intersphinx inventory 'sklearn' from https://scikit-learn.org/stable/objects.inv...
WARNING: inventory <https://scikit-learn.org/stable/> conta... | 29,491 | [
0.03406854346394539,
-0.029076121747493744,
-0.011736913584172726,
0.018398813903331757,
0.055857136845588684,
0.026605090126395226,
0.052518147975206375,
0.01299979817122221,
-0.02053481712937355,
-0.037853896617889404,
-0.011480054818093777,
0.01668429560959339,
0.0134532880038023,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/29491 | [
"Bug",
"Needs Triage"
] | Intersphinx duplicate definition warning
### Describe the bug
When using intersphinx with the scikit-learn docs, the build warns about duplicate definitions:
```
loading intersphinx inventory 'sklearn' from https://scikit-learn.org/stable/objects.inv...
WARNING: inventory <https://scikit-learn.org/stable/> conta... | 29,491 | [
0.02975701354444027,
-0.047934118658304214,
-0.010081213898956776,
0.025141825899481773,
0.05333114415407181,
0.023357722908258438,
0.0649498999118805,
0.010163302533328533,
-0.01855631172657013,
-0.04028309881687164,
-0.019991794601082802,
0.020826678723096848,
0.018887251615524292,
0.013... |
https://github.com/scikit-learn/scikit-learn/issues/29487 | [
"Bug",
"Needs Triage"
] | Ignore "index"-columns of transformations in `polars.DataFrame` objects
### Describe the bug
I would like to be able to use a decorator in order to wrap `transform` methods. The wrapped methods should ignore all columns in a `polars.DataFrame` starting with a certain prefix, i.e. "index".
I need to preserve the... | 29,487 | [
0.010033860802650452,
0.05271636322140694,
0.028599748387932777,
0.0007204544963315129,
0.07385388016700745,
0.018653875216841698,
0.04778185859322548,
0.008956998586654663,
-0.01138150691986084,
-0.013419078662991524,
-0.016863752156496048,
0.06781407445669174,
0.01265649776905775,
0.0063... |
https://github.com/scikit-learn/scikit-learn/issues/29487 | [
"Bug",
"Needs Triage"
] | Ignore "index"-columns of transformations in `polars.DataFrame` objects
### Describe the bug
I would like to be able to use a decorator in order to wrap `transform` methods. The wrapped methods should ignore all columns in a `polars.DataFrame` starting with a certain prefix, i.e. "index".
I need to preserve the... | 29,487 | [
0.010033860802650452,
0.05271636322140694,
0.028599748387932777,
0.0007204544963315129,
0.07385388016700745,
0.018653875216841698,
0.04778185859322548,
0.008956998586654663,
-0.01138150691986084,
-0.013419078662991524,
-0.016863752156496048,
0.06781407445669174,
0.01265649776905775,
0.0063... |
https://github.com/scikit-learn/scikit-learn/issues/29480 | [
"Bug",
"Needs Triage"
] | Instantiate tunedthresholdCV on google collab failed
### Describe the bug
I updated scikit-learn version into google Collab and instantiate TunedThresholdClassifierCV by importing name 'TunedThresholdClassifierCV' from 'sklearn.model_selection' but I got this error
```python
ImportError: cannot import name... | 29,480 | [
-0.00929866824299097,
-0.01174665056169033,
0.008530844002962112,
-0.025940947234630585,
0.06504872441291809,
-0.008843846619129181,
0.030993230640888214,
0.022333813831210136,
0.029704609885811806,
-0.004866743925958872,
0.01915729232132435,
0.0842127799987793,
0.005273488350212574,
0.062... |
https://github.com/scikit-learn/scikit-learn/issues/29479 | [
"New Feature",
"Needs Triage"
] | Grid search with predetermined parameter order
### Describe the workflow you want to enable
I often have a pipeline with expensive preprocessing, and I tune hyperparameters both for preprocessing and downstream classifier. A toy example with PCA and logistic regression:
```
pipeline = Pipeline([
("pca", PCA(... | 29,479 | [
-0.020060626789927483,
0.0824735164642334,
-0.013810298405587673,
-0.050201475620269775,
0.03991220146417618,
-0.011503085494041443,
-0.017032314091920853,
-0.011973132379353046,
0.015736639499664307,
-0.03286956250667572,
0.05488297715783119,
0.03695430979132652,
0.009884729981422424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29479 | [
"New Feature",
"Needs Triage"
] | Grid search with predetermined parameter order
### Describe the workflow you want to enable
I often have a pipeline with expensive preprocessing, and I tune hyperparameters both for preprocessing and downstream classifier. A toy example with PCA and logistic regression:
```
pipeline = Pipeline([
("pca", PCA(... | 29,479 | [
-0.020060626789927483,
0.0824735164642334,
-0.013810298405587673,
-0.050201475620269775,
0.03991220146417618,
-0.011503085494041443,
-0.017032314091920853,
-0.011973132379353046,
0.015736639499664307,
-0.03286956250667572,
0.05488297715783119,
0.03695430979132652,
0.009884729981422424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29479 | [
"New Feature",
"Needs Triage"
] | Grid search with predetermined parameter order
### Describe the workflow you want to enable
I often have a pipeline with expensive preprocessing, and I tune hyperparameters both for preprocessing and downstream classifier. A toy example with PCA and logistic regression:
```
pipeline = Pipeline([
("pca", PCA(... | 29,479 | [
-0.020060626789927483,
0.0824735164642334,
-0.013810298405587673,
-0.050201475620269775,
0.03991220146417618,
-0.011503085494041443,
-0.017032314091920853,
-0.011973132379353046,
0.015736639499664307,
-0.03286956250667572,
0.05488297715783119,
0.03695430979132652,
0.009884729981422424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29479 | [
"New Feature",
"Needs Triage"
] | Grid search with predetermined parameter order
### Describe the workflow you want to enable
I often have a pipeline with expensive preprocessing, and I tune hyperparameters both for preprocessing and downstream classifier. A toy example with PCA and logistic regression:
```
pipeline = Pipeline([
("pca", PCA(... | 29,479 | [
-0.020060626789927483,
0.0824735164642334,
-0.013810298405587673,
-0.050201475620269775,
0.03991220146417618,
-0.011503085494041443,
-0.017032314091920853,
-0.011973132379353046,
0.015736639499664307,
-0.03286956250667572,
0.05488297715783119,
0.03695430979132652,
0.009884729981422424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29479 | [
"New Feature",
"Needs Triage"
] | Grid search with predetermined parameter order
### Describe the workflow you want to enable
I often have a pipeline with expensive preprocessing, and I tune hyperparameters both for preprocessing and downstream classifier. A toy example with PCA and logistic regression:
```
pipeline = Pipeline([
("pca", PCA(... | 29,479 | [
-0.020060626789927483,
0.0824735164642334,
-0.013810298405587673,
-0.050201475620269775,
0.03991220146417618,
-0.011503085494041443,
-0.017032314091920853,
-0.011973132379353046,
0.015736639499664307,
-0.03286956250667572,
0.05488297715783119,
0.03695430979132652,
0.009884729981422424,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29464 | [
"Documentation",
"spam"
] | Boundary value problem of `SequentialFeatureSelector` and a suggestion on the document
### Describe the issue linked to the documentation
Hi devs of scikit-learn,
I found a potential slight boundary value problem in [`sklearn.feature_selection.SequentialFeatureSelector`](https://github.com/scikit-learn/scikit-lear... | 29,464 | [
-0.0016177259385585785,
0.00822573620826006,
-0.017047883942723274,
-0.04180242866277695,
-0.0007805432542227209,
0.006375127471983433,
0.06765077263116837,
-0.016358375549316406,
0.002557500032708049,
-0.0048057083040475845,
0.0657060518860817,
0.018945593386888504,
0.04158663749694824,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29464 | [
"Documentation",
"spam"
] | Boundary value problem of `SequentialFeatureSelector` and a suggestion on the document
### Describe the issue linked to the documentation
Hi devs of scikit-learn,
I found a potential slight boundary value problem in [`sklearn.feature_selection.SequentialFeatureSelector`](https://github.com/scikit-learn/scikit-lear... | 29,464 | [
-0.0016177259385585785,
0.00822573620826006,
-0.017047883942723274,
-0.04180242866277695,
-0.0007805432542227209,
0.006375127471983433,
0.06765077263116837,
-0.016358375549316406,
0.002557500032708049,
-0.0048057083040475845,
0.0657060518860817,
0.018945593386888504,
0.04158663749694824,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29464 | [
"Documentation",
"spam"
] | Boundary value problem of `SequentialFeatureSelector` and a suggestion on the document
### Describe the issue linked to the documentation
Hi devs of scikit-learn,
I found a potential slight boundary value problem in [`sklearn.feature_selection.SequentialFeatureSelector`](https://github.com/scikit-learn/scikit-lear... | 29,464 | [
-0.0016177259385585785,
0.00822573620826006,
-0.017047883942723274,
-0.04180242866277695,
-0.0007805432542227209,
0.006375127471983433,
0.06765077263116837,
-0.016358375549316406,
0.002557500032708049,
-0.0048057083040475845,
0.0657060518860817,
0.018945593386888504,
0.04158663749694824,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29463 | [
"Documentation",
"spam"
] | DOC: Add missing solver in the doc of `LogisticRegressionCV`
### Describe the issue linked to the documentation
Hi,
I found a potential code-doc inconsistency issue in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#logistic... | 29,463 | [
0.030092937871813774,
0.05510029196739197,
0.018444404006004333,
-0.0015391702763736248,
0.03352107107639313,
0.021014589816331863,
0.07242221385240555,
0.034094128757715225,
0.01810939610004425,
-0.026335731148719788,
0.09815429151058197,
0.04657570272684097,
-0.02782883308827877,
-0.0204... |
https://github.com/scikit-learn/scikit-learn/issues/29463 | [
"Documentation",
"spam"
] | DOC: Add missing solver in the doc of `LogisticRegressionCV`
### Describe the issue linked to the documentation
Hi,
I found a potential code-doc inconsistency issue in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#logistic... | 29,463 | [
0.030065394937992096,
0.054222818464040756,
0.01817583106458187,
-0.0017785176169127226,
0.03361576050519943,
0.020894844084978104,
0.07276074588298798,
0.03436891734600067,
0.01883809082210064,
-0.027081402018666267,
0.09739340841770172,
0.04741683602333069,
-0.027963319793343544,
-0.0202... |
https://github.com/scikit-learn/scikit-learn/issues/29463 | [
"Documentation",
"spam"
] | DOC: Add missing solver in the doc of `LogisticRegressionCV`
### Describe the issue linked to the documentation
Hi,
I found a potential code-doc inconsistency issue in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#logistic... | 29,463 | [
0.03410167992115021,
0.06041229888796806,
0.020062560215592384,
-0.006500038783997297,
0.02376570738852024,
0.019353533163666725,
0.07665760815143585,
0.030256740748882294,
0.010475140996277332,
-0.030306726694107056,
0.08798848092556,
0.040690068155527115,
-0.02497914806008339,
-0.0272193... |
https://github.com/scikit-learn/scikit-learn/issues/29463 | [
"Documentation",
"spam"
] | DOC: Add missing solver in the doc of `LogisticRegressionCV`
### Describe the issue linked to the documentation
Hi,
I found a potential code-doc inconsistency issue in [`sklearn.linear_model.LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#logistic... | 29,463 | [
0.032664380967617035,
0.06318548321723938,
0.01568015106022358,
-0.004116869065910578,
0.02616993710398674,
0.02574554644525051,
0.08133232593536377,
0.034143321216106415,
0.02470145747065544,
-0.029658243060112,
0.08724738657474518,
0.05096571147441864,
-0.021432645618915558,
-0.015279991... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29459 | [
"module:tree",
"cython"
] | MAINT, RFC Simplify the Cython code in `sklearn/tree/` by splitting the "Splitter" and "Partitioner" code
### Summary of the problem
There are quite a number of GH issues with the label `tree` (https://github.com/scikit-learn/scikit-learn/issues?page=2&q=is%3Aopen+is%3Aissue+label%3Amodule%3Atree).
However, the co... | 29,459 | [
0.020416468381881714,
0.02020307071506977,
-0.028458835557103157,
0.032807786017656326,
-0.03505170717835426,
-0.013307983987033367,
0.02993130311369896,
0.006078833714127541,
0.00850035808980465,
-0.054888367652893066,
0.007832868956029415,
0.05044293403625488,
-0.03139060363173485,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/29457 | [
"good first issue",
"help wanted"
] | MAINT Remove scipy<1.6 specific code in QuantileRegressor and example
We don't need this code anymore since our minimum supported version is scipy 1.6:
https://github.com/scikit-learn/scikit-learn/blob/fa14001fa19a262c7eb43b2ef3c0d6b56b4c8fad/examples/linear_model/plot_quantile_regression.py#L112-L116
While we a... | 29,457 | [
0.009727977216243744,
0.05576673522591591,
-0.018279647454619408,
0.01934373378753662,
0.05700267106294632,
0.0334305502474308,
0.022908493876457214,
0.07941653579473495,
0.065770223736763,
-0.01030751597136259,
0.017722615972161293,
0.10673099756240845,
-0.01740141026675701,
0.05607773363... |
https://github.com/scikit-learn/scikit-learn/issues/29457 | [
"good first issue",
"help wanted"
] | MAINT Remove scipy<1.6 specific code in QuantileRegressor and example
We don't need this code anymore since our minimum supported version is scipy 1.6:
https://github.com/scikit-learn/scikit-learn/blob/fa14001fa19a262c7eb43b2ef3c0d6b56b4c8fad/examples/linear_model/plot_quantile_regression.py#L112-L116
While we a... | 29,457 | [
0.007757888175547123,
0.05881454050540924,
-0.01738012209534645,
0.0058101508766412735,
0.05499120429158211,
0.023433294147253036,
0.021419433876872063,
0.07114993035793304,
0.06831075251102448,
-0.00612748134881258,
0.02352321334183216,
0.09782123565673828,
-0.01913350448012352,
0.0582473... |
https://github.com/scikit-learn/scikit-learn/issues/29455 | [
"Bug",
"Needs Triage"
] | StackingRegressor doesn't take estimators created via make_pipeline
### Describe the bug
sklearn 1.5.0. Using the shortcut `make_pipeline` triggers an error while using the explicit `Pipeline` instantiation works (see code example)
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.ensemble impor... | 29,455 | [
-0.03566481173038483,
0.02436521276831627,
0.011999513953924179,
-0.03952544927597046,
0.07106897979974747,
0.006375250406563282,
0.0858062133193016,
-0.04595007374882698,
-0.0010306010954082012,
0.019283490255475044,
0.034997377544641495,
0.040389809757471085,
0.0071091721765697,
-0.00476... |
https://github.com/scikit-learn/scikit-learn/issues/29454 | [
"Bug",
"Needs Triage"
] | StackingRegressor.fit() doesn't support sample_weight when using Pipeline objects as estimators
### Describe the bug
sklearn 1.5.0. When using a Stacking model, fitting raises the following error when:
- `sample_weight` argument is passed, AND
- the individual estimators contain a Pipeline object instead of a singl... | 29,454 | [
-0.024751584976911545,
0.037871215492486954,
0.02425142377614975,
-0.026622401550412178,
0.08169357478618622,
-0.004383789375424385,
0.0707206204533577,
-0.016057735309004784,
0.021215301007032394,
0.018238984048366547,
0.03898903355002403,
0.016424547880887985,
0.022391891106963158,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29454 | [
"Bug",
"Needs Triage"
] | StackingRegressor.fit() doesn't support sample_weight when using Pipeline objects as estimators
### Describe the bug
sklearn 1.5.0. When using a Stacking model, fitting raises the following error when:
- `sample_weight` argument is passed, AND
- the individual estimators contain a Pipeline object instead of a singl... | 29,454 | [
-0.024751584976911545,
0.037871215492486954,
0.02425142377614975,
-0.026622401550412178,
0.08169357478618622,
-0.004383789375424385,
0.0707206204533577,
-0.016057735309004784,
0.021215301007032394,
0.018238984048366547,
0.03898903355002403,
0.016424547880887985,
0.022391891106963158,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29453 | [
"Documentation",
"Needs Triage"
] | Why 30 neighbors in 'Agglomerative clustering with and without structure'?
### Describe the issue linked to the documentation
This is not so much an issue as a request for explanation. I was going through the scikit-learn user guide on 'Agglomerative clustering with and without structure', which can be found at htt... | 29,453 | [
-0.05296899378299713,
-0.08923649042844772,
0.015484035946428776,
0.03553721308708191,
0.009016109630465508,
-0.001231218222528696,
0.07251197099685669,
0.007907670922577381,
0.0021049382630735636,
0.048849642276763916,
-0.014345754869282246,
0.015236499719321728,
0.04614589735865593,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29452 | [
"Bug",
"Array API"
] | Array API regression in `homogeneity_completeness_v_measure`?
### Describe the bug
When I enable Array API and run `homogeneity_completeness_v_measure`, an error is raised, since within it, a sparse matrix is passed into `mutual_info_score`, which already supports array API ([li. 530-531](https://github.com/scikit-... | 29,452 | [
-0.004649823065847158,
0.003995870240032673,
0.013747256249189377,
-0.006563646253198385,
0.04877056926488876,
0.0027344999834895134,
0.04777626320719719,
0.025262638926506042,
0.01819913275539875,
0.00274925259873271,
0.02025572583079338,
0.02678726427257061,
0.032867759466171265,
0.01992... |
https://github.com/scikit-learn/scikit-learn/issues/29452 | [
"Bug",
"Array API"
] | Array API regression in `homogeneity_completeness_v_measure`?
### Describe the bug
When I enable Array API and run `homogeneity_completeness_v_measure`, an error is raised, since within it, a sparse matrix is passed into `mutual_info_score`, which already supports array API ([li. 530-531](https://github.com/scikit-... | 29,452 | [
-0.004649823065847158,
0.003995870240032673,
0.013747256249189377,
-0.006563646253198385,
0.04877056926488876,
0.0027344999834895134,
0.04777626320719719,
0.025262638926506042,
0.01819913275539875,
0.00274925259873271,
0.02025572583079338,
0.02678726427257061,
0.032867759466171265,
0.01992... |
https://github.com/scikit-learn/scikit-learn/issues/29452 | [
"Bug",
"Array API"
] | Array API regression in `homogeneity_completeness_v_measure`?
### Describe the bug
When I enable Array API and run `homogeneity_completeness_v_measure`, an error is raised, since within it, a sparse matrix is passed into `mutual_info_score`, which already supports array API ([li. 530-531](https://github.com/scikit-... | 29,452 | [
-0.004649823065847158,
0.003995870240032673,
0.013747256249189377,
-0.006563646253198385,
0.04877056926488876,
0.0027344999834895134,
0.04777626320719719,
0.025262638926506042,
0.01819913275539875,
0.00274925259873271,
0.02025572583079338,
0.02678726427257061,
0.032867759466171265,
0.01992... |
https://github.com/scikit-learn/scikit-learn/issues/29452 | [
"Bug",
"Array API"
] | Array API regression in `homogeneity_completeness_v_measure`?
### Describe the bug
When I enable Array API and run `homogeneity_completeness_v_measure`, an error is raised, since within it, a sparse matrix is passed into `mutual_info_score`, which already supports array API ([li. 530-531](https://github.com/scikit-... | 29,452 | [
-0.004649823065847158,
0.003995870240032673,
0.013747256249189377,
-0.006563646253198385,
0.04877056926488876,
0.0027344999834895134,
0.04777626320719719,
0.025262638926506042,
0.01819913275539875,
0.00274925259873271,
0.02025572583079338,
0.02678726427257061,
0.032867759466171265,
0.01992... |
https://github.com/scikit-learn/scikit-learn/issues/29452 | [
"Bug",
"Array API"
] | Array API regression in `homogeneity_completeness_v_measure`?
### Describe the bug
When I enable Array API and run `homogeneity_completeness_v_measure`, an error is raised, since within it, a sparse matrix is passed into `mutual_info_score`, which already supports array API ([li. 530-531](https://github.com/scikit-... | 29,452 | [
-0.004649823065847158,
0.003995870240032673,
0.013747256249189377,
-0.006563646253198385,
0.04877056926488876,
0.0027344999834895134,
0.04777626320719719,
0.025262638926506042,
0.01819913275539875,
0.00274925259873271,
0.02025572583079338,
0.02678726427257061,
0.032867759466171265,
0.01992... |
https://github.com/scikit-learn/scikit-learn/issues/29443 | [
"Bug"
] | KernelDensity(bandwidth='silverman') doesn't throw proper error for 1d X
Essentially the bandwidth estimation codepath is not covered in the common tests, but it should be :)
COMMENT:
I assume that we have similar issue with every estimators that do not use default parameters. We should at least add this test in the ... | 29,443 | [
-0.05806383118033409,
0.01823391020298004,
0.010603368282318115,
0.014959035441279411,
-0.013156459666788578,
-0.07180727273225784,
0.013747367076575756,
0.015796063467860222,
-0.037050556391477585,
0.04641247168183327,
0.0658339187502861,
0.0022349064238369465,
0.03573670610785484,
0.0211... |
https://github.com/scikit-learn/scikit-learn/issues/29443 | [
"Bug"
] | KernelDensity(bandwidth='silverman') doesn't throw proper error for 1d X
Essentially the bandwidth estimation codepath is not covered in the common tests, but it should be :)
COMMENT:
/take | 29,443 | [
-0.059757381677627563,
0.0001316119742114097,
-0.011805771850049496,
0.030214039608836174,
-0.01254955306649208,
-0.08595216274261475,
0.006461569108068943,
0.0176085215061903,
-0.03388195484876633,
0.03867248818278313,
0.0580391027033329,
0.0024078695569187403,
0.03486339747905731,
0.0414... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29440 | [
"Documentation",
"spam"
] | Suggesting updates on the doc of `sklearn.neighbors.NeighborhoodComponentsAnalysis`
### Describe the issue linked to the documentation
Hi,
We are an academic team of software engineering researchers from a university working on automated program analysis techniques to improve API documentation quality, ultimately ... | 29,440 | [
0.016436615958809853,
0.07411638647317886,
-0.0064516314305365086,
-0.00674418406561017,
-0.002041921950876713,
-0.006472279783338308,
0.09940434992313385,
-0.06706155836582184,
0.006506797857582569,
-0.044247787445783615,
0.0581543855369091,
0.006317193619906902,
0.03740347549319267,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29439 | [
"Bug",
"Needs Triage"
] | Large errors when computing Euclidean pairwise distances
### Describe the bug
See the code below. Matrices `X` and `Y` here are the same. The function `cdist` here gives the correct answer. The "worst offender" is large diagonal entry at `incorrect_answer[2,2]`.
### Steps/Code to Reproduce
import numpy as np
from ... | 29,439 | [
-0.025001829490065575,
-0.0027521804440766573,
0.009387017227709293,
0.027136042714118958,
0.04133417084813118,
0.023743847385048866,
0.009056624956429005,
0.03242122754454613,
0.023272177204489708,
-0.028837425634264946,
0.000733868801034987,
0.027711967006325722,
0.010880078189074993,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29438 | [
"Documentation",
"Needs Triage"
] | Wrong number of features in documentation
### Describe the issue linked to the documentation
In the scikit learn web page, [ https://scikit-learn.org/stable/modules/decomposition.html#pca, section](https://scikit-learn.org/stable/modules/decomposition.html#exact-pca-and-probabilistic-interpretation), there is a wrong... | 29,438 | [
0.019442696124315262,
-0.00747540220618248,
-0.026372525840997696,
0.055730465799570084,
0.01740643009543419,
0.016530917957425117,
0.08420455455780029,
-0.022135574370622635,
-0.029216410592198372,
0.01711288094520569,
0.06728947907686234,
-0.004060384351760149,
0.05848383903503418,
0.049... |
https://github.com/scikit-learn/scikit-learn/issues/29434 | [
"Build / CI"
] | CI Unpin matplotlib<3.9 in doc build
In https://github.com/scikit-learn/scikit-learn/pull/29388 we pinned `matplotlib<3.9` see in particular https://github.com/scikit-learn/scikit-learn/pull/29388#discussion_r1668574040.
This is a DeprecationWarning in matplotlib 3.9 turned into error in the CI:
```
matplotlib.... | 29,434 | [
-0.00037098766188137233,
0.03724293410778046,
-0.017047055065631866,
-0.043851058930158615,
0.001594260334968567,
0.01981952413916588,
0.019150912761688232,
0.058209650218486786,
0.02258528769016266,
0.004831766709685326,
0.028383662924170494,
0.0918324887752533,
-0.055821117013692856,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29434 | [
"Build / CI"
] | CI Unpin matplotlib<3.9 in doc build
In https://github.com/scikit-learn/scikit-learn/pull/29388 we pinned `matplotlib<3.9` see in particular https://github.com/scikit-learn/scikit-learn/pull/29388#discussion_r1668574040.
This is a DeprecationWarning in matplotlib 3.9 turned into error in the CI:
```
matplotlib.... | 29,434 | [
-0.00037098766188137233,
0.03724293410778046,
-0.017047055065631866,
-0.043851058930158615,
0.001594260334968567,
0.01981952413916588,
0.019150912761688232,
0.058209650218486786,
0.02258528769016266,
0.004831766709685326,
0.028383662924170494,
0.0918324887752533,
-0.055821117013692856,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29434 | [
"Build / CI"
] | CI Unpin matplotlib<3.9 in doc build
In https://github.com/scikit-learn/scikit-learn/pull/29388 we pinned `matplotlib<3.9` see in particular https://github.com/scikit-learn/scikit-learn/pull/29388#discussion_r1668574040.
This is a DeprecationWarning in matplotlib 3.9 turned into error in the CI:
```
matplotlib.... | 29,434 | [
-0.00037098766188137233,
0.03724293410778046,
-0.017047055065631866,
-0.043851058930158615,
0.001594260334968567,
0.01981952413916588,
0.019150912761688232,
0.058209650218486786,
0.02258528769016266,
0.004831766709685326,
0.028383662924170494,
0.0918324887752533,
-0.055821117013692856,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29425 | [
"Documentation",
"Needs Triage"
] | Add weighting example to Color Quantization using K-Means page
### Describe the issue linked to the documentation
This is not an issue, but a request for addition.
The page I am referring to can be found at https://scikit-learn.org/stable/auto_examples/cluster/plot_color_quantization.html
I recently read tried out... | 29,425 | [
-0.011898449622094631,
-0.023543765768408775,
-0.03344688564538956,
0.028129898011684418,
0.02019197680056095,
0.004019850865006447,
0.04730645567178726,
0.014497672207653522,
-0.025957774370908737,
-0.014004090800881386,
0.0406985729932785,
0.045388493686914444,
0.0074339015409350395,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29424 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Jul 08, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/9833194137)** (Jul 08, 2024)
COMMENT:
Temporary issue with scipy-dev see https://github.com/scikit-learn/scikit-learn/pull/29428#issuecomment-2213522161 | 29,424 | [
-0.02342604100704193,
0.038072239607572556,
-0.019832734018564224,
-0.028386613354086876,
0.011705641634762287,
0.024024296551942825,
0.00897477101534605,
0.04439161717891693,
-0.03338014334440231,
0.01566973142325878,
0.08341383934020996,
0.04067729413509369,
-0.020971570163965225,
0.0877... |
https://github.com/scikit-learn/scikit-learn/issues/29423 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev (last failure: Jul 08, 2024) ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=68381&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jul 08, 2024)
- test_search_cv[HalvingGr... | 29,423 | [
-0.005104808136820793,
0.018912214785814285,
-0.020784452557563782,
-0.02370040863752365,
0.052583519369363785,
-0.0018092566169798374,
0.008837668225169182,
0.06231491640210152,
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0.023493101820349693,
0.06306495517492294,
0.0535426139831543,
-0.007468438241630793,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29422 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_pip_free_threaded (last failure: Jul 08, 2024) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_pip_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=68381&view=logs&j=8bc43b48-889f-54b9-cd8b-781ee8447bf2)** (Jul 08, 2024)
- test... | 29,422 | [
-0.021082619205117226,
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0.05047916993498802,
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0.0018654816085472703,
0.060038093477487564,
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0.02962702326476574,
0.05668650195002556,
0.05696718394756317,
-0.007315387018024921,
0... |
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