html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
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https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
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https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
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0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
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0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
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0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
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0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
0.019359350204467773,
0.06806984543800354,
0.03385979309678078,
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0.002214323729276657,
0.02432011067867279,
0.09068141132593155,
-0.06009742617607117,
0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
0.019359350204467773,
0.06806984543800354,
0.03385979309678078,
-0.030190477147698402,
0.020772483199834824,
0.009270502254366875,
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0.043439581990242004,
0.002214323729276657,
0.02432011067867279,
0.09068141132593155,
-0.06009742617607117,
0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31750 | [
"New Feature",
"Needs Decision"
] | Full Python/sklearn Adaptation of py-earth
### Describe the workflow you want to enable
A full Python (not c or cython) port of py-earth, an archived sklearn project.
### Describe your proposed solution
- MARS regression is a great and really practical technique.
- py-earth implemented this, based in the R earth li... | 31,750 | [
0.019359350204467773,
0.06806984543800354,
0.03385979309678078,
-0.030190477147698402,
0.020772483199834824,
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0.043439581990242004,
0.002214323729276657,
0.02432011067867279,
0.09068141132593155,
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0.127... |
https://github.com/scikit-learn/scikit-learn/issues/31738 | [
"Documentation"
] | Present parameters and attributes sorted alphabetically to make it easier to find them on the documentation pages.
### Describe the issue linked to the documentation
## Example
On documentation page https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html the parameters are listed o... | 31,738 | [
0.004188870079815388,
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-0.009671879932284355,
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0.048634257167577744,
0.03230061009526253,
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https://github.com/scikit-learn/scikit-learn/issues/31738 | [
"Documentation"
] | Present parameters and attributes sorted alphabetically to make it easier to find them on the documentation pages.
### Describe the issue linked to the documentation
## Example
On documentation page https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html the parameters are listed o... | 31,738 | [
0.015435985289514065,
0.011297122575342655,
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https://github.com/scikit-learn/scikit-learn/issues/31738 | [
"Documentation"
] | Present parameters and attributes sorted alphabetically to make it easier to find them on the documentation pages.
### Describe the issue linked to the documentation
## Example
On documentation page https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html the parameters are listed o... | 31,738 | [
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https://github.com/scikit-learn/scikit-learn/issues/31738 | [
"Documentation"
] | Present parameters and attributes sorted alphabetically to make it easier to find them on the documentation pages.
### Describe the issue linked to the documentation
## Example
On documentation page https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html the parameters are listed o... | 31,738 | [
0.011638178490102291,
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0.032516151666641235,
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https://github.com/scikit-learn/scikit-learn/issues/31733 | [
"New Feature",
"spam",
"Needs Triage"
] | Add More Data to the RidgeCV, LassoCV, and ElasticNetCV Path
### Describe the workflow you want to enable
Currently, the mse_path_ is available from the above models, which lets you inspect/plot the mse for all folds, alphas, and l1_ratios for elasticnet for instance. It would be very nice to record not only the mse ... | 31,733 | [
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0.04160924628376961,
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0.0642220601439476,
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0.0986403077840805,
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0.10251... |
https://github.com/scikit-learn/scikit-learn/issues/31731 | [
"Bug"
] | `scipy.minimize(method=’L-BFGS-B’)` deprecation warning for `iprint` and `disp` arguments
### Describe the bug
When upgrading to scipy 1.16, fitting a LogisticRegression raises a deprecation warning:
```
DeprecationWarning: scipy.optimize: The `disp` and `iprint` options of the L-BFGS-B solver are deprecated and wil... | 31,731 | [
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0.009470044635236263,
0.03120172955095768,
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-0.... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.06812398880720139,
0.06454872339963913,
-0.022120174020528793,
0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31728 | [
"New Feature",
"Developer API"
] | Making the extension contract stable through version upgrades
### Describe the workflow you want to enable
Currently, every time `scikit-learn` releases a new minor version - e.g., 1.5.0, 1.6.0, 1.7.0 - compliant extensions, e.g., custom transformers, classifiers, etc, break - specifically, referring to the API confo... | 31,728 | [
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0.029918545857071877,
-0.007149179931730032,
-0.04841398075222969,
0.02759765461087227,
0.02757098712027073,
-0.007314672693610191,
0.06812398880720139,
0.06454872339963913,
-0.022120174020528793,
0.02950... |
https://github.com/scikit-learn/scikit-learn/issues/31725 | [
"Documentation",
"spam",
"Needs Triage"
] | Confusion around coef_ and intercept_ for Polynomial Ridge Regression inside a Pipeline
### Describe the issue linked to the documentation
When using a Pipeline with PolynomialFeatures and Ridge, it's unclear in the documentation how to extract the actual model coefficients and intercept to reproduce the regression e... | 31,725 | [
-0.01550779677927494,
0.05355193838477135,
0.014539683237671852,
0.026277419179677963,
0.03389351814985275,
-0.020361848175525665,
0.09239261597394943,
-0.013616944663226604,
0.05529170110821724,
-0.021451953798532486,
0.07162024080753326,
0.045322220772504807,
0.026683218777179718,
0.0630... |
https://github.com/scikit-learn/scikit-learn/issues/31725 | [
"Documentation",
"spam",
"Needs Triage"
] | Confusion around coef_ and intercept_ for Polynomial Ridge Regression inside a Pipeline
### Describe the issue linked to the documentation
When using a Pipeline with PolynomialFeatures and Ridge, it's unclear in the documentation how to extract the actual model coefficients and intercept to reproduce the regression e... | 31,725 | [
-0.01550779677927494,
0.05355193838477135,
0.014539683237671852,
0.026277419179677963,
0.03389351814985275,
-0.020361848175525665,
0.09239261597394943,
-0.013616944663226604,
0.05529170110821724,
-0.021451953798532486,
0.07162024080753326,
0.045322220772504807,
0.026683218777179718,
0.0630... |
https://github.com/scikit-learn/scikit-learn/issues/31725 | [
"Documentation",
"spam",
"Needs Triage"
] | Confusion around coef_ and intercept_ for Polynomial Ridge Regression inside a Pipeline
### Describe the issue linked to the documentation
When using a Pipeline with PolynomialFeatures and Ridge, it's unclear in the documentation how to extract the actual model coefficients and intercept to reproduce the regression e... | 31,725 | [
-0.01550779677927494,
0.05355193838477135,
0.014539683237671852,
0.026277419179677963,
0.03389351814985275,
-0.020361848175525665,
0.09239261597394943,
-0.013616944663226604,
0.05529170110821724,
-0.021451953798532486,
0.07162024080753326,
0.045322220772504807,
0.026683218777179718,
0.0630... |
https://github.com/scikit-learn/scikit-learn/issues/31724 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jul 09, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=78075&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jul 09, 2025)
- Test Collection Failure
COMMENT:
##... | 31,724 | [
-0.007903833873569965,
0.04269654303789139,
-0.022133462131023407,
-0.03157808631658554,
0.038466423749923706,
0.006154938600957394,
0.03765834867954254,
0.04834814742207527,
-0.024942275136709213,
0.026590796187520027,
0.04547235742211342,
0.03517329320311546,
-0.005295731592923403,
0.093... |
https://github.com/scikit-learn/scikit-learn/issues/31722 | [
"Bug",
"Hard",
"module:svm",
"Needs Reproducible Code"
] | `test_unsorted_indices` for `SVC` may fail randomly with sparse vs dense data
### Describe the bug
The [<code>test_unsorted_indices</code>](https://github.com/scikit-learn/scikit-learn/blob/cfd5f7833dfb3794e711e79e4a3373e599d5a1f0/sklearn/svm/tests/test_sparse.py#L121) function occasionally fails on CI when comparing... | 31,722 | [
-0.01626337133347988,
-0.00742170587182045,
0.015357641503214836,
0.016510603949427605,
0.09031537920236588,
-0.00435130437836051,
0.03166753426194191,
0.056224629282951355,
-0.0037262269761413336,
0.03682256117463112,
0.08503251522779465,
-0.013520026579499245,
0.03656259924173355,
0.0153... |
https://github.com/scikit-learn/scikit-learn/issues/31722 | [
"Bug",
"Hard",
"module:svm",
"Needs Reproducible Code"
] | `test_unsorted_indices` for `SVC` may fail randomly with sparse vs dense data
### Describe the bug
The [<code>test_unsorted_indices</code>](https://github.com/scikit-learn/scikit-learn/blob/cfd5f7833dfb3794e711e79e4a3373e599d5a1f0/sklearn/svm/tests/test_sparse.py#L121) function occasionally fails on CI when comparing... | 31,722 | [
-0.01626337133347988,
-0.00742170587182045,
0.015357641503214836,
0.016510603949427605,
0.09031537920236588,
-0.00435130437836051,
0.03166753426194191,
0.056224629282951355,
-0.0037262269761413336,
0.03682256117463112,
0.08503251522779465,
-0.013520026579499245,
0.03656259924173355,
0.0153... |
https://github.com/scikit-learn/scikit-learn/issues/31722 | [
"Bug",
"Hard",
"module:svm",
"Needs Reproducible Code"
] | `test_unsorted_indices` for `SVC` may fail randomly with sparse vs dense data
### Describe the bug
The [<code>test_unsorted_indices</code>](https://github.com/scikit-learn/scikit-learn/blob/cfd5f7833dfb3794e711e79e4a3373e599d5a1f0/sklearn/svm/tests/test_sparse.py#L121) function occasionally fails on CI when comparing... | 31,722 | [
-0.01626337133347988,
-0.00742170587182045,
0.015357641503214836,
0.016510603949427605,
0.09031537920236588,
-0.00435130437836051,
0.03166753426194191,
0.056224629282951355,
-0.0037262269761413336,
0.03682256117463112,
0.08503251522779465,
-0.013520026579499245,
0.03656259924173355,
0.0153... |
https://github.com/scikit-learn/scikit-learn/issues/31719 | [
"Documentation"
] | What are the coefficients returned by Polynomial Ridge Regression (or any regression)?
### Describe the issue linked to the documentation
I asked and answered a question about Regression in [Stack Overflow](https://stackoverflow.com/questions/79691953/ridge-polynomial-regression-how-to-get-parameters-for-equation-fou... | 31,719 | [
-0.006422588601708412,
-0.007066373247653246,
0.01735486276447773,
0.059861235320568085,
0.06730794161558151,
-0.024097047746181488,
0.00047298066783696413,
0.0044706896878778934,
0.0006845380994491279,
-0.027725720778107643,
0.034082263708114624,
0.10300140827894211,
0.028962425887584686,
... |
https://github.com/scikit-learn/scikit-learn/issues/31719 | [
"Documentation"
] | What are the coefficients returned by Polynomial Ridge Regression (or any regression)?
### Describe the issue linked to the documentation
I asked and answered a question about Regression in [Stack Overflow](https://stackoverflow.com/questions/79691953/ridge-polynomial-regression-how-to-get-parameters-for-equation-fou... | 31,719 | [
-0.006422588601708412,
-0.007066373247653246,
0.01735486276447773,
0.059861235320568085,
0.06730794161558151,
-0.024097047746181488,
0.00047298066783696413,
0.0044706896878778934,
0.0006845380994491279,
-0.027725720778107643,
0.034082263708114624,
0.10300140827894211,
0.028962425887584686,
... |
https://github.com/scikit-learn/scikit-learn/issues/31719 | [
"Documentation"
] | What are the coefficients returned by Polynomial Ridge Regression (or any regression)?
### Describe the issue linked to the documentation
I asked and answered a question about Regression in [Stack Overflow](https://stackoverflow.com/questions/79691953/ridge-polynomial-regression-how-to-get-parameters-for-equation-fou... | 31,719 | [
-0.006422588601708412,
-0.007066373247653246,
0.01735486276447773,
0.059861235320568085,
0.06730794161558151,
-0.024097047746181488,
0.00047298066783696413,
0.0044706896878778934,
0.0006845380994491279,
-0.027725720778107643,
0.034082263708114624,
0.10300140827894211,
0.028962425887584686,
... |
https://github.com/scikit-learn/scikit-learn/issues/31719 | [
"Documentation"
] | What are the coefficients returned by Polynomial Ridge Regression (or any regression)?
### Describe the issue linked to the documentation
I asked and answered a question about Regression in [Stack Overflow](https://stackoverflow.com/questions/79691953/ridge-polynomial-regression-how-to-get-parameters-for-equation-fou... | 31,719 | [
-0.006422588601708412,
-0.007066373247653246,
0.01735486276447773,
0.059861235320568085,
0.06730794161558151,
-0.024097047746181488,
0.00047298066783696413,
0.0044706896878778934,
0.0006845380994491279,
-0.027725720778107643,
0.034082263708114624,
0.10300140827894211,
0.028962425887584686,
... |
https://github.com/scikit-learn/scikit-learn/issues/31719 | [
"Documentation"
] | What are the coefficients returned by Polynomial Ridge Regression (or any regression)?
### Describe the issue linked to the documentation
I asked and answered a question about Regression in [Stack Overflow](https://stackoverflow.com/questions/79691953/ridge-polynomial-regression-how-to-get-parameters-for-equation-fou... | 31,719 | [
-0.006422588601708412,
-0.007066373247653246,
0.01735486276447773,
0.059861235320568085,
0.06730794161558151,
-0.024097047746181488,
0.00047298066783696413,
0.0044706896878778934,
0.0006845380994491279,
-0.027725720778107643,
0.034082263708114624,
0.10300140827894211,
0.028962425887584686,
... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31717 | [
"Bug"
] | SimpleImputer fails in "most_frequent" if incomparable types only if ties
### Describe the bug
### Observed behavior
When using the "most_frequent" strategy from SimpleImputer and there is a tie, the code takes the minimum values among all ties. This crashes if the values are not comparable such as `str` and `NoneTy... | 31,717 | [
-0.0072692581452429295,
-0.022376205772161484,
0.020042598247528076,
-0.02427620440721512,
0.05740689858794212,
-0.0285838283598423,
0.016650304198265076,
0.03277598321437836,
0.03892960026860237,
-0.02995992638170719,
0.023037418723106384,
0.043901458382606506,
0.04282558336853981,
-0.000... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31708 | [
"New Feature",
"Needs Decision - Include Feature"
] | Frisch-Newton Interior Point Solver for Quantile Regression
### Describe the workflow you want to enable
Hi @ scikit-learn devs!
Over at [pyfixest](https://github.com/py-econometrics/pyfixest), we have implemented a Frisch-Newton Interior Point solver to fit quantile regressions. The algorithm goes back to work fro... | 31,708 | [
-0.06322792917490005,
0.02424051985144615,
0.007488572038710117,
-0.009648561477661133,
0.01936151832342148,
-0.04897307977080345,
-0.025923386216163635,
0.038278620690107346,
-0.012305854819715023,
0.019727014005184174,
0.017614904791116714,
0.0046730609610676765,
-0.010763746686279774,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31705 | [
"Documentation"
] | EmpiricalCovariance user guide assume_centered tip incorrect
### Describe the issue linked to the documentation
The [user guide documentation](https://scikit-learn.org/stable/modules/covariance.html#empirical-covariance) for EmpiricalCovariance currently states:
> More precisely, if `assume_centered=False`, then the... | 31,705 | [
-0.0039048409089446068,
-0.030494602397084236,
0.007130290847271681,
-0.012466764077544212,
0.007863268256187439,
0.00084473448805511,
0.06811177730560303,
-0.01695168949663639,
0.0007779254810884595,
0.01570366509258747,
0.0684916079044342,
-0.0034897378645837307,
0.0329083614051342,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31705 | [
"Documentation"
] | EmpiricalCovariance user guide assume_centered tip incorrect
### Describe the issue linked to the documentation
The [user guide documentation](https://scikit-learn.org/stable/modules/covariance.html#empirical-covariance) for EmpiricalCovariance currently states:
> More precisely, if `assume_centered=False`, then the... | 31,705 | [
-0.009836400859057903,
-0.051573239266872406,
0.006733997259289026,
-0.010079877451062202,
0.010768121108412743,
0.004033944103866816,
0.07230991870164871,
-0.015473879873752594,
0.012184061110019684,
0.02262001484632492,
0.05437081679701805,
0.004496541805565357,
0.04949607327580452,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31700 | [
"Bug"
] | Pipelines are permitted to have no steps and are displayed as fitted
### Describe the bug
Pipeline without defined steps is displayed in HTML as fitted.
### Steps/Code to Reproduce
```
from sklearn.pipeline import Pipeline
pipe = Pipeline([])
pipe
```
### Expected Results
Maybe empty list should not be ac... | 31,700 | [
-0.01187338400632143,
-0.02393040992319584,
-0.004869706463068724,
-0.003446700284257531,
0.08011350780725479,
-0.005569970700889826,
0.020883724093437195,
-0.007085633464157581,
0.08703160285949707,
0.009895396418869495,
0.042482078075408936,
0.05723641440272331,
0.0407608225941658,
0.053... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
https://github.com/scikit-learn/scikit-learn/issues/31679 | [
"RFC"
] | AI tools like Copilot Coding Agent don't know about / don't respect our Automated Contributions Policy
(I am creating an issue to a PR already opened (#31643), because there are many more ways to solve the problem.)
AI tools many people use to create PRs don't care about our [Automated Contributions Policy](https://s... | 31,679 | [
0.007694758474826813,
0.07915767282247543,
-0.013246247544884682,
-0.06592050194740295,
-0.021395811811089516,
-0.02479386329650879,
0.04883323237299919,
-0.004433215130120516,
-0.025824768468737602,
-0.015116183087229729,
0.031336087733507156,
0.07885514944791794,
-0.025618435814976692,
0... |
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