html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/23354 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_ICC.pylatest_conda_forge_mkl ⚠️
**CI Failed on [Linux_Nightly_ICC.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42057&view=logs&j=8628a494-79d0-53fa-274c-1b00464f7121)**
Unable to find junit file. Please see link for details.
COMMENT:
It... | 23,354 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23334 | [
"API",
"Needs Decision"
] | API to predict multiple quantiles at once
Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn only predict a conditional expectile E[Y|X], and some have a `return_std` opti... | 23,334 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23328 | [
"Easy",
"Documentation"
] | Consistent formulae for metrics in the user guide
### Describe the issue linked to the documentation
### Description
https://scikit-learn.org/stable/modules/model_evaluation.html lists the formulae of many metrics / scores. Most of them sum over a sample (incomplete list):
- accuracy
- multinomial / multiclass log... | 23,328 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23324 | [
"New Feature",
"Needs Decision - Include Feature"
] | Support for ordinal multi-classification
### Describe the workflow you want to enable
Encode response variable ordering with every scikit learn classifier according to the method introduced in this [frequently cited paper](https://link.springer.com/chapter/10.1007/3-540-44795-4_13).
### Describe your proposed so... | 23,324 |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 |
https://github.com/scikit-learn/scikit-learn/issues/23323 | [
"Bug",
"module:semi_supervised"
] | SelfTrainingClassifier on a Pipeline
### Describe the bug
SelfTrainingClassifier cannot be fit on text data even if the base_estimator parameter is an estimator that can accept text data (e.g. a pipeline with text preprocessing). In particular, it seems that SelfTrainingClassifier validates the data on the classifi... | 23,323 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23319 | [
"Bug",
"module:preprocessing"
] | Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)
### Describe the bug
When I use a power transformer with yeo-johnson method I get this warning in numpy:
`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encount... | 23,319 |
https://github.com/scikit-learn/scikit-learn/issues/23313 | [
"Bug",
"module:model_selection"
] | Problem with maximal `int` value in `train_test_split`
### Describe the bug
It seems that above ~10^9, ints are not accepted and `train_test_split` spits out some nonsense values ... This occurs only if I pass numpy arrays, with lists all seems to work fine.
I have been able to circumvent it by just dividing the v... | 23,313 |
https://github.com/scikit-learn/scikit-learn/issues/23313 | [
"Bug",
"module:model_selection"
] | Problem with maximal `int` value in `train_test_split`
### Describe the bug
It seems that above ~10^9, ints are not accepted and `train_test_split` spits out some nonsense values ... This occurs only if I pass numpy arrays, with lists all seems to work fine.
I have been able to circumvent it by just dividing the v... | 23,313 |
https://github.com/scikit-learn/scikit-learn/issues/23311 | [
"Needs Triage"
] | Spurious warning with DecisionBoundaryPlot
The following snippet will raise a warning regarding feature names
```python
# %%
from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
X = iris.data[["sepal width (cm)", "petal width (cm)"]]
y = iris.target
# %%
from sklearn.model_selection imp... | 23,311 |
https://github.com/scikit-learn/scikit-learn/issues/23311 | [
"Needs Triage"
] | Spurious warning with DecisionBoundaryPlot
The following snippet will raise a warning regarding feature names
```python
# %%
from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
X = iris.data[["sepal width (cm)", "petal width (cm)"]]
y = iris.target
# %%
from sklearn.model_selection imp... | 23,311 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23295 | [
"cython",
"Refactor"
] | Use `cimport numpy as cnp` in Cython files for NumPy C API
I propose using `cnp` to reference NumPy's C API in Cython files. This pattern is [adapted in pandas](https://github.com/pandas-dev/pandas/blob/422e92ab29ea279c95d212124d9ffe5988c34ab6/pandas/_libs/lib.pyx#L33-L35) and it looks reasonable. The idea is to use `... | 23,295 |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 |
https://github.com/scikit-learn/scikit-learn/issues/23288 | [
"module:feature_selection",
"Needs Decision - Include Feature"
] | Less greedy RFECV (1-S.E. Rule)
Current implementation of RFECV selects the number of features that achieve optimal score (e.g., accuracy, r2).
This can lead to a greedy behavior and i feel it would be useful to have a "One-standard-error" rule for selecting the number of features. This is feasible now that the `cv_r... | 23,288 |
https://github.com/scikit-learn/scikit-learn/issues/23287 | [
"Bug",
"Needs Triage"
] | randomized_svd uses all available cpus
### Describe the bug
When i run randomized_svd it uses all available cpus
### Steps/Code to Reproduce
```
from sklearn.utils.extmath import randomized_svd
a = np.random.rand(1000000,2000)
output = randomized_svd(a, n_components=20, flip_sign=True, n_iter=20, random... | 23,287 |
https://github.com/scikit-learn/scikit-learn/issues/23283 | [
"Bug",
"Needs Triage"
] | i need some help with this pyinstaller
### Describe the bug
when i do the download for pyinstaller it says ERROR
### Steps/Code to Reproduce
pip install pyinstaller
Collecting pyinstaller
Using cached pyinstaller-5.0.1-py3-none-win_amd64.whl (2.0 MB)
Requirement already satisfied: setuptools in c:\program file... | 23,283 |
https://github.com/scikit-learn/scikit-learn/issues/23280 | [
"module:covariance",
"Needs Investigation"
] | clarification on OAS estimator formula (sklearn.covariance.OAS) (mean instead of trace is used)
Dear sklearn experts,
I was comparing different shrinkage algorithms and when looking at sklearn implementation of the OAS estimator I found something strange in the definition of the shrinkage factor or at least not cle... | 23,280 |
https://github.com/scikit-learn/scikit-learn/issues/23277 | [
"Bug",
"module:feature_selection"
] | partial_fit from SelectFromModel doesn't validate the parameters
### Describe the bug
Bug discovered while reviewing #23271.
in `SelectFromModel`, the `partial_fit` method doesn't do any validation. It should do the same validation as the `fit`method. It should also set ``n_features_in_`` and co.
### Steps/Code to ... | 23,277 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23269 | [
"Bug"
] | AttributeError in Birch for StandardScaled values
I have run into this issue which shows up in a few older reported issues as well, but the current open is a bit different.
Using this [data](https://drive.google.com/file/d/1jX2Xu15MPJcSwPOb07bX7V456BJVE251/view?usp=sharing), and running this code:
```python
im... | 23,269 |
https://github.com/scikit-learn/scikit-learn/issues/23267 | [
"Bug"
] | Regression in `SelectFromModel` where `max_features_` does not exist with `prefit=True`.
### Describe the bug
While testing the RC in `xgboost`, there is a test failure due to a regression after introducing https://github.com/scikit-learn/scikit-learn/pull/22356.
I assume that we did not think about the case when ... | 23,267 |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 |
https://github.com/scikit-learn/scikit-learn/issues/23265 | [
"Bug",
"Needs Triage"
] | Sklearn python 3.7 failing on pip
### Describe the bug
Installation failed with python 3.7. found it on travis. Python other versions till 3.9 are working fine.
### Steps/Code to Reproduce
pip install sklearn
### Expected Results
Successful installtion
### Actual Results
Collecting sklearn
Downloading skle... | 23,265 |
https://github.com/scikit-learn/scikit-learn/issues/23262 | [
"Bug"
] | Randomized SVD benchmark is broken
While updating the call of `fetch_openml`, I saw that the following benchmark is broken:
https://github.com/scikit-learn/scikit-learn/blob/main/benchmarks/bench_plot_randomized_svd.py
We should probably solve the issues.
COMMENT:
I think the first thing to do is to use named par... | 23,262 |
https://github.com/scikit-learn/scikit-learn/issues/23257 | [
"New Feature",
"Needs Triage"
] | Add standard-deviation output to sklearn.ensemble.RandomForestRegressor
### Describe the workflow you want to enable
I think it would be awesome if the RF regressor returns the standard deviation (not only the mean) of the output of the different trees.
### Describe your proposed solution
This is not a de... | 23,257 |
https://github.com/scikit-learn/scikit-learn/issues/23257 | [
"New Feature",
"Needs Triage"
] | Add standard-deviation output to sklearn.ensemble.RandomForestRegressor
### Describe the workflow you want to enable
I think it would be awesome if the RF regressor returns the standard deviation (not only the mean) of the output of the different trees.
### Describe your proposed solution
This is not a de... | 23,257 |
https://github.com/scikit-learn/scikit-learn/issues/23254 | [
"Documentation"
] | RandomizedSearchCV verbose parameter description is not describing the verbosity levels.
### Describe the issue linked to the documentation
In the website of the RandomizedSearchCV the `verbose` parameter is not discussing the verbosity levels:
"verbose : int
Controls the verbosity: the higher, the more messages."
... | 23,254 |
https://github.com/scikit-learn/scikit-learn/issues/23254 | [
"Documentation"
] | RandomizedSearchCV verbose parameter description is not describing the verbosity levels.
### Describe the issue linked to the documentation
In the website of the RandomizedSearchCV the `verbose` parameter is not discussing the verbosity levels:
"verbose : int
Controls the verbosity: the higher, the more messages."
... | 23,254 |
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