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/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30663 | [
"Documentation"
] | KNeighborsClassifier reports different nearest neighbors and decision boundary depending on sys.platform
### Describe the bug
Training a `KNeighborsClassifier` on the iris dataset produces output that seems to depend on the system architecture (Linux, Mac, Windows tested). The order of neighboring points returned by ... | 30,663 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30662 | [
"Performance",
"High Priority",
"module:ensemble"
] | HistGradientBoostingClassifier/Regressor 15x slowdown on small data problems compared to disabled OpenMP threading
This problem was first described as part of #14306, but I think it might make sense to open a dedicated issue for the particular problem of small data shapes.
The fundamental problem seems to be that the... | 30,662 |
https://github.com/scikit-learn/scikit-learn/issues/30655 | [
"Bug",
"Needs Triage"
] | 'super' object has no attribute '__sklearn_tags__'
COMMENT:
duplicate of https://github.com/scikit-learn/scikit-learn/issues/30542
It has been resolved in the `main` branch of `XGBoost` but the package has not been released yet. | 30,655 |
https://github.com/scikit-learn/scikit-learn/issues/30653 | [
"Documentation"
] | Update videos list with recent presentations
The [presentations.rst](https://github.com/scikit-learn/scikit-learn/blob/main/doc/presentations.rst) page has very old resources. The last video listed is from 2013, over 10 years ago.
There are updated videos on the playlists here:
https://www.youtube.com/@scikit-le... | 30,653 |
https://github.com/scikit-learn/scikit-learn/issues/30653 | [
"Documentation"
] | Update videos list with recent presentations
The [presentations.rst](https://github.com/scikit-learn/scikit-learn/blob/main/doc/presentations.rst) page has very old resources. The last video listed is from 2013, over 10 years ago.
There are updated videos on the playlists here:
https://www.youtube.com/@scikit-le... | 30,653 |
https://github.com/scikit-learn/scikit-learn/issues/30652 | [
"Bug"
] | Unconsistent FutureWarning when using `force_int_remainder_cols=True` in `ColumnTransformer`
### Describe the bug
Calling fit on a pipeline that includes a `ColumnTransformer` step with `remainder="passthrough"` and `force_int_remainder_cols=True` (the default value as in v1.6) raises a
`FutureWarning:
The format of... | 30,652 |
https://github.com/scikit-learn/scikit-learn/issues/30652 | [
"Bug"
] | Unconsistent FutureWarning when using `force_int_remainder_cols=True` in `ColumnTransformer`
### Describe the bug
Calling fit on a pipeline that includes a `ColumnTransformer` step with `remainder="passthrough"` and `force_int_remainder_cols=True` (the default value as in v1.6) raises a
`FutureWarning:
The format of... | 30,652 |
https://github.com/scikit-learn/scikit-learn/issues/30652 | [
"Bug"
] | Unconsistent FutureWarning when using `force_int_remainder_cols=True` in `ColumnTransformer`
### Describe the bug
Calling fit on a pipeline that includes a `ColumnTransformer` step with `remainder="passthrough"` and `force_int_remainder_cols=True` (the default value as in v1.6) raises a
`FutureWarning:
The format of... | 30,652 |
https://github.com/scikit-learn/scikit-learn/issues/30645 | [
"Needs Reproducible Code",
"OS:Windows"
] | sklearn.cluster KMeans creates a status heap memory corruption error 0xC0000374
I have Windows 11 Home 24.2 Python 3.12.8 PyCharm Community Edition 2024.3 venv with pip 24.3.1 Numpy 2.2.1 Scikit-learn 1.6.1 Scipy 1.15.1 threadpoolctl 3.5.0 joblib 1.4.2 and this code gives me the heap corruption error
Python installat... | 30,645 |
https://github.com/scikit-learn/scikit-learn/issues/30645 | [
"Needs Reproducible Code",
"OS:Windows"
] | sklearn.cluster KMeans creates a status heap memory corruption error 0xC0000374
I have Windows 11 Home 24.2 Python 3.12.8 PyCharm Community Edition 2024.3 venv with pip 24.3.1 Numpy 2.2.1 Scikit-learn 1.6.1 Scipy 1.15.1 threadpoolctl 3.5.0 joblib 1.4.2 and this code gives me the heap corruption error
Python installat... | 30,645 |
https://github.com/scikit-learn/scikit-learn/issues/30641 | [
"Documentation"
] | docs: TimeSeriesSplit
### Describe the issue linked to the documentation
In the [TSS](https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) documentation, it states that it `Provides train/test indices to split time series data samples that are observed at fixed time intervals`.... | 30,641 |
https://github.com/scikit-learn/scikit-learn/issues/30639 | [
"New Feature",
"Needs Decision - Close"
] | UnboundTransform implementing log and logit transforms
### Describe the workflow you want to enable
Most classifiers and regressors expected unbounded input. Bounded input typically comes in the forms (a, infty) and (a, b) with the important special cases (0, infty) for radii, counts, and other things that are alwa... | 30,639 |
https://github.com/scikit-learn/scikit-learn/issues/30639 | [
"New Feature",
"Needs Decision - Close"
] | UnboundTransform implementing log and logit transforms
### Describe the workflow you want to enable
Most classifiers and regressors expected unbounded input. Bounded input typically comes in the forms (a, infty) and (a, b) with the important special cases (0, infty) for radii, counts, and other things that are alwa... | 30,639 |
https://github.com/scikit-learn/scikit-learn/issues/30639 | [
"New Feature",
"Needs Decision - Close"
] | UnboundTransform implementing log and logit transforms
### Describe the workflow you want to enable
Most classifiers and regressors expected unbounded input. Bounded input typically comes in the forms (a, infty) and (a, b) with the important special cases (0, infty) for radii, counts, and other things that are alwa... | 30,639 |
https://github.com/scikit-learn/scikit-learn/issues/30639 | [
"New Feature",
"Needs Decision - Close"
] | UnboundTransform implementing log and logit transforms
### Describe the workflow you want to enable
Most classifiers and regressors expected unbounded input. Bounded input typically comes in the forms (a, infty) and (a, b) with the important special cases (0, infty) for radii, counts, and other things that are alwa... | 30,639 |
https://github.com/scikit-learn/scikit-learn/issues/30638 | [
"Documentation",
"RFC",
"Array API"
] | Documenting return array types
Since we are introducing Array API compatibility we are discussing that some functions (especially in the metrics section) would not return the input array type, but a numpy array.
How would we document that, so that users know what they get as a return type?
We have started to di... | 30,638 |
https://github.com/scikit-learn/scikit-learn/issues/30638 | [
"Documentation",
"RFC",
"Array API"
] | Documenting return array types
Since we are introducing Array API compatibility we are discussing that some functions (especially in the metrics section) would not return the input array type, but a numpy array.
How would we document that, so that users know what they get as a return type?
We have started to di... | 30,638 |
https://github.com/scikit-learn/scikit-learn/issues/30638 | [
"Documentation",
"RFC",
"Array API"
] | Documenting return array types
Since we are introducing Array API compatibility we are discussing that some functions (especially in the metrics section) would not return the input array type, but a numpy array.
How would we document that, so that users know what they get as a return type?
We have started to di... | 30,638 |
https://github.com/scikit-learn/scikit-learn/issues/30625 | [
"Bug",
"Regression"
] | scikit-learn 1.6: Elliptic Envelope Fails with More Features than Samples
### Describe the bug
When using the EllipticEnvelope class in scikit-learn 1.6, the model raises an error when the number of features exceeds the number of samples in the input dataset. This issue occurs even when the data is preprocessed (e.g.... | 30,625 |
https://github.com/scikit-learn/scikit-learn/issues/30625 | [
"Bug",
"Regression"
] | scikit-learn 1.6: Elliptic Envelope Fails with More Features than Samples
### Describe the bug
When using the EllipticEnvelope class in scikit-learn 1.6, the model raises an error when the number of features exceeds the number of samples in the input dataset. This issue occurs even when the data is preprocessed (e.g.... | 30,625 |
https://github.com/scikit-learn/scikit-learn/issues/30624 | [
"Bug",
"Needs Triage"
] | Inconsistency in shapes of `coef_` attributes between `LinearRegression` and `Ridge` when parameter `y` is 2D with `n_targets = 1`
### Describe the bug
This issue comes from my (possibly incorrect) understanding that `LinearRegression` and `Ridge` classes should handle the dimensions of the `X` and `y` parameters t... | 30,624 |
https://github.com/scikit-learn/scikit-learn/issues/30623 | [
"Documentation",
"Needs Triage"
] | Bad color choice in Prediction Intervals for Gradient Boosting Regression
### Describe the issue linked to the documentation
The first plot in the example [Prediction Intervals for Gradient Boosting Regression](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html#fitting-non-l... | 30,623 |
https://github.com/scikit-learn/scikit-learn/issues/30623 | [
"Documentation",
"Needs Triage"
] | Bad color choice in Prediction Intervals for Gradient Boosting Regression
### Describe the issue linked to the documentation
The first plot in the example [Prediction Intervals for Gradient Boosting Regression](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html#fitting-non-l... | 30,623 |
https://github.com/scikit-learn/scikit-learn/issues/30622 | [
"New Feature"
] | Validate estimators argument of VotingClassifier
### Describe the workflow you want to enable
`VotingClassifier` takes as input `estimators`, which is expected to be `list of (str, estimator) tuples`.
However, if one accidentially puts in a list of estimators instead of a list of `tuples(str, estim)` or a single ... | 30,622 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30621 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add links to examples from the docstrings and user guide
_TLDR: Meta-issue for new contributors to add links to the examples in helpful places of the rest of the docs._
<!--
NOTE FOR AUTOMATED TOOLS:
This issue requires human judgment, contextual understanding, and familiarity with scikit-learn’s documentation struct... | 30,621 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30615 | [
"Bug",
"Needs Investigation"
] | average_precision_score produces unexpected output when scoring a single sample
### Describe the bug
When using `average_precision_score` and scoring a single sample, the metric ignores `y_score` and will always produce a score of 1.0 if `y_true = [1]` and otherwise will return a score of 0. I would have expected tha... | 30,615 |
https://github.com/scikit-learn/scikit-learn/issues/30596 | [
"Documentation"
] | Improve user experience in the user guide - make it clear to users that images are clickable
### Describe the issue linked to the documentation
In the user guide, it's not very noticeable that it's possible to click on images which then leads users to the example in which the respective image is used and explained ... | 30,596 |
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