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/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22482 | [
"Moderate",
"module:calibration"
] | Deprecate normalize parameter in `calibration_curve`
### Describe the workflow you want to enable
Similar to the behavior of `calibration_curve`, I would like to be able to set `CalibrationDisplay.from_predictions(normalize=True)`.
### Describe your proposed solution
Add a keyword argument `normalize` to `Calibrati... | 22,482 |
https://github.com/scikit-learn/scikit-learn/issues/22478 | [
"Bug"
] | DummyRegressor converts some params to NumPy after fit()
### Describe the bug
The parameters of the DummyRegressor get converted into NumPy-format after calling .fit(). This is disadvantageous if the parameters are to be extracted and written in a JSON format, e.g., to save configurations.
### Steps/Code to Reproduc... | 22,478 |
https://github.com/scikit-learn/scikit-learn/issues/22477 | [
"Documentation",
"Needs Triage"
] | Clarification around `cross_val_predict` cross validator.
### Describe the issue linked to the documentation
This is not necessarily true for [cross_val_predict](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html), right?:
_Each sample belongs to exactly one test set...... | 22,477 |
https://github.com/scikit-learn/scikit-learn/issues/22477 | [
"Documentation",
"Needs Triage"
] | Clarification around `cross_val_predict` cross validator.
### Describe the issue linked to the documentation
This is not necessarily true for [cross_val_predict](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html), right?:
_Each sample belongs to exactly one test set...... | 22,477 |
https://github.com/scikit-learn/scikit-learn/issues/22473 | [
"Documentation"
] | Model Persistence page is missing side navigation contents
### Describe the issue linked to the documentation
Under User Guide, when you click the Model Persistence page, the left parent navigation contents is missing.
Link: https://scikit-learn.org/stable/modules/model_persistence.html
I noticed the Model Persi... | 22,473 |
https://github.com/scikit-learn/scikit-learn/issues/22473 | [
"Documentation"
] | Model Persistence page is missing side navigation contents
### Describe the issue linked to the documentation
Under User Guide, when you click the Model Persistence page, the left parent navigation contents is missing.
Link: https://scikit-learn.org/stable/modules/model_persistence.html
I noticed the Model Persi... | 22,473 |
https://github.com/scikit-learn/scikit-learn/issues/22473 | [
"Documentation"
] | Model Persistence page is missing side navigation contents
### Describe the issue linked to the documentation
Under User Guide, when you click the Model Persistence page, the left parent navigation contents is missing.
Link: https://scikit-learn.org/stable/modules/model_persistence.html
I noticed the Model Persi... | 22,473 |
https://github.com/scikit-learn/scikit-learn/issues/22466 | [
"module:metrics"
] | The weighted average should be replaced with a weighted sum?
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/metrics/_regression.py#L454
COMMENT:
This was asked before in https://github.com/scikit-learn/scikit-learn/issues/8758. The comment there applies here too: ht... | 22,466 |
https://github.com/scikit-learn/scikit-learn/issues/22466 | [
"module:metrics"
] | The weighted average should be replaced with a weighted sum?
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/metrics/_regression.py#L454
COMMENT:
The minimum of the coded function is the same as the minimum of what is typically defined as mean square error, since, as... | 22,466 |
https://github.com/scikit-learn/scikit-learn/issues/22466 | [
"module:metrics"
] | The weighted average should be replaced with a weighted sum?
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/metrics/_regression.py#L454
COMMENT:
I am -1 for this change as well. Note that for consistency, such a change would affect not only `mean_squared_error` but ... | 22,466 |
https://github.com/scikit-learn/scikit-learn/issues/22466 | [
"module:metrics"
] | The weighted average should be replaced with a weighted sum?
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/metrics/_regression.py#L454
COMMENT:
Given the comments in https://github.com/scikit-learn/scikit-learn/issues/8758#issuecomment-294809889 and https://github.... | 22,466 |
https://github.com/scikit-learn/scikit-learn/issues/22463 | [
"Documentation"
] | Workflow Improvement/Clarification: Issue vs. PR
## Problem
~The scikit-learn [contribution docs](https://scikit-learn.org/stable/developers/contributing.html) do not address the relationship between issues and PRs at all~. While the docs _do_ indeed mention the [relationship](https://scikit-learn.org/stable/develope... | 22,463 |
https://github.com/scikit-learn/scikit-learn/issues/22463 | [
"Documentation"
] | Workflow Improvement/Clarification: Issue vs. PR
## Problem
~The scikit-learn [contribution docs](https://scikit-learn.org/stable/developers/contributing.html) do not address the relationship between issues and PRs at all~. While the docs _do_ indeed mention the [relationship](https://scikit-learn.org/stable/develope... | 22,463 |
https://github.com/scikit-learn/scikit-learn/issues/22463 | [
"Documentation"
] | Workflow Improvement/Clarification: Issue vs. PR
## Problem
~The scikit-learn [contribution docs](https://scikit-learn.org/stable/developers/contributing.html) do not address the relationship between issues and PRs at all~. While the docs _do_ indeed mention the [relationship](https://scikit-learn.org/stable/develope... | 22,463 |
https://github.com/scikit-learn/scikit-learn/issues/22463 | [
"Documentation"
] | Workflow Improvement/Clarification: Issue vs. PR
## Problem
~The scikit-learn [contribution docs](https://scikit-learn.org/stable/developers/contributing.html) do not address the relationship between issues and PRs at all~. While the docs _do_ indeed mention the [relationship](https://scikit-learn.org/stable/develope... | 22,463 |
https://github.com/scikit-learn/scikit-learn/issues/22455 | [
"Needs Triage"
] | add which solver was used in sklearn Ridge regression : add clf.get_params(solver)
### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html
solver{‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’
S... | 22,455 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22453 | [
"module:inspection",
"Needs Decision - Include Feature"
] | Sensitivity Analysis function
## Proposal
Add a Sensitivity Analysis (SA) function.
The function would compute _Sobol'_ indices [1,2]. Consider a function `f` with parameters `x1`, `x2` and `x3`. Hence `y=f(x1,x2,x3)`. We are interested to know which parameter has the most impact, in terms of variance, on the valu... | 22,453 |
https://github.com/scikit-learn/scikit-learn/issues/22446 | [
"Bug",
"module:gaussian_process"
] | `test_y_multioutput`in Gaussian Process is failing on Debian 32bit
### Describe the bug
On 32bit systems on debian the test `test_y_multioutput` is failing.
The test will probably be just skipped during the build, this is not urgent, but maybe something underlying multioutput and Gaussian Process is hidden here (s... | 22,446 |
https://github.com/scikit-learn/scikit-learn/issues/22445 | [
"Bug",
"Needs Triage"
] | Adding a White Kernel to GP Regressor makes predictions all 0
### Describe the bug
When using a White Kernel as part of multiple concatenated kernels, GP Regressors predictions get zeroed out and I have no idea what is causing this. WhiteKernel should be taking the variance into account
### Steps/Code to Reproduce
... | 22,445 |
https://github.com/scikit-learn/scikit-learn/issues/22445 | [
"Bug",
"Needs Triage"
] | Adding a White Kernel to GP Regressor makes predictions all 0
### Describe the bug
When using a White Kernel as part of multiple concatenated kernels, GP Regressors predictions get zeroed out and I have no idea what is causing this. WhiteKernel should be taking the variance into account
### Steps/Code to Reproduce
... | 22,445 |
https://github.com/scikit-learn/scikit-learn/issues/22445 | [
"Bug",
"Needs Triage"
] | Adding a White Kernel to GP Regressor makes predictions all 0
### Describe the bug
When using a White Kernel as part of multiple concatenated kernels, GP Regressors predictions get zeroed out and I have no idea what is causing this. WhiteKernel should be taking the variance into account
### Steps/Code to Reproduce
... | 22,445 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22442 | [
"Bug",
"module:preprocessing"
] | StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)
### Describe the bug
If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in a complex pipeline, an issue comes up if you ever fit these transfo... | 22,442 |
https://github.com/scikit-learn/scikit-learn/issues/22441 | [
"New Feature",
"module:multiclass"
] | Verbosity for OneVsRestClassifier
### Describe the workflow you want to enable
Hi, is it possible to add a verbose parameter to OneVsRestClassifier so that we can see what the model is currently doing?
### Describe your proposed solution
Add a parameter like
```
OneVsRestClassifier(XGBClassifier(n_jobs=-1, max... | 22,441 |
https://github.com/scikit-learn/scikit-learn/issues/22441 | [
"New Feature",
"module:multiclass"
] | Verbosity for OneVsRestClassifier
### Describe the workflow you want to enable
Hi, is it possible to add a verbose parameter to OneVsRestClassifier so that we can see what the model is currently doing?
### Describe your proposed solution
Add a parameter like
```
OneVsRestClassifier(XGBClassifier(n_jobs=-1, max... | 22,441 |
https://github.com/scikit-learn/scikit-learn/issues/22441 | [
"New Feature",
"module:multiclass"
] | Verbosity for OneVsRestClassifier
### Describe the workflow you want to enable
Hi, is it possible to add a verbose parameter to OneVsRestClassifier so that we can see what the model is currently doing?
### Describe your proposed solution
Add a parameter like
```
OneVsRestClassifier(XGBClassifier(n_jobs=-1, max... | 22,441 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22438 | [
"API",
"Performance"
] | Path for pluggable low-level computational routines
The goal of this issue is to discuss the design and prototype a way to register alternative implementations for core low level routines in scikit-learn, in particular to benefit from hardware optimized implementations (e.g. using GPUs efficiently).
## Motivation
... | 22,438 |
https://github.com/scikit-learn/scikit-learn/issues/22435 | [
"New Feature",
"module:ensemble"
] | FEA post-fit calibration option in HGBT
### Describe the workflow you want to enable
The histogram gradient boosted decision trees usually do not fulfil the so called *balance property* on the training data, i.e. `sum([proba]predictions) == sum(observations)`. A simple "post-fit" step could ensure this condition. Thi... | 22,435 |
https://github.com/scikit-learn/scikit-learn/issues/22435 | [
"New Feature",
"module:ensemble"
] | FEA post-fit calibration option in HGBT
### Describe the workflow you want to enable
The histogram gradient boosted decision trees usually do not fulfil the so called *balance property* on the training data, i.e. `sum([proba]predictions) == sum(observations)`. A simple "post-fit" step could ensure this condition. Thi... | 22,435 |
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