html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
|---|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/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 | [
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0.04927639290690422,
0.04686814546585083,
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0.08406500518321991,
0.025846442207694054,
0.06578... |
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 | [
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0.05522239953279495,
0.024692503735423088,
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0.023... |
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 | [
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0.026385625824332237,
0.02970469929277897,
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0.002545031486079097,
0... |
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 | [
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0.019905714318156242,
0.0380866639316082,
-0.036714714020490646,
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0.007383361458778381,
0.067... |
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 | [
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0.038668472319841385,
0.03632340952754021,
-0.026574527844786644,
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0.03140903264284134,
0.011573481373488903,
0.08116744458675385,
0.0012363988207653165,
0.067... |
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 | [
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0.037027567625045776,
0.036632239818573,
-0.02535562589764595,
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0.06989... |
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 | [
-0.06289892643690109,
0.022151276469230652,
0.045368872582912445,
-0.015692252665758133,
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0.07696305215358734,
0.007622804492712021,
0.06... |
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 | [
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0.028... |
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 | [
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0.044092994183301926,
0.02... |
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 | [
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0.0... |
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 | [
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0.00206... |
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 | [
0.028583955019712448,
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0.016339773312211037,
0.0... |
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 | [
0.03390396013855934,
0.021994508802890778,
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0.02387131191790104,
-0.000... |
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 | [
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0.00020378944464027882,
0.07253845036029816,
0... |
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 | [
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0.06725842505693436,
0.0027844... |
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 | [
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0.02990606240928173,
0.025441... |
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 | [
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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 | [
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0.0... |
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 | [
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0.0023802071809768677,
0.009817423298954964,
0.0... |
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 | [
0.045708950608968735,
0.029758421704173088,
0.018281465396285057,
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0.0807073712348938,
0.0023802071809768677,
0.009817423298954964,
0.0... |
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 | [
0.045708950608968735,
0.029758421704173088,
0.018281465396285057,
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0.009817423298954964,
0.0... |
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 | [
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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 | [
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0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.03605879470705986,
0.04377974942326546,
0.0023337057791650295,
-0.0008659716695547104,
0.008826288394629955,
0.006123863160610199,
0.04454802721738815,
0.0002164226898457855,
0.028561290353536606,
-0.010229595936834812,
0.019535817205905914,
0.012940444052219391,
0.005507840774953365,
0... |
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 | [
-0.018611356616020203,
0.0023937863297760487,
0.025976896286010742,
0.04982197284698486,
0.041651401668787,
-0.019657175987958908,
0.05985555052757263,
0.04568137228488922,
0.004288980271667242,
0.017850404605269432,
0.029475992545485497,
0.006566293071955442,
0.02584526687860489,
0.011632... |
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 | [
0.009703310206532478,
0.06388305872678757,
0.0447576530277729,
0.050292640924453735,
0.06076805666089058,
-0.025567563250660896,
0.039969995617866516,
-0.010131248272955418,
-0.009688829071819782,
-0.0018723112298175693,
0.017704728990793228,
0.029367605224251747,
0.03322497755289078,
0.02... |
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 | [
0.009703310206532478,
0.06388305872678757,
0.0447576530277729,
0.050292640924453735,
0.06076805666089058,
-0.025567563250660896,
0.039969995617866516,
-0.010131248272955418,
-0.009688829071819782,
-0.0018723112298175693,
0.017704728990793228,
0.029367605224251747,
0.03322497755289078,
0.02... |
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 | [
0.009703310206532478,
0.06388305872678757,
0.0447576530277729,
0.050292640924453735,
0.06076805666089058,
-0.025567563250660896,
0.039969995617866516,
-0.010131248272955418,
-0.009688829071819782,
-0.0018723112298175693,
0.017704728990793228,
0.029367605224251747,
0.03322497755289078,
0.02... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.06297988444566727,
0.024316605180501938,
0.036015164107084274,
-0.004842769354581833,
0.07833694666624069,
-0.008270367048680782,
0.021981975063681602,
-0.019748464226722717,
0.028873378410935402,
-0.0013728253543376923,
0.0686909630894661,
0.0034700213000178337,
0.00787894893437624,
0.... |
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 | [
-0.00552482670173049,
0.02328256145119667,
-0.0004300804575905204,
0.011895301751792431,
-0.03402835503220558,
-0.010764033533632755,
0.016224544495344162,
0.00426300847902894,
-0.05360081419348717,
0.005885094404220581,
0.0861254632472992,
0.009647628292441368,
-0.032695699483156204,
0.02... |
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 | [
-0.010199520736932755,
0.02341196872293949,
0.0005716175655834377,
-0.0097396494820714,
-0.014262384735047817,
-0.017010832205414772,
-0.00543917017057538,
0.012419815175235271,
-0.035806912928819656,
0.05265019088983536,
0.09472105652093887,
0.0466039665043354,
-0.05827151983976364,
0.043... |
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 | [
-0.009176012128591537,
-0.0020920077804476023,
0.004542181733995676,
0.010192849673330784,
-0.02973831444978714,
-0.004585365764796734,
0.006108198780566454,
-0.009935778565704823,
-0.04845855385065079,
0.011106030084192753,
0.08481280505657196,
0.01416519470512867,
-0.038734473288059235,
... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
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0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
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-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
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0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
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0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
0.016057360917329788,
0.09102673083543777,
0.012085619382560253,
-0.024985017254948616,
-0.034014418721199036,
0.00698512326925993,
0.08567887544631958,
-0.00024362279509659857,
0.05232079699635506,
-0.0032494012266397476,
0.016208793967962265,
0.06901771575212479,
0.017591621726751328,
0.... |
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 | [
-0.034763216972351074,
0.03106374479830265,
0.038969025015830994,
-0.04225945845246315,
0.03230161964893341,
-0.030043406412005424,
-0.05273452028632164,
0.014587855897843838,
0.025346994400024414,
0.007746830116957426,
-0.06326048821210861,
-0.029353003948926926,
0.011062716133892536,
0.0... |
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 | [
-0.034763216972351074,
0.03106374479830265,
0.038969025015830994,
-0.04225945845246315,
0.03230161964893341,
-0.030043406412005424,
-0.05273452028632164,
0.014587855897843838,
0.025346994400024414,
0.007746830116957426,
-0.06326048821210861,
-0.029353003948926926,
0.011062716133892536,
0.0... |
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