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/24462 | [
"New Feature",
"module:tree",
"Needs Decision - Include Feature"
] | Implement p-value splitting criterion for Decision Trees
### Describe the workflow you want to enable
The current list of valid criterions for Decision Trees are:
{“squared_error”, “friedman_mse”, “absolute_error”, “poisson”}
With regard to regression problems, I have run into numerous situations where I would ... | 24,462 | [
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https://github.com/scikit-learn/scikit-learn/issues/24458 | [
"Needs Triage"
] | Please change Confusion Matrix Heatmap cmap.
The current **cmap** is `viridis` is un-readable. This really needs to be changed to a much better, and readable cmap.
COMMENT:
When using `metrics.ConfusionMatrixDisplay` you can directly set the `cmap` to whatever you prefer through the `cmap` parameter passed to any of ... | 24,458 | [
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https://github.com/scikit-learn/scikit-learn/issues/24458 | [
"Needs Triage"
] | Please change Confusion Matrix Heatmap cmap.
The current **cmap** is `viridis` is un-readable. This really needs to be changed to a much better, and readable cmap.
COMMENT:
Absolutely. I'm glad you checked it out! | 24,458 | [
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0.028046270832419395,
-0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24449 | [
"Bug",
"Needs Triage"
] | The loss squared_error is not supported for SGDRegressor
### Describe the bug
Hi I'm using SGDRegressor for OLS regression but encountered this error:
ValueError: The loss squared_error is not supported.
### Steps/Code to Reproduce
from sklearn.linear_model import SGDRegressor
clf = SGDRegressor(loss="squared_... | 24,449 | [
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0.0243... |
https://github.com/scikit-learn/scikit-learn/issues/24434 | [
"New Feature",
"Needs Triage"
] | Different dataset for different models when using StackingClassifier
### Describe the workflow you want to enable
Since stacking is a combination of different models, the different models might have different features i.e is is rather often that the same dataset does not fit different models (or it is a sub-optimal... | 24,434 | [
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0.014647... |
https://github.com/scikit-learn/scikit-learn/issues/24432 | [
"New Feature"
] | StackingRegressor does not allow classifiers as estimators (and vice versa)
### Describe the workflow you want to enable
I'm facing an ordinal regression problem. In these kind of problems, both a classifier or a regressor can be used as a naive approach.
I'm trying to stack both of them, however sklearn does not ... | 24,432 | [
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-0.010060136206448078,
-0.00720... |
https://github.com/scikit-learn/scikit-learn/issues/24432 | [
"New Feature"
] | StackingRegressor does not allow classifiers as estimators (and vice versa)
### Describe the workflow you want to enable
I'm facing an ordinal regression problem. In these kind of problems, both a classifier or a regressor can be used as a naive approach.
I'm trying to stack both of them, however sklearn does not ... | 24,432 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/24430 | [
"Bug",
"Enhancement"
] | `cross_val_score` crashes with `StackingRegressor`
### Describe the bug
I'm trying to make a simple stacking and getting the cross validation score but an error raises:
`NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.`
###... | 24,430 | [
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0.03494225814938545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24430 | [
"Bug",
"Enhancement"
] | `cross_val_score` crashes with `StackingRegressor`
### Describe the bug
I'm trying to make a simple stacking and getting the cross validation score but an error raises:
`NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.`
###... | 24,430 | [
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0.03494225814938545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24430 | [
"Bug",
"Enhancement"
] | `cross_val_score` crashes with `StackingRegressor`
### Describe the bug
I'm trying to make a simple stacking and getting the cross validation score but an error raises:
`NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.`
###... | 24,430 | [
-0.049083974212408066,
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0.03494225814938545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24429 | [
"Bug",
"Needs Triage"
] | "StackingClassifier" throws ValueError when cv = "prefit"
### Describe the bug
When I use the `StackingClassifier`, I get the error "ValueError: Expected cv as an integer, cross-validation object (from sklearn.model_selection) or an iterable. Got prefit." when calling the `.fit` method
### Steps/Code to Reproduce
... | 24,429 | [
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https://github.com/scikit-learn/scikit-learn/issues/24428 | [
"Bug",
"Needs Triage"
] | `MinMaxScaler.fit_transform()` overflow when input array is `float16`
### Describe the bug
I often use `float16` array to save working memory, but when I use `MinMaxScaler.fit_transform()`, some values overflow and become `inf`.
This occurs even if the transfomed values can fit to the range of float16.
### Step... | 24,428 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/24428 | [
"Bug",
"Needs Triage"
] | `MinMaxScaler.fit_transform()` overflow when input array is `float16`
### Describe the bug
I often use `float16` array to save working memory, but when I use `MinMaxScaler.fit_transform()`, some values overflow and become `inf`.
This occurs even if the transfomed values can fit to the range of float16.
### Step... | 24,428 | [
-0.04004982113838196,
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0.00109706143848598,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24428 | [
"Bug",
"Needs Triage"
] | `MinMaxScaler.fit_transform()` overflow when input array is `float16`
### Describe the bug
I often use `float16` array to save working memory, but when I use `MinMaxScaler.fit_transform()`, some values overflow and become `inf`.
This occurs even if the transfomed values can fit to the range of float16.
### Step... | 24,428 | [
-0.04004982113838196,
-0.05867195129394531,
0.025798382237553596,
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0.01812570169568062,
0.00109706143848598,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24428 | [
"Bug",
"Needs Triage"
] | `MinMaxScaler.fit_transform()` overflow when input array is `float16`
### Describe the bug
I often use `float16` array to save working memory, but when I use `MinMaxScaler.fit_transform()`, some values overflow and become `inf`.
This occurs even if the transfomed values can fit to the range of float16.
### Step... | 24,428 | [
-0.04004982113838196,
-0.05867195129394531,
0.025798382237553596,
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0.01812570169568062,
0.00109706143848598,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24428 | [
"Bug",
"Needs Triage"
] | `MinMaxScaler.fit_transform()` overflow when input array is `float16`
### Describe the bug
I often use `float16` array to save working memory, but when I use `MinMaxScaler.fit_transform()`, some values overflow and become `inf`.
This occurs even if the transfomed values can fit to the range of float16.
### Step... | 24,428 | [
-0.04004982113838196,
-0.05867195129394531,
0.025798382237553596,
-0.00038643990410491824,
0.08846663683652878,
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0.00109706143848598,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
@cmarmo: would you b... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
Cross-referencing a ... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
> @cmarmo: would you... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
I have started to wo... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
> scikit-learn CI do... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
> should I prioritiz... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
For the failing asse... | 24,427 | [
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https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
@jeremiedbb I am mil... | 24,427 | [
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0.106151461... |
https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
I am having an issue... | 24,427 | [
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0.06020... |
https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
+1 for skipping the ... | 24,427 | [
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-0.03957531973719597,
0.108045... |
https://github.com/scikit-learn/scikit-learn/issues/24427 | [
"New Feature",
"Build / CI",
"Packaging"
] | Python 3.11 wheels
### Describe the workflow you want to enable
Installing scikit-learn for Python 3.11
### Describe your proposed solution
Add Python 3.11 `python: 311` to cibuildwheel CI job matrix
### Additional context
Likely you'll want to wait for scipy/scipy#16851 to land
COMMENT:
About the other test... | 24,427 | [
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0.11026706... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
-0.0008111178176477551,
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-0.010671223513782024,
0.0719... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
0.017121504992246628,
0.020430855453014374,
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0.05799068510532379,
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0.032464299350976944,
-0.003888435661792755,
0.0377... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
0.00022744658053852618,
0.038474421948194504,
0.006704541388899088,
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0.0033741353545337915,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
-0.006671078968793154,
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0.0005290739354677498,
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0.019366353750228882,
0.06276999413967133,
0.02365904673933983,
-0.01648225076496601,
0.06086... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
0.0035356509033590555,
0.025001684203743935,
0.01715366542339325,
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0.030818307772278786,
0.01960466243326664,
-0.002534783910959959,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24424 | [
"Bug"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47587&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 13, 2022)
- test_searchcv_raise_warning_with_non_finite_score[Gri... | 24,424 | [
0.009745700284838676,
0.03574094921350479,
-0.027812525629997253,
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0.044219646602869034,
0.03021595999598503,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24411 | [
"Bug",
"Enhancement",
"Needs Decision",
"module:neural_network"
] | MLPRegressor - Validation score wrongly defined
### Describe the bug
In MLPRegressor, if the option early_stopping is set as True, the model will monitor the loss calculated on the validation set in stead of the training set, using the same loss formulation which is the mean squared error. However, as implemented i... | 24,411 | [
-0.019914017990231514,
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0.023414647206664085,
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0.08191994577646255,
0.0007142883259803057,
0.038544416427612305,
0.008210478350520134,
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0.006647076457738876,
0.07698629796504974,
0.022220192477107048,
0.011071213521063328,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24411 | [
"Bug",
"Enhancement",
"Needs Decision",
"module:neural_network"
] | MLPRegressor - Validation score wrongly defined
### Describe the bug
In MLPRegressor, if the option early_stopping is set as True, the model will monitor the loss calculated on the validation set in stead of the training set, using the same loss formulation which is the mean squared error. However, as implemented i... | 24,411 | [
-0.029453005641698837,
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0.011855226941406727,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24411 | [
"Bug",
"Enhancement",
"Needs Decision",
"module:neural_network"
] | MLPRegressor - Validation score wrongly defined
### Describe the bug
In MLPRegressor, if the option early_stopping is set as True, the model will monitor the loss calculated on the validation set in stead of the training set, using the same loss formulation which is the mean squared error. However, as implemented i... | 24,411 | [
-0.024668067693710327,
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-0.0008359798812307417,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24411 | [
"Bug",
"Enhancement",
"Needs Decision",
"module:neural_network"
] | MLPRegressor - Validation score wrongly defined
### Describe the bug
In MLPRegressor, if the option early_stopping is set as True, the model will monitor the loss calculated on the validation set in stead of the training set, using the same loss formulation which is the mean squared error. However, as implemented i... | 24,411 | [
-0.016040394082665443,
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0.032110992819070816,
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0.01143689639866352,
0.0681542307138443,
0.03704562410712242,
0.009363295510411263,
0.0577371... |
https://github.com/scikit-learn/scikit-learn/issues/24411 | [
"Bug",
"Enhancement",
"Needs Decision",
"module:neural_network"
] | MLPRegressor - Validation score wrongly defined
### Describe the bug
In MLPRegressor, if the option early_stopping is set as True, the model will monitor the loss calculated on the validation set in stead of the training set, using the same loss formulation which is the mean squared error. However, as implemented i... | 24,411 | [
-0.020398227497935295,
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0.029558537527918816,
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0.06398456543684006,
0.009649951942265034,
-0.003003118559718132,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24409 | [
"Bug",
"module:model_selection",
"module:base"
] | GridSearchCV does not seem to recognize whether estimators from StackingClassifier are fitted or not
### Describe the bug
There seems to be a bug with the combination of `GridSearchCV` and `StackingClassifier` when the parameter `cv` of `StackingClassifier` is set to 'prefit'. With this option, the estimators of t... | 24,409 | [
0.012003322131931782,
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0.004344917368143797,
0.0021756712812930346,
0.03683844581246376,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24409 | [
"Bug",
"module:model_selection",
"module:base"
] | GridSearchCV does not seem to recognize whether estimators from StackingClassifier are fitted or not
### Describe the bug
There seems to be a bug with the combination of `GridSearchCV` and `StackingClassifier` when the parameter `cv` of `StackingClassifier` is set to 'prefit'. With this option, the estimators of t... | 24,409 | [
0.012003322131931782,
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0.047792576253414154,
0.02150624990463257,
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0.004344917368143797,
0.0021756712812930346,
0.03683844581246376,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.031085286289453506,
0.08307566493749619,
-0.004546579904854298,
-0.06765926629304886,
-0.0504164844751358,
0.00035131213371641934,
0.01197445672005415,
0.03558892384171486,
0.0013216548832133412,
-0.018137354403734207,
0.10123611986637115,
0.04822597652673721,
0.004227910190820694,
0.034... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03152860701084137,
0.08274827152490616,
-0.0048399027436971664,
-0.0688851997256279,
-0.050297945737838745,
0.00010933360317721963,
0.010733290575444698,
0.03692440688610077,
0.0017780927009880543,
-0.017452042549848557,
0.10263630002737045,
0.048461638391017914,
0.003961056005209684,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.02853522263467312,
0.08284704387187958,
-0.0035397056490182877,
-0.06849024444818497,
-0.0507691353559494,
0.0017231963574886322,
0.01319870911538601,
0.035987790673971176,
0.0012679831124842167,
-0.01699216663837433,
0.09763724356889725,
0.04805221036076546,
0.0040443106554448605,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.02934420295059681,
0.08837278187274933,
-0.00417357636615634,
-0.06683269888162613,
-0.05134885385632515,
-0.003256413619965315,
0.007428555749356747,
0.03599382936954498,
0.0007730085053481162,
-0.016773663461208344,
0.10032553225755692,
0.048821721225976944,
-0.0021800717804580927,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.030392082408070564,
0.08291390538215637,
-0.003072784747928381,
-0.06699064373970032,
-0.05113300681114197,
-0.0014101217966526747,
0.013786736875772476,
0.037629492580890656,
0.0014877041103318334,
-0.018318749964237213,
0.09744690358638763,
0.04795338585972786,
0.003956469241529703,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03042919561266899,
0.08337114751338959,
-0.004318392835557461,
-0.06836380809545517,
-0.05039115995168686,
-0.00010082968219649047,
0.011563656851649284,
0.036573026329278946,
0.0016691121272742748,
-0.0181182362139225,
0.10066847503185272,
0.0484871082007885,
0.0036948653869330883,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.028782423585653305,
0.08531618863344193,
-0.003158490639179945,
-0.06928790360689163,
-0.04712541773915291,
0.003101601731032133,
0.012601488269865513,
0.03519605100154877,
0.002771880943328142,
-0.0168803408741951,
0.09960899502038956,
0.051801394671201706,
0.004213899839669466,
0.02832... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.021991314366459846,
0.08846765011548996,
-0.0071069407276809216,
-0.07060808688402176,
-0.051633913069963455,
-0.0014253964181989431,
0.013840869069099426,
0.039947375655174255,
0.0018887269543483853,
-0.013503903523087502,
0.10024216771125793,
0.054220713675022125,
0.0004061252693645656,
... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.030270874500274658,
0.08269888162612915,
-0.0048826574347913265,
-0.0678425058722496,
-0.04899395629763603,
0.000033808129956014454,
0.012084945105016232,
0.03750336915254593,
0.0020043067634105682,
-0.01780778542160988,
0.10102630406618118,
0.04907004162669182,
0.004331572912633419,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03096144273877144,
0.0819353312253952,
-0.004376801196485758,
-0.0694696307182312,
-0.04960737004876137,
0.0002676416770555079,
0.010571260936558247,
0.03750111907720566,
-0.00007335751433856785,
-0.01759582757949829,
0.1017293930053711,
0.05098138004541397,
0.004317238926887512,
0.03357... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.028955096378922462,
0.08499699831008911,
-0.004493402782827616,
-0.06729445606470108,
-0.04931740090250969,
0.0005647650104947388,
0.009853793308138847,
0.03690026327967644,
0.00120960280764848,
-0.017236053943634033,
0.09951537102460861,
0.050337690860033035,
0.003209180198609829,
0.035... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03029772639274597,
0.0897923931479454,
-0.005057916045188904,
-0.06684271991252899,
-0.04886826127767563,
0.003829898778349161,
0.008837971836328506,
0.03534018620848656,
-0.0006956943543627858,
-0.01878097839653492,
0.094880111515522,
0.04831596836447716,
0.0007662824937142432,
0.032321... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03080020286142826,
0.08569569885730743,
-0.0044010397978127,
-0.06705091893672943,
-0.05059510096907616,
0.0014708430971950293,
0.010668770410120487,
0.03339855372905731,
-0.00014863903925288469,
-0.018135733902454376,
0.09954868257045746,
0.04839998856186867,
0.004563895054161549,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.030671315267682076,
0.08393313735723495,
-0.003155362792313099,
-0.06860339641571045,
-0.050699058920145035,
0.0002535369130782783,
0.012114832177758217,
0.03685849532485008,
0.002295395126566291,
-0.017517097294330597,
0.10093014687299728,
0.04900406301021576,
0.004072731826454401,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.027416082099080086,
0.08762271702289581,
-0.005052828695625067,
-0.06885601580142975,
-0.0516873337328434,
0.0009686214616522193,
0.016022592782974243,
0.03811869025230408,
0.002724384656175971,
-0.014234134927392006,
0.10084807127714157,
0.051336441189050674,
0.0013438307214528322,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/24401 | [
"RFC",
"Packaging"
] | RFC bump up dependencies for 1.2
This is an issue to discuss what will be the min versions of our dependencies for the 1.2 release, targeted in november.
Here's the list of our current min versions
```
current min version | latest version
|
python = 3.8 | 3.11 (by then)
... | 24,401 | [
0.03080020286142826,
0.08569569885730743,
-0.0044010397978127,
-0.06705091893672943,
-0.05059510096907616,
0.0014708430971950293,
0.010668770410120487,
0.03339855372905731,
-0.00014863903925288469,
-0.018135733902454376,
0.09954868257045746,
0.04839998856186867,
0.004563895054161549,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/24392 | [
"Bug",
"Needs Triage"
] | StackingClassifier crashes with stack_method="predict"
### Describe the bug
I trained a StackingClassifier with 8 estimators and logistic regression final estimator, with stacking method of "predict" and after the fit, when i try to do predict i get this error:
Traceback (most recent call last):
File "<string>"... | 24,392 | [
0.009896497242152691,
0.062093764543533325,
0.02841867506504059,
-0.004683774430304766,
0.11951550841331482,
0.004565594717860222,
0.034383028745651245,
0.009161779657006264,
0.017590874806046486,
0.011255732737481594,
0.016061710193753242,
0.004598966334015131,
0.007985659874975681,
0.028... |
https://github.com/scikit-learn/scikit-learn/issues/24390 | [
"Documentation",
"Needs Triage"
] | Example performs data preprocessing before train-test split occurs
### Describe the issue linked to the documentation
[This example](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py) uses the following code... | 24,390 | [
-0.04983656853437424,
0.022348668426275253,
0.012628301978111267,
-0.005620290525257587,
0.02898354083299637,
0.014405902475118637,
0.09548299014568329,
0.024601878598332405,
0.00790459755808115,
-0.01125729363411665,
0.06358194351196289,
0.008780743926763535,
0.018334802240133286,
0.08849... |
https://github.com/scikit-learn/scikit-learn/issues/24390 | [
"Documentation",
"Needs Triage"
] | Example performs data preprocessing before train-test split occurs
### Describe the issue linked to the documentation
[This example](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py) uses the following code... | 24,390 | [
-0.03873535990715027,
0.008431406691670418,
0.0067177945747971535,
-0.003688712837174535,
0.03071938082575798,
0.01946978084743023,
0.09216603636741638,
0.016215503215789795,
0.01659148745238781,
-0.016271041706204414,
0.0545627623796463,
0.0025580560322850943,
0.01736314967274666,
0.06620... |
https://github.com/scikit-learn/scikit-learn/issues/24390 | [
"Documentation",
"Needs Triage"
] | Example performs data preprocessing before train-test split occurs
### Describe the issue linked to the documentation
[This example](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py) uses the following code... | 24,390 | [
-0.031039100140333176,
-0.00015957174764480442,
0.006095778197050095,
-0.0026901308447122574,
0.029064679518342018,
0.02272140420973301,
0.09057553857564926,
0.018353139981627464,
0.00958528183400631,
-0.016360152512788773,
0.05951787158846855,
0.004362593870609999,
0.024785732850432396,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24387 | [
"API"
] | Deprecate n_features_ in PCA
When n_features_in_ was introduced for all estimators, some estimators already had a `n_features_` attribute that was deprecated in favor of n_features_in_. It should have been done for PCA and RFE as well, but we must have missed it. We need to deprecate in 1.2 for removal in 1.4 now.
CO... | 24,387 | [
-0.03898875042796135,
0.07394315302371979,
-0.029358798637986183,
-0.016044646501541138,
0.018035562708973885,
0.0030160327441990376,
0.039856668561697006,
-0.005100669339299202,
0.0036213123239576817,
0.03901369124650955,
0.08906780183315277,
0.0091453418135643,
0.005190972238779068,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24387 | [
"API"
] | Deprecate n_features_ in PCA
When n_features_in_ was introduced for all estimators, some estimators already had a `n_features_` attribute that was deprecated in favor of n_features_in_. It should have been done for PCA and RFE as well, but we must have missed it. We need to deprecate in 1.2 for removal in 1.4 now.
CO... | 24,387 | [
-0.0448286235332489,
0.08053109794855118,
-0.02865005098283291,
-0.014898872002959251,
0.020825443789362907,
0.0035791529808193445,
0.04101293534040451,
-0.0069991908967494965,
0.004304341971874237,
0.03847331926226616,
0.08373337239027023,
0.013678637333214283,
0.0050700511783361435,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24387 | [
"API"
] | Deprecate n_features_ in PCA
When n_features_in_ was introduced for all estimators, some estimators already had a `n_features_` attribute that was deprecated in favor of n_features_in_. It should have been done for PCA and RFE as well, but we must have missed it. We need to deprecate in 1.2 for removal in 1.4 now.
CO... | 24,387 | [
-0.03077133186161518,
0.04253300651907921,
-0.02603750489652157,
-0.012142783962190151,
-0.011590365320444107,
-0.00537573266774416,
0.044402338564395905,
-0.011518995277583599,
-0.019056593999266624,
0.02660268172621727,
0.06440962851047516,
0.0007659763796254992,
0.02032279223203659,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24387 | [
"API"
] | Deprecate n_features_ in PCA
When n_features_in_ was introduced for all estimators, some estimators already had a `n_features_` attribute that was deprecated in favor of n_features_in_. It should have been done for PCA and RFE as well, but we must have missed it. We need to deprecate in 1.2 for removal in 1.4 now.
CO... | 24,387 | [
-0.04421859234571457,
0.058509696274995804,
-0.023411009460687637,
-0.010289120487868786,
0.007047250401228666,
0.0008311276906169951,
0.024333929643034935,
-0.0002727275714278221,
-0.009670574218034744,
0.028827352449297905,
0.0684894323348999,
0.0015464122407138348,
0.0160475242882967,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24387 | [
"API"
] | Deprecate n_features_ in PCA
When n_features_in_ was introduced for all estimators, some estimators already had a `n_features_` attribute that was deprecated in favor of n_features_in_. It should have been done for PCA and RFE as well, but we must have missed it. We need to deprecate in 1.2 for removal in 1.4 now.
CO... | 24,387 | [
-0.03693943843245506,
0.07484555244445801,
-0.027236955240368843,
-0.014509337954223156,
0.01795581914484501,
0.003261045552790165,
0.04217550903558731,
-0.004682207480072975,
0.003594078589230776,
0.04019647464156151,
0.08721892535686493,
0.008473929949104786,
0.007794403005391359,
0.0610... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24381 | [
"Enhancement",
"module:metrics"
] | Inconsistency in AUC ROC and AUPR API
### Describe the bug
When only one class is present on the groundtruth. The function `roc_auc_score` throws an `ValueError` and exits while the `average_precision_score` returns `-0.0`. I feel that both functions should return similar output in this case.
### Steps/Code to Repro... | 24,381 | [
-0.002652814844623208,
-0.04670407250523567,
0.015505267307162285,
0.02750721387565136,
0.0666268840432167,
-0.012691582553088665,
-0.0016717629041522741,
-0.046735502779483795,
-0.02237153798341751,
-0.013143124990165234,
0.023610631003975868,
0.007513635791838169,
0.05797125771641731,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24378 | [
"Documentation"
] | DOC: new example on feature engineering for time-series forecasting with prediction intervals
For EuroScipy 2022, I gave a tutorial on how to use pandas-engineered lagged and windowed features for time series forecasting with scikit-learn regressors.
Here is the notebook:
- https://nbviewer.org/github/ogrisel/eu... | 24,378 | [
-0.026817811653017998,
0.08375568687915802,
-0.006058245897293091,
-0.011046062223613262,
-0.010746530257165432,
-0.0018685393733903766,
0.0833299532532692,
-0.021797019988298416,
0.03182928264141083,
0.005274033639580011,
0.06108296290040016,
0.019909925758838654,
-0.010787690989673138,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24378 | [
"Documentation"
] | DOC: new example on feature engineering for time-series forecasting with prediction intervals
For EuroScipy 2022, I gave a tutorial on how to use pandas-engineered lagged and windowed features for time series forecasting with scikit-learn regressors.
Here is the notebook:
- https://nbviewer.org/github/ogrisel/eu... | 24,378 | [
-0.01571168750524521,
0.09836047887802124,
-0.0050360411405563354,
-0.012679020874202251,
-0.0243778545409441,
0.00268853479065001,
0.08596280217170715,
-0.025015970692038536,
0.021278269588947296,
0.013323977589607239,
0.03186813369393349,
0.009085464291274548,
-0.0001874148874776438,
0.1... |
https://github.com/scikit-learn/scikit-learn/issues/24378 | [
"Documentation"
] | DOC: new example on feature engineering for time-series forecasting with prediction intervals
For EuroScipy 2022, I gave a tutorial on how to use pandas-engineered lagged and windowed features for time series forecasting with scikit-learn regressors.
Here is the notebook:
- https://nbviewer.org/github/ogrisel/eu... | 24,378 | [
-0.023698385804891586,
0.09481871128082275,
-0.0027100660372525454,
-0.019515525549650192,
-0.021680505946278572,
-0.00921277329325676,
0.07776462286710739,
-0.021046273410320282,
0.03421924635767937,
0.008822646923363209,
0.04404978081583977,
0.0163887906819582,
-0.0044027287513017654,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24378 | [
"Documentation"
] | DOC: new example on feature engineering for time-series forecasting with prediction intervals
For EuroScipy 2022, I gave a tutorial on how to use pandas-engineered lagged and windowed features for time series forecasting with scikit-learn regressors.
Here is the notebook:
- https://nbviewer.org/github/ogrisel/eu... | 24,378 | [
-0.020294466987252235,
0.08710698783397675,
-0.0038423300720751286,
-0.014992427080869675,
-0.019190823659300804,
-0.00827113725244999,
0.0759122222661972,
-0.02096589282155037,
0.02758014388382435,
0.013502757996320724,
0.043728675693273544,
0.01602860540151596,
-0.00223141280002892,
0.16... |
https://github.com/scikit-learn/scikit-learn/issues/24369 | [
"Documentation"
] | DOC Clarify documentation writing guideline
### Describe the issue linked to the documentation
The ['Guidelines for writing documentation'](https://scikit-learn.org/dev/developers/contributing.html#guidelines-for-writing-documentation) section seems to be specifically about docstrings and the suggestions don't seem... | 24,369 | [
0.03278937563300133,
-0.06891198456287384,
0.01066536083817482,
-0.03957827016711235,
0.016489440575242043,
-0.0023830849677324295,
0.03805528208613396,
0.04649028554558754,
-0.08012630045413971,
-0.046492885798215866,
0.0675419494509697,
-0.02288542129099369,
0.03696681931614876,
-0.03172... |
https://github.com/scikit-learn/scikit-learn/issues/24369 | [
"Documentation"
] | DOC Clarify documentation writing guideline
### Describe the issue linked to the documentation
The ['Guidelines for writing documentation'](https://scikit-learn.org/dev/developers/contributing.html#guidelines-for-writing-documentation) section seems to be specifically about docstrings and the suggestions don't seem... | 24,369 | [
0.04092630371451378,
-0.04050241410732269,
0.0037678610533475876,
-0.03385014459490776,
0.027369825169444084,
0.0024997428990900517,
0.027469199150800705,
0.04282638058066368,
-0.07249752432107925,
-0.04428190737962723,
0.045869771391153336,
0.006863679736852646,
0.044407233595848083,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24369 | [
"Documentation"
] | DOC Clarify documentation writing guideline
### Describe the issue linked to the documentation
The ['Guidelines for writing documentation'](https://scikit-learn.org/dev/developers/contributing.html#guidelines-for-writing-documentation) section seems to be specifically about docstrings and the suggestions don't seem... | 24,369 | [
0.030804120004177094,
-0.06734427064657211,
0.008398598991334438,
-0.0416339673101902,
0.019561653956770897,
-0.004996903706341982,
0.03708614036440849,
0.0417439229786396,
-0.07637251168489456,
-0.04042075201869011,
0.05520841106772423,
-0.012957029044628143,
0.03779617324471474,
-0.03770... |
https://github.com/scikit-learn/scikit-learn/issues/24364 | [
"Documentation"
] | Deprecated is_first_col() function
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/examples/decomposition/plot_varimax_fa.py#L72
The is_first_col function was deprecated in Matplotlib 3.4 #40714 (https://github.com/pandas-dev/pandas/issues/40714). Could you change the func... | 24,364 | [
0.04094858467578888,
0.018590325489640236,
0.03481690213084221,
0.02224539965391159,
0.04579615220427513,
0.033362772315740585,
0.027616197243332863,
0.06913405656814575,
0.018309123814105988,
0.001962078269571066,
-0.007600444834679365,
-0.0016022080089896917,
0.007474057376384735,
0.0190... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24355 | [
"Enhancement",
"module:utils"
] | `type_of_target` returns `unknown` for valid arrays of dtype `object`
### Describe the bug
`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.
I would be happy to contribute a fix, but I'm not entirely sure h... | 24,355 | [
0.009672701358795166,
0.02520253136754036,
0.014648889191448689,
0.020970065146684647,
0.10621221363544464,
0.033888570964336395,
0.05032714456319809,
0.035449251532554626,
-0.02658838964998722,
-0.03543267771601677,
0.04217047244310379,
0.0544777512550354,
0.013514909893274307,
-0.0078922... |
https://github.com/scikit-learn/scikit-learn/issues/24353 | [
"Bug",
"module:manifold"
] | BUG: MLLE implementation does not yield expected results
### Describe the bug
The implemented manifold learning method Modified Locally Linear Embedding (MLLE) in /sklearn/manifold/_locally_linear.py line 403 ('method==modified') does not produce the expected results, see attached img.
From the reference [3] it ... | 24,353 | [
-0.005639086477458477,
-0.03877316415309906,
0.019593670964241028,
0.06772606074810028,
0.008705832064151764,
-0.0240049809217453,
0.05056174844503403,
0.008469223976135254,
0.026448257267475128,
0.021135438233613968,
-0.0025163928512483835,
0.07713650912046432,
0.017344675958156586,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24340 | [
"Bug"
] | BUG: GaussianProcessRegressor.predict inplace modifies input X, when passed via kernel
### Describe the bug
In line 425, 426 of /sklearn/gaussian_process/_gpr.py (inside the predict method) y_var is modified in place:
```
# Compute variance of predictive distribution
# Use einsum ... | 24,340 | [
0.015717322006821632,
0.02504741959273815,
0.007130498066544533,
0.0008761823992244899,
0.05681764334440231,
-0.0352056659758091,
0.017844870686531067,
0.0032567987218499184,
0.00026655790861696005,
0.03651934862136841,
0.03581644222140312,
0.07501363754272461,
0.04177936166524887,
0.02659... |
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