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/23130 | [
"Bug",
"module:metrics"
] | Macro Average F1 Score Computation Bug
### Describe the bug
The current algorithm computes labels with no support:
### Steps/Code to Reproduce
```
precision recall f1-score support
archive 0.00 0.00 0.00 0
high 0.35 0.34 0.34 1400
... | 23,130 | [
0.020909903571009636,
-0.022757334634661674,
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0.006587453652173281,
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0.05341149866580963,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23130 | [
"Bug",
"module:metrics"
] | Macro Average F1 Score Computation Bug
### Describe the bug
The current algorithm computes labels with no support:
### Steps/Code to Reproduce
```
precision recall f1-score support
archive 0.00 0.00 0.00 0
high 0.35 0.34 0.34 1400
... | 23,130 | [
0.020909903571009636,
-0.022757334634661674,
0.02515738643705845,
0.02200588397681713,
0.07073367387056351,
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-0.003825857536867261,
-0.008184789679944515,
0.05341149866580963,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23130 | [
"Bug",
"module:metrics"
] | Macro Average F1 Score Computation Bug
### Describe the bug
The current algorithm computes labels with no support:
### Steps/Code to Reproduce
```
precision recall f1-score support
archive 0.00 0.00 0.00 0
high 0.35 0.34 0.34 1400
... | 23,130 | [
0.020909903571009636,
-0.022757334634661674,
0.02515738643705845,
0.02200588397681713,
0.07073367387056351,
-0.010787461884319782,
0.006587453652173281,
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-0.033048637211322784,
-0.003825857536867261,
-0.008184789679944515,
0.05341149866580963,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23130 | [
"Bug",
"module:metrics"
] | Macro Average F1 Score Computation Bug
### Describe the bug
The current algorithm computes labels with no support:
### Steps/Code to Reproduce
```
precision recall f1-score support
archive 0.00 0.00 0.00 0
high 0.35 0.34 0.34 1400
... | 23,130 | [
0.020909903571009636,
-0.022757334634661674,
0.02515738643705845,
0.02200588397681713,
0.07073367387056351,
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-0.003825857536867261,
-0.008184789679944515,
0.05341149866580963,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23125 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | GridSearchCV scoring behavior on weighted metrics, such as Balanced Accuracy
### Describe the workflow you want to enable
As a user who specify weighted metrics/scoring such as balanced accuracy, I would like the scoring `GridSearchCV` uses for optimising and `best_score_` to be weighted as well. Based on current doc... | 23,125 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/23125 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | GridSearchCV scoring behavior on weighted metrics, such as Balanced Accuracy
### Describe the workflow you want to enable
As a user who specify weighted metrics/scoring such as balanced accuracy, I would like the scoring `GridSearchCV` uses for optimising and `best_score_` to be weighted as well. Based on current doc... | 23,125 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/23125 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | GridSearchCV scoring behavior on weighted metrics, such as Balanced Accuracy
### Describe the workflow you want to enable
As a user who specify weighted metrics/scoring such as balanced accuracy, I would like the scoring `GridSearchCV` uses for optimising and `best_score_` to be weighted as well. Based on current doc... | 23,125 | [
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0.0014718100428581238,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/23125 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | GridSearchCV scoring behavior on weighted metrics, such as Balanced Accuracy
### Describe the workflow you want to enable
As a user who specify weighted metrics/scoring such as balanced accuracy, I would like the scoring `GridSearchCV` uses for optimising and `best_score_` to be weighted as well. Based on current doc... | 23,125 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23125 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | GridSearchCV scoring behavior on weighted metrics, such as Balanced Accuracy
### Describe the workflow you want to enable
As a user who specify weighted metrics/scoring such as balanced accuracy, I would like the scoring `GridSearchCV` uses for optimising and `best_score_` to be weighted as well. Based on current doc... | 23,125 | [
-0.024432601407170296,
0.015261195600032806,
0.023892981931567192,
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0.02... |
https://github.com/scikit-learn/scikit-learn/issues/23119 | [
"New Feature",
"Needs Triage"
] | Add expected calibration error (ECE) functionality to sklearn.
### Describe the workflow you want to enable
Measuring calibration error in deep learning is a big issue now-a-days. However, we do not find any suitable package available to measure the calibration error. I would like to add a function that can measure... | 23,119 | [
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0.025429995730519295,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
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0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
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0.010143550112843513,
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0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23112 | [
"Bug",
"module:pipeline"
] | Cache final transformer in pipeline with memory setting
### Describe the bug
When setting the `memory` parameter of a transformer `Pipeline` (i.e., one whose last step is a transformer), the final transformer is not cached.
Discovered at https://stackoverflow.com/q/71812869/10495893.
### Steps/Code to Reproduce
... | 23,112 | [
-0.024833302944898605,
0.027635645121335983,
0.0007631565094925463,
0.017548782750964165,
0.03959108516573906,
-0.008200605399906635,
0.03443177416920662,
0.010143550112843513,
0.023286428302526474,
0.003522033803164959,
0.026396751403808594,
0.007551575545221567,
-0.0006499802111648023,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23109 | [
"Bug",
"module:multioutput"
] | RegressionChain does not accept nans, when base_estimator does
### Describe the bug
XGBRegressors accepts nan values. Which is defined in the tag `force_all_finite='allow-nan`
But if define
```py
RegressorChain(XGBRegressor())
```
Then raises the error
```
File "C:\\lib\site-packages\sklearn\multioutput.py", ... | 23,109 | [
-0.0015728865982964635,
0.002558354754000902,
0.04489646852016449,
-0.024542279541492462,
0.08679083734750748,
0.007173409219831228,
0.07493461668491364,
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0.020206820219755173,
0.02345171943306923,
0.0032120589166879654,
-0.010533376596868038,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/23109 | [
"Bug",
"module:multioutput"
] | RegressionChain does not accept nans, when base_estimator does
### Describe the bug
XGBRegressors accepts nan values. Which is defined in the tag `force_all_finite='allow-nan`
But if define
```py
RegressorChain(XGBRegressor())
```
Then raises the error
```
File "C:\\lib\site-packages\sklearn\multioutput.py", ... | 23,109 | [
-0.0015728865982964635,
0.002558354754000902,
0.04489646852016449,
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0.020206820219755173,
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0.0032120589166879654,
-0.010533376596868038,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.011875849217176437,
0.041720107197761536,
-0.006642215885221958,
-0.012335517443716526,
0.022935859858989716,
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0.037790875881910324,
0.0241203885525465,
-0.041586484760046005,
... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.02334178239107132,
0.05394960567355156,
0.002523730043321848,
-0.012277846224606037,
0.031701553612947464,
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0.0337534062564373,
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0.004... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.01032015960663557,
0.030265042558312416,
0.010618933476507664,
-0.010961071588099003,
0.04986788332462311,
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-0.017133943736553192,
0.05292244628071785,
0.0454995222389698,
-0.06304927170276642,
0.0017... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.02100280113518238,
0.0517328716814518,
0.001742035150527954,
-0.019686730578541756,
0.024784475564956665,
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0.006... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.01945395953953266,
0.040959808975458145,
0.0007977879140526056,
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0.03431108593940735,
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0.025057945400476456,
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0.05367448553442955,
0.03929118812084198,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.023407725617289543,
0.05016373470425606,
0.0014807499246671796,
-0.021709052845835686,
0.0353117473423481,
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0.04810170456767082,
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0.054309647530317307,
0.0425424687564373,
-0.06417758762836456,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.020342618227005005,
0.05409299209713936,
0.0019461591728031635,
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0.04333396628499031,
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0.045601069927215576,
0.03251112625002861,
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-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23108 | [
"New Feature",
"module:model_selection"
] | Add "pre_dispatch" parameter to HalvingGridSearchCV
### Describe the workflow you want to enable
In base GridSearchCV it is possible to set a pre_dispatch parameter, so that RAM usage by new jobs is limited.
I would like to do the same thing for the newer and arguably better versions of gridsearch.
### Describe you... | 23,108 | [
-0.01810680516064167,
0.04531577602028847,
0.0013716440880671144,
-0.022853802889585495,
0.030313944444060326,
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0.046767957508563995,
0.026481008157134056,
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0.055448759347200394,
0.03756021708250046,
-0.06849759072065353,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23107 | [
"Bug",
"module:feature_selection"
] | `SequentialFeatureSelector` is not passing pandas df to estimator/pipeline
### Describe the bug
`SequentialFeatureSelector` cannot be used with a pipeline that expects a pandas dataframe (e.g. a one containing a `ColumnTransformer`) as an input.
At the same time the same pipeline can be used in `cross_val_score`... | 23,107 | [
-0.0068492465652525425,
0.04042976349592209,
0.02340821735560894,
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0.10591041296720505,
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0.016390979290008545,
0.0362362340092659,
0.023378772661089897,
0.03820853307843208,
0.06465... |
https://github.com/scikit-learn/scikit-learn/issues/23107 | [
"Bug",
"module:feature_selection"
] | `SequentialFeatureSelector` is not passing pandas df to estimator/pipeline
### Describe the bug
`SequentialFeatureSelector` cannot be used with a pipeline that expects a pandas dataframe (e.g. a one containing a `ColumnTransformer`) as an input.
At the same time the same pipeline can be used in `cross_val_score`... | 23,107 | [
-0.0068492465652525425,
0.04042976349592209,
0.02340821735560894,
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0.016390979290008545,
0.0362362340092659,
0.023378772661089897,
0.03820853307843208,
0.06465... |
https://github.com/scikit-learn/scikit-learn/issues/23096 | [
"Bug"
] | When import sklearn, there is an AttributeError
### Describe the bug
When I wrote some code as follows and click run, it was broken.
`import sklearn`
Python version: `3.8.2`
Scikit-learn version: `1.0.2`
Error Information:
```
Traceback (most recent call last):
File "/data/user/0/ru.iiec.pydroid3/f... | 23,096 | [
0.056656040251255035,
-0.029120400547981262,
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0.0965307354927063,
0.05501613765954971,
0.08325860649347305,
0.0389435701072216,
0.06574862450361252,
-0.0446266233921051,
-0.008660354651510715,
0.05343317240476608,
0.005329703912138939,
0.02178305... |
https://github.com/scikit-learn/scikit-learn/issues/23096 | [
"Bug"
] | When import sklearn, there is an AttributeError
### Describe the bug
When I wrote some code as follows and click run, it was broken.
`import sklearn`
Python version: `3.8.2`
Scikit-learn version: `1.0.2`
Error Information:
```
Traceback (most recent call last):
File "/data/user/0/ru.iiec.pydroid3/f... | 23,096 | [
0.056656040251255035,
-0.029120400547981262,
-0.01563115231692791,
-0.004007519688457251,
0.0965307354927063,
0.05501613765954971,
0.08325860649347305,
0.0389435701072216,
0.06574862450361252,
-0.0446266233921051,
-0.008660354651510715,
0.05343317240476608,
0.005329703912138939,
0.02178305... |
https://github.com/scikit-learn/scikit-learn/issues/23096 | [
"Bug"
] | When import sklearn, there is an AttributeError
### Describe the bug
When I wrote some code as follows and click run, it was broken.
`import sklearn`
Python version: `3.8.2`
Scikit-learn version: `1.0.2`
Error Information:
```
Traceback (most recent call last):
File "/data/user/0/ru.iiec.pydroid3/f... | 23,096 | [
0.056656040251255035,
-0.029120400547981262,
-0.01563115231692791,
-0.004007519688457251,
0.0965307354927063,
0.05501613765954971,
0.08325860649347305,
0.0389435701072216,
0.06574862450361252,
-0.0446266233921051,
-0.008660354651510715,
0.05343317240476608,
0.005329703912138939,
0.02178305... |
https://github.com/scikit-learn/scikit-learn/issues/23080 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Implement Min-Cut clustering
### Describe the workflow you want to enable
Use the exact min-cut culstering method with API similar to sklearn.cluster.SpectralClustering.
### Describe your proposed solution
Implement an algorithm to solve the min-cut problem as a means of dataset clustering.
### Describe alternati... | 23,080 | [
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0.0048253582790493965,
0.04277961328625679,
0.06144944205880165,
0.00004478792106965557,
0.010734341107308865,
0.03752889484167099,
-0.024702832102775574,
... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
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0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23074 | [
"Bug",
"module:linear_model"
] | RidgeCV doesn't allow `alpha=0`
### Describe the bug
RidgeCV doesn't allow any alphas to be 0, despite the underlying `Ridge` linear model allowing such behavior.
### Steps/Code to Reproduce
```python
from sklearn.datasets import load_diabetes
from sklearn.linear_model import RidgeCV
X, y = load_diabetes(return_... | 23,074 | [
0.02770918980240822,
-0.0298320259898901,
0.05147925019264221,
0.0451635904610157,
0.09042968600988388,
-0.02757994271814823,
0.0538465678691864,
0.05123242735862732,
0.011291731148958206,
-0.0018135539721697569,
0.02632726915180683,
0.05523587390780449,
-0.0265148114413023,
0.036124367266... |
https://github.com/scikit-learn/scikit-learn/issues/23072 | [
"Documentation"
] | Link to logos in Community section of website
Can we link to the scikit-learn logos in the "Community" section of the website?
Currently, the logos are buried, and I think this might make it easier for users to find it:
https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
To Do:
- [x] create a READ... | 23,072 | [
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0.03141629323363304,
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0.04149036109447479,
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0.036508508026599884,
-0.0030886996537446976,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23072 | [
"Documentation"
] | Link to logos in Community section of website
Can we link to the scikit-learn logos in the "Community" section of the website?
Currently, the logos are buried, and I think this might make it easier for users to find it:
https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
To Do:
- [x] create a READ... | 23,072 | [
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0.024254482239484787,
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-0.0029470897279679775,
... |
https://github.com/scikit-learn/scikit-learn/issues/23072 | [
"Documentation"
] | Link to logos in Community section of website
Can we link to the scikit-learn logos in the "Community" section of the website?
Currently, the logos are buried, and I think this might make it easier for users to find it:
https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
To Do:
- [x] create a READ... | 23,072 | [
0.0355948768556118,
-0.0168185792863369,
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0.02407880499958992,
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0.0590210035443306,
-0.007744137663394213,
0.039166... |
https://github.com/scikit-learn/scikit-learn/issues/23072 | [
"Documentation"
] | Link to logos in Community section of website
Can we link to the scikit-learn logos in the "Community" section of the website?
Currently, the logos are buried, and I think this might make it easier for users to find it:
https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
To Do:
- [x] create a READ... | 23,072 | [
0.03586834669113159,
-0.02140922099351883,
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0.028428224846720695,
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0.03244825825095177,
0.0013136802008375525,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23072 | [
"Documentation"
] | Link to logos in Community section of website
Can we link to the scikit-learn logos in the "Community" section of the website?
Currently, the logos are buried, and I think this might make it easier for users to find it:
https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
To Do:
- [x] create a READ... | 23,072 | [
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0.027212325483560562,
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0.058134231716394424,
-0.008069697767496109,
... |
https://github.com/scikit-learn/scikit-learn/issues/23069 | [
"Bug",
"Needs Triage"
] | Error during installation
### Describe the bug
getting error while installing pycaret
### Steps/Code to Reproduce
pip install pycaret
### Expected Results
failed to build wheels
### Actual ... | 23,069 | [
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0.0006848170305602252,
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0.0313081257045269,
0.020923234522342682,
0.057227447628974915,
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0.013113466091454029,
0.04214302822947502,
0.04793936014175415,
-0.021364081650972366,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23059 | [
"Documentation",
"Enhancement",
"module:metrics"
] | DOC Improve the docstring of log loss to cover the multiclass case
### Describe the issue linked to the documentation
The docstring of the `log_loss` function (also known as the "cross-entropy loss") only gives the mathematical description of the loss when `y_true` is a binary variable (binary cross-entropy).
##... | 23,059 | [
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0.03669023886322975,
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0.015757856890559196,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23059 | [
"Documentation",
"Enhancement",
"module:metrics"
] | DOC Improve the docstring of log loss to cover the multiclass case
### Describe the issue linked to the documentation
The docstring of the `log_loss` function (also known as the "cross-entropy loss") only gives the mathematical description of the loss when `y_true` is a binary variable (binary cross-entropy).
##... | 23,059 | [
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0.037587057799100876,
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0.014841864816844463,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23059 | [
"Documentation",
"Enhancement",
"module:metrics"
] | DOC Improve the docstring of log loss to cover the multiclass case
### Describe the issue linked to the documentation
The docstring of the `log_loss` function (also known as the "cross-entropy loss") only gives the mathematical description of the loss when `y_true` is a binary variable (binary cross-entropy).
##... | 23,059 | [
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0.03812148794531822,
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0.014147810637950897,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23059 | [
"Documentation",
"Enhancement",
"module:metrics"
] | DOC Improve the docstring of log loss to cover the multiclass case
### Describe the issue linked to the documentation
The docstring of the `log_loss` function (also known as the "cross-entropy loss") only gives the mathematical description of the loss when `y_true` is a binary variable (binary cross-entropy).
##... | 23,059 | [
-0.007087737321853638,
0.0033157076686620712,
0.00868825614452362,
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0.03931192681193352,
-0.0027380804531276226,
0.01734529249370098,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23053 | [
"Documentation"
] | Doc Examples Bug: `plot_column_transformer_mixed_types`, missing categorical SimpleImputer
### Describe the issue linked to the documentation
The [`plot_column_transformer_mixed_types`](https://github.com/scikit-learn/scikit-learn/blob/582fa30a31ffd1d2afc6325ec3506418e35b88c2/examples/compose/plot_column_transformer_... | 23,053 | [
-0.028276028111577034,
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0.09613281488418579,
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0.019890911877155304,
-0.0010724897729232907,
0.02953014336526394,
0.034... |
https://github.com/scikit-learn/scikit-learn/issues/23053 | [
"Documentation"
] | Doc Examples Bug: `plot_column_transformer_mixed_types`, missing categorical SimpleImputer
### Describe the issue linked to the documentation
The [`plot_column_transformer_mixed_types`](https://github.com/scikit-learn/scikit-learn/blob/582fa30a31ffd1d2afc6325ec3506418e35b88c2/examples/compose/plot_column_transformer_... | 23,053 | [
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0.010465101338922977,
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0.011993163265287876,
0.043... |
https://github.com/scikit-learn/scikit-learn/issues/23053 | [
"Documentation"
] | Doc Examples Bug: `plot_column_transformer_mixed_types`, missing categorical SimpleImputer
### Describe the issue linked to the documentation
The [`plot_column_transformer_mixed_types`](https://github.com/scikit-learn/scikit-learn/blob/582fa30a31ffd1d2afc6325ec3506418e35b88c2/examples/compose/plot_column_transformer_... | 23,053 | [
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0.022737525403499603,
0.0024752942845225334,
0.01982882246375084,
0.0311... |
https://github.com/scikit-learn/scikit-learn/issues/23048 | [
"Bug",
"module:datasets"
] | Covtype dataset raises error when fetching
### Describe the bug
When fetching the Covtype dataset
```python
from sklearn.datasets import fetch_covtype
fetch_covtype()
```
I am getting an
```python
error: Error -3 while decompressing data: invalid block type
```
Renaming the folder `$HOME/scikit_learn... | 23,048 | [
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0.016194023191928864,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23032 | [
"Bug",
"Needs Triage"
] | GaussianMixture sample() ValueError on Models with 1 Component Fitted on <32 samples
### Describe the bug
If you fit a GaussianMixture model with one component on less than 32 samples, a ValueError is thrown when trying to generate a random sample from the model. If you use a model with more than one component, you a... | 23,032 | [
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0.018528325483202934,
0.016965370625257492,
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... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.06524506956338882,
-0.015692157670855522,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.035... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.035... |
https://github.com/scikit-learn/scikit-learn/issues/23025 | [
"New Feature",
"module:preprocessing"
] | LabelEncoder based on value_counts
### Describe the workflow you want to enable
sklearn.preprocessing.LabelEncoder sorts classes_ based on alphabet. Would be good to have the parameter mapping='value_counts' to sort the classes based on the number of appearences (normalized) in training sample.
If you give ok to... | 23,025 | [
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0.02987... |
https://github.com/scikit-learn/scikit-learn/issues/23019 | [
"New Feature",
"Needs Triage"
] | DateEncoder
### Describe the workflow you want to enable
Would be cool to have a preprocessor that maps dates (str or unixtime) to its' respective date parts: dayofmonth, dayofweek, hour,etc.
There's any reason this is not included in sklearn?
In case not, I could do a PR
### Describe your proposed solution
>>>... | 23,019 | [
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0.047... |
https://github.com/scikit-learn/scikit-learn/issues/23018 | [
"New Feature",
"Needs Triage"
] | Add Pre-fitted Model to `VotingClassifier`
### Describe the workflow you want to enable
Allow passing trained models to `VotingClassifier`, and use these trained models directly for prediction, without refitting.
The current `VotingClassifier` requires fitting all inputted estimators on the given training data, ... | 23,018 | [
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https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0.07179806381464005,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0.07965187728404999,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0.00845501758158207,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23014 | [
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_vstacked_X is not stable
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40346&view=logs&jobId=97641769-79fb-5590-9088-a30ce9b850b9&j=97641769-79fb-5590-9088-a30ce9b850b9&t=4745baa1-36b5-56c8-9a8e-6480742db1a6
introduced in #22910.
@lorentzenchr was it tested on "all" ... | 23,014 | [
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0.011068199761211872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23013 | [
"Needs Triage"
] | Dataframe also as a sklearn transform output
Hi all,
i work intensively with sklearn pipeline for building ML pipelines and pre-processing. Sklearn has been really useful in this case.
The thing is post model training , i would like to know what my dataset looks like, which columns were dropped, or transformed. Ho... | 23,013 | [
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0.05003989487886429,
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0.003959247376769781,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/23013 | [
"Needs Triage"
] | Dataframe also as a sklearn transform output
Hi all,
i work intensively with sklearn pipeline for building ML pipelines and pre-processing. Sklearn has been really useful in this case.
The thing is post model training , i would like to know what my dataset looks like, which columns were dropped, or transformed. Ho... | 23,013 | [
-0.04175300523638725,
0.05003989487886429,
0.018367553129792213,
-0.013023771345615387,
0.06529761105775833,
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0.047876693308353424,
0.003959247376769781,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/23013 | [
"Needs Triage"
] | Dataframe also as a sklearn transform output
Hi all,
i work intensively with sklearn pipeline for building ML pipelines and pre-processing. Sklearn has been really useful in this case.
The thing is post model training , i would like to know what my dataset looks like, which columns were dropped, or transformed. Ho... | 23,013 | [
-0.04175300523638725,
0.05003989487886429,
0.018367553129792213,
-0.013023771345615387,
0.06529761105775833,
0.0225369855761528,
0.06372594833374023,
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-0.0036628744564950466,
-0.009599734097719193,
0.047876693308353424,
0.003959247376769781,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/23013 | [
"Needs Triage"
] | Dataframe also as a sklearn transform output
Hi all,
i work intensively with sklearn pipeline for building ML pipelines and pre-processing. Sklearn has been really useful in this case.
The thing is post model training , i would like to know what my dataset looks like, which columns were dropped, or transformed. Ho... | 23,013 | [
-0.04175300523638725,
0.05003989487886429,
0.018367553129792213,
-0.013023771345615387,
0.06529761105775833,
0.0225369855761528,
0.06372594833374023,
-0.011883131228387356,
0.019087467342615128,
-0.0036628744564950466,
-0.009599734097719193,
0.047876693308353424,
0.003959247376769781,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/23008 | [
"Bug",
"Needs Triage"
] | PR #22548 breaks documentation building
### Describe the bug
The changes made in #22548 appear to cause building the example docs with `make html` to fail (the docs build fine in the previous commit).
### Steps/Code to Reproduce
1. Setup a [development environment for scikit-learn](https://github.com/data-umbrella/... | 23,008 | [
0.03202636167407036,
0.020120147615671158,
-0.003734729252755642,
-0.05157291144132614,
0.04990759119391441,
0.04200383648276329,
0.03489961475133896,
0.03013749048113823,
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-0.04159628227353096,
0.028817730024456978,
0.01488490216434002,
0.050496142357587814,
-0.02920... |
https://github.com/scikit-learn/scikit-learn/issues/23008 | [
"Bug",
"Needs Triage"
] | PR #22548 breaks documentation building
### Describe the bug
The changes made in #22548 appear to cause building the example docs with `make html` to fail (the docs build fine in the previous commit).
### Steps/Code to Reproduce
1. Setup a [development environment for scikit-learn](https://github.com/data-umbrella/... | 23,008 | [
0.03202636167407036,
0.020120147615671158,
-0.003734729252755642,
-0.05157291144132614,
0.04990759119391441,
0.04200383648276329,
0.03489961475133896,
0.03013749048113823,
-0.031715575605630875,
-0.04159628227353096,
0.028817730024456978,
0.01488490216434002,
0.050496142357587814,
-0.02920... |
https://github.com/scikit-learn/scikit-learn/issues/23008 | [
"Bug",
"Needs Triage"
] | PR #22548 breaks documentation building
### Describe the bug
The changes made in #22548 appear to cause building the example docs with `make html` to fail (the docs build fine in the previous commit).
### Steps/Code to Reproduce
1. Setup a [development environment for scikit-learn](https://github.com/data-umbrella/... | 23,008 | [
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0.01488490216434002,
0.050496142357587814,
-0.02920... |
https://github.com/scikit-learn/scikit-learn/issues/23004 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Decouple CountVectorizer => TextTokenizer + ItemCountVectorizer
### Describe the workflow you want to enable
The `CountVectorizer` component has the responsibility of not just vectorizing term frequencies but also internally tokenizing & normalizing the input text into terms. While this functionality is convenient ... | 23,004 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/23004 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Decouple CountVectorizer => TextTokenizer + ItemCountVectorizer
### Describe the workflow you want to enable
The `CountVectorizer` component has the responsibility of not just vectorizing term frequencies but also internally tokenizing & normalizing the input text into terms. While this functionality is convenient ... | 23,004 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23001 | [
"API"
] | Pandas Output Proposal Outline
With `get_feature_names_out` complete, I am currently reworking the SLEP for pandas output. I am thinking of only covering transformers in the SLEP to reduce the scope. This issue covers the complete idea for pandas output that covers all methods that return arrays: `transform`, `predict... | 23,001 | [
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0.0917391... |
https://github.com/scikit-learn/scikit-learn/issues/23001 | [
"API"
] | Pandas Output Proposal Outline
With `get_feature_names_out` complete, I am currently reworking the SLEP for pandas output. I am thinking of only covering transformers in the SLEP to reduce the scope. This issue covers the complete idea for pandas output that covers all methods that return arrays: `transform`, `predict... | 23,001 | [
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0.0917391... |
https://github.com/scikit-learn/scikit-learn/issues/23000 | [
"module:metrics"
] | completeness score (v-measure) for trivial clustering is non-zero
### Describe the bug
If I calculate the completeness score for a trivial clustering (as many clusters as datapoints) I would expect the completness score to be 0.0, however, I get non-zero values if I have more than one class (true labels).
### Steps/... | 23,000 | [
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0.0465053915977478,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/23000 | [
"module:metrics"
] | completeness score (v-measure) for trivial clustering is non-zero
### Describe the bug
If I calculate the completeness score for a trivial clustering (as many clusters as datapoints) I would expect the completness score to be 0.0, however, I get non-zero values if I have more than one class (true labels).
### Steps/... | 23,000 | [
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0.012187954969704151,
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0.04796070605516434,
0.006... |
https://github.com/scikit-learn/scikit-learn/issues/22985 | [
"New Feature",
"module:neighbors"
] | Compute centroids using median for metric = 'cosine' in NearestCentroid
### Describe the workflow you want to enable
In the current NearestCentroid code, we get centroids computed using median only for metric = 'manhattan'.
If enabling centroids computation using median for metric = 'cosine' will help in online pred... | 22,985 | [
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0.023356808349490166,... |
https://github.com/scikit-learn/scikit-learn/issues/22985 | [
"New Feature",
"module:neighbors"
] | Compute centroids using median for metric = 'cosine' in NearestCentroid
### Describe the workflow you want to enable
In the current NearestCentroid code, we get centroids computed using median only for metric = 'manhattan'.
If enabling centroids computation using median for metric = 'cosine' will help in online pred... | 22,985 | [
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0.03168594837188721... |
https://github.com/scikit-learn/scikit-learn/issues/22985 | [
"New Feature",
"module:neighbors"
] | Compute centroids using median for metric = 'cosine' in NearestCentroid
### Describe the workflow you want to enable
In the current NearestCentroid code, we get centroids computed using median only for metric = 'manhattan'.
If enabling centroids computation using median for metric = 'cosine' will help in online pred... | 22,985 | [
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0.03162787854671478,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/22981 | [
"Bug",
"module:model_selection"
] | learning curve does not work in incremental mode with MLPRegressor
### Describe the bug
It looks as though incremental learning via the learning_curve method assumes that the estimator accepts a classes argument in **partial_fit**
### Steps/Code to Reproduce
```py
from sklearn.datasets import make_regression... | 22,981 | [
0.000651333131827414,
0.0347086563706398,
0.03242385759949684,
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0.0514245443046093,
0.10264170914888382,
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0.021576... |
https://github.com/scikit-learn/scikit-learn/issues/22981 | [
"Bug",
"module:model_selection"
] | learning curve does not work in incremental mode with MLPRegressor
### Describe the bug
It looks as though incremental learning via the learning_curve method assumes that the estimator accepts a classes argument in **partial_fit**
### Steps/Code to Reproduce
```py
from sklearn.datasets import make_regression... | 22,981 | [
0.000651333131827414,
0.0347086563706398,
0.03242385759949684,
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0.10264170914888382,
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0.021576... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
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0.08381626754999161,
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-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
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-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22979 | [
"Bug",
"module:model_selection",
"Needs Reproducible Code"
] | Bug in train_test_split
### Describe the bug
I am pretty confident I found a bug in train_test_split. When using train_test_split multiple times in a row it produces wrong indexes in X_train and y_train.
### Steps/Code to Reproduce
Using this code at the first time in Jupyter notebook produced the correct result wi... | 22,979 | [
0.02601306512951851,
0.03235630691051483,
0.004198412876576185,
0.023728491738438606,
0.03917364403605461,
-0.009149911813437939,
0.08381626754999161,
0.043007783591747284,
-0.021213293075561523,
-0.03384155407547951,
0.002195193665102124,
-0.00037488644011318684,
-0.0031723997090011835,
0... |
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