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/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27981 | [
"Bug",
"Needs Triage"
] | Nested Cross Validation using cross_validate does not show correct fitted model.
### Describe the bug
Hi all,
I am trying to do nested cross validation using for example `GridSearchCV` or `RandomizedSearchCV` together with `cross_validate`.
When using the cross_validate function together with the parameter sett... | 27,981 | [
-0.015288932248950005,
-0.06719941645860672,
0.022037670016288757,
0.03126848116517067,
0.08380097150802612,
-0.04116592928767204,
-0.0013600336387753487,
0.008336356841027737,
0.031202374026179314,
0.014256125316023827,
-0.029636632651090622,
0.06330272555351257,
0.051658935844898224,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27981 | [
"Bug",
"Needs Triage"
] | Nested Cross Validation using cross_validate does not show correct fitted model.
### Describe the bug
Hi all,
I am trying to do nested cross validation using for example `GridSearchCV` or `RandomizedSearchCV` together with `cross_validate`.
When using the cross_validate function together with the parameter sett... | 27,981 | [
-0.015288932248950005,
-0.06719941645860672,
0.022037670016288757,
0.03126848116517067,
0.08380097150802612,
-0.04116592928767204,
-0.0013600336387753487,
0.008336356841027737,
0.031202374026179314,
0.014256125316023827,
-0.029636632651090622,
0.06330272555351257,
0.051658935844898224,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 | [
0.01085763517767191,
0.04077191650867462,
0.04452379420399666,
0.005209136754274368,
0.03110988810658455,
-0.02746632881462574,
0.004475378897041082,
-0.0034554696176201105,
-0.031205784529447556,
-0.030989959836006165,
0.001121545908972621,
0.09886545687913895,
-0.006857297383248806,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 | [
0.01085763517767191,
0.04077191650867462,
0.04452379420399666,
0.005209136754274368,
0.03110988810658455,
-0.02746632881462574,
0.004475378897041082,
-0.0034554696176201105,
-0.031205784529447556,
-0.030989959836006165,
0.001121545908972621,
0.09886545687913895,
-0.006857297383248806,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/27977 | [
"New Feature",
"Metadata Routing"
] | Routing metadata to the `response_method` used by a scorer
### Describe the workflow you want to enable
I would like to pass sample properties to the response method (eg `predict`) called by a scorer.
For example, the `fairlearn` package has a `ThresholdOptimizer` estimator which needs (in addition to X and y) the `... | 27,977 | [
0.01085763517767191,
0.04077191650867462,
0.04452379420399666,
0.005209136754274368,
0.03110988810658455,
-0.02746632881462574,
0.004475378897041082,
-0.0034554696176201105,
-0.031205784529447556,
-0.030989959836006165,
0.001121545908972621,
0.09886545687913895,
-0.006857297383248806,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 | [
0.019821086898446083,
0.06703455001115799,
0.013955383561551571,
-0.025674762204289436,
0.0053297048434615135,
-0.006544027011841536,
-0.008009363897144794,
-0.03790145739912987,
-0.024996010586619377,
-0.029453568160533905,
0.03887852653861046,
0.01429346390068531,
0.05763602629303932,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 | [
0.019821086898446083,
0.06703455001115799,
0.013955383561551571,
-0.025674762204289436,
0.0053297048434615135,
-0.006544027011841536,
-0.008009363897144794,
-0.03790145739912987,
-0.024996010586619377,
-0.029453568160533905,
0.03887852653861046,
0.01429346390068531,
0.05763602629303932,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 | [
0.019821086898446083,
0.06703455001115799,
0.013955383561551571,
-0.025674762204289436,
0.0053297048434615135,
-0.006544027011841536,
-0.008009363897144794,
-0.03790145739912987,
-0.024996010586619377,
-0.029453568160533905,
0.03887852653861046,
0.01429346390068531,
0.05763602629303932,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 | [
0.019821086898446083,
0.06703455001115799,
0.013955383561551571,
-0.025674762204289436,
0.0053297048434615135,
-0.006544027011841536,
-0.008009363897144794,
-0.03790145739912987,
-0.024996010586619377,
-0.029453568160533905,
0.03887852653861046,
0.01429346390068531,
0.05763602629303932,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/27973 | [
"Bug"
] | Bug in utils/multiclass.py/_ovr_decision_function
### Describe the workflow you want to enable
Dear scikit learn developpers,
I think the implementation of `_ovr_decision_function` in utils
/multiclass.py doesn't work properly when the parameter `confidences` is probability. While as the documentation suggests, i... | 27,973 | [
0.019821086898446083,
0.06703455001115799,
0.013955383561551571,
-0.025674762204289436,
0.0053297048434615135,
-0.006544027011841536,
-0.008009363897144794,
-0.03790145739912987,
-0.024996010586619377,
-0.029453568160533905,
0.03887852653861046,
0.01429346390068531,
0.05763602629303932,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 | [
-0.002915759105235338,
0.0021248203702270985,
-0.00973176583647728,
0.00420570420101285,
-0.04430472478270531,
-0.00465172016993165,
0.0555749349296093,
-0.03158484399318695,
-0.012904767878353596,
-0.012671778909862041,
0.06201740726828575,
-0.01897040568292141,
0.0466620959341526,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 | [
-0.0031534447334706783,
-0.008101731538772583,
-0.014749870635569096,
0.023273678496479988,
-0.051457930356264114,
-0.01641756482422352,
0.05643085762858391,
-0.03743082657456398,
-0.014015697874128819,
-0.00485101668164134,
0.07547330111265182,
0.000041167379094986245,
0.031968943774700165,... |
https://github.com/scikit-learn/scikit-learn/issues/27972 | [
"Bug",
"Documentation"
] | Is the time complexity of neural network in the doc right?
### Describe the issue linked to the documentation
Are you sure the [time complexity](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity) is right? Exponential complexity with respect to the number of layers rather than polyn... | 27,972 | [
-0.0009901138255372643,
-0.00958682969212532,
-0.008465941995382309,
0.02058960683643818,
-0.045422036200761795,
-0.007317105308175087,
0.058774806559085846,
-0.03894051909446716,
-0.013929836452007294,
-0.010013206861913204,
0.07147706300020218,
-0.0018268892308697104,
0.024644076824188232,... |
https://github.com/scikit-learn/scikit-learn/issues/27968 | [
"Documentation",
"Needs Triage"
] | DOC doc build sphinx version link out-dated again
### Describe the issue linked to the documentation
The link to the sphinx versions for doc build at the end of [*Building the documentation*](https://scikit-learn.org/dev/developers/contributing.html#building-the-documentation) is again out-dated, with sphinx version ... | 27,968 | [
0.05848001316189766,
0.03334157168865204,
-0.023345835506916046,
-0.020647887140512466,
0.004136559087783098,
0.02431020326912403,
-0.00021153021953068674,
0.0372605174779892,
-0.02540045790374279,
-0.05524026229977608,
0.02945484034717083,
0.029428424313664436,
0.038759272545576096,
-0.06... |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 | [
-0.0241744015365839,
-0.049133237451314926,
0.03259953483939171,
0.0009398284601047635,
0.06852216273546219,
-0.014446164481341839,
0.07122509181499481,
0.014603795483708382,
-0.026277590543031693,
0.03211104869842529,
-0.0002686537627596408,
0.09909708052873611,
0.05197189003229141,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 | [
-0.0241744015365839,
-0.049133237451314926,
0.03259953483939171,
0.0009398284601047635,
0.06852216273546219,
-0.014446164481341839,
0.07122509181499481,
0.014603795483708382,
-0.026277590543031693,
0.03211104869842529,
-0.0002686537627596408,
0.09909708052873611,
0.05197189003229141,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 | [
-0.0241744015365839,
-0.049133237451314926,
0.03259953483939171,
0.0009398284601047635,
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0.07122509181499481,
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0.03211104869842529,
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0.09909708052873611,
0.05197189003229141,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27964 | [
"Bug"
] | Correct scale back for PLS regression coefficients
### Describe the bug
In `cross_decomposition/_pls.py`, PLS regression coefficients are calculated in class `_PLS` (starts at line 165). In this class, when `scale=True`, data are scaled (on line 265). In that case, the resulting regression coefficients need to be s... | 27,964 | [
-0.0241744015365839,
-0.049133237451314926,
0.03259953483939171,
0.0009398284601047635,
0.06852216273546219,
-0.014446164481341839,
0.07122509181499481,
0.014603795483708382,
-0.026277590543031693,
0.03211104869842529,
-0.0002686537627596408,
0.09909708052873611,
0.05197189003229141,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27959 | [
"New Feature",
"Needs Triage"
] | PR: Polynomial Chaos Expansions with no responses???
### Describe the workflow you want to enable
.
### Describe your proposed solution
.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
Why no one comment this PR https://github.com/scikit-learn/scikit-learn/pull/278... | 27,959 | [
0.0015380623517557979,
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... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.04721513018012047,
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0.002958722645416856,
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0.03084045834839344,
0.0633731260895729,
0.0045048450119793415,
0.03097466565668583,
0.136... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.057152699679136276,
-0.009636731818318367,
0.002547639887779951,
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0.03323006629943848,
0.05716845765709877,
0.012828399427235126,
0.029098128899931908,
0.13... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
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0.005559967830777168,
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0.02617097832262516,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05002794787287712,
0.0031266724690794945,
0.01608816161751747,
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0.0868435800075531,
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0.04921351745724678,
0.06032615527510643,
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0.04161045700311661,
0.126656... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
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0.0013628449523821473,
0.030834538862109184,
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0.07311175018548965,
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0.02604788914322853,
0.0461517758667469,
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0.023295586928725243,
0.1... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06686638295650482,
0.028309835121035576,
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0.006329602096229792,
0.07460741698741913,
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0.051912907510995865,
0.06236625462770462,
0.005128222517669201,
0.02317904308438301,
0.1281491816... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
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0.007704926189035177,
0.0012839139671996236,
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0.03512343019247055,
0.03899117931723595,
0.00929260067641735,
0.02675701305270195,
0.13085... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.03496573492884636,
0.0079716257750988,
0.011613617651164532,
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0.025158243253827095,
0.027001507580280304,
0.07023352384567261,
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0.045511987060308456,
0.014961278066039085,
-0.0006951501709409058,
0.0268345195800066,
0.12... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05817870795726776,
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0.002030032454058528,
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0.026262374594807625,
0.07165885716676712,
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0.0382431298494339,
0.061477046459913254,
0.008613752201199532,
0.031690843403339386,
0.132... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.047343138605356216,
-0.002938235178589821,
0.022197820246219635,
-0.057807765901088715,
0.04498985409736633,
0.041752055287361145,
0.06755769997835159,
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0.014536143280565739,
0.023162338882684708,
0.009204319678246975,
0.039349425584077835,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05480031669139862,
0.00789773277938366,
0.010508827865123749,
-0.07357057929039001,
0.01157982274889946,
0.03270505741238594,
0.06959262490272522,
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0.02458658255636692,
0.04950892552733421,
-0.005884832236915827,
0.03954900801181793,
0.1324673... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.04297146201133728,
0.018909232690930367,
0.019173748791217804,
-0.0736568346619606,
0.020742220804095268,
0.028710627928376198,
0.06599713861942291,
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0.018631333485245705,
0.031569983810186386,
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0.03601749241352081,
0.129... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.02844257652759552,
0.018280301243066788,
0.025883886963129044,
-0.0655038058757782,
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0.03463038057088852,
0.06646376103162766,
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0.01813054457306862,
0.03677326813340187,
0.019891967996954918,
0.033969443291425705,
0.128090... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06371619552373886,
-0.0044631860218942165,
0.004243580158799887,
-0.0782819390296936,
0.003083714982494712,
0.020741775631904602,
0.07923322170972824,
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0.02966706082224846,
0.043695952743291855,
-0.005494436714798212,
0.03981047123670578,
0.13... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06243303790688515,
0.00201919162645936,
0.005350017454475164,
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0.008709706366062164,
0.029213201254606247,
0.07918549329042435,
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0.028242599219083786,
0.040429677814245224,
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0.03406381979584694,
0.131... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06713244318962097,
-0.008765519596636295,
0.0033033962827175856,
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0.011132624931633472,
0.02299370989203453,
0.0653095692396164,
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0.0370277538895607,
0.048850253224372864,
0.003109286306425929,
0.03965335711836815,
0.13131... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.048954520374536514,
0.004334965255111456,
0.014251051470637321,
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0.024092240259051323,
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0.08562726527452469,
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0.012362338602542877,
0.02795427292585373,
-0.003827271517366171,
0.04163782298564911,
0.11... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.04415423050522804,
0.00486460467800498,
0.02669871412217617,
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0.07720762491226196,
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0.04608946666121483,
0.018588624894618988,
0.017101997509598732,
0.040637578815221786,
0.1151614... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.049752358347177505,
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0.017475072294473648,
0.08203389495611191,
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0.023240836337208748,
0.045332372188568115,
-0.01189375203102827,
0.03746598958969116,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06486757844686508,
-0.009111858904361725,
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0.0020250247325748205,
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0.07480525970458984,
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0.03231104835867882,
0.04767001420259476,
0.0039108372293412685,
0.03533603996038437,
0.14... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05856902897357941,
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0.00480275321751833,
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0.0062493388541042805,
0.034115854650735855,
0.07177508622407913,
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0.02720324695110321,
0.047548215836286545,
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0.04182528704404831,
0.1... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05035725608468056,
-0.012689506635069847,
0.02145112119615078,
-0.06701024621725082,
0.022971030324697495,
0.021012306213378906,
0.06607566773891449,
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0.010568496771156788,
0.03552839532494545,
0.05376461148262024,
0.01711777225136757,
0.043464645743370056,
0.1259214... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05443835258483887,
-0.010657215490937233,
-0.013373768888413906,
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0.012432772666215897,
0.07915759831666946,
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0.03233170881867409,
0.056888192892074585,
0.0007711265352554619,
0.036001238971948624,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.03125237673521042,
0.010808347724378109,
-0.0021906481124460697,
-0.09230295568704605,
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0.011010879650712013,
0.06306332349777222,
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0.030421553179621696,
0.07693839073181152,
-0.013417462818324566,
0.0259608943015337,
0.1... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05702829733490944,
-0.017186107113957405,
-0.010812687687575817,
-0.08589407801628113,
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0.025864994153380394,
0.07622881233692169,
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0.03354663401842117,
0.06356608867645264,
0.003524522762745619,
0.03786793351173401,
0.13438... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.05795843154191971,
-0.02092338539659977,
0.0012232904555276036,
-0.08590929210186005,
0.0037944854702800512,
0.0276663675904274,
0.07312896847724915,
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0.0345509834587574,
0.054842036217451096,
-0.000661501195281744,
0.03496033698320389,
0.1342... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06962695717811584,
-0.019473759457468987,
-0.0003235609328839928,
-0.08301975578069687,
-0.0029307734221220016,
0.03159252554178238,
0.07104003429412842,
-0.035348664969205856,
-0.017266947776079178,
0.034977223724126816,
0.056481778621673584,
0.003129322314634919,
0.03520270809531212,
... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.06619369238615036,
-0.021151628345251083,
-0.0016293120570480824,
-0.08468957245349884,
0.005519844591617584,
0.024865010753273964,
0.0640602856874466,
-0.03657842054963112,
-0.01908639632165432,
0.0323563851416111,
0.06268709897994995,
0.003775492310523987,
0.03272148594260216,
0.13964... |
https://github.com/scikit-learn/scikit-learn/issues/27957 | [
"New Feature"
] | Standard "Total Variance" Scaler
### Desired feature
A preprocessor that removes the mean for each feature, and then scales the total variance of the dataset, rather than the variance of each feature, to 1.
### Proposed Solution
A new preprocessor that operates like StandardScaler but automatically scales tot... | 27,957 | [
-0.0644635334610939,
-0.005357956979423761,
-0.009115422144532204,
-0.07538719475269318,
0.009635246358811855,
0.02783888578414917,
0.06342937797307968,
-0.04341672360897064,
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0.03588145971298218,
0.0417892262339592,
0.005045953206717968,
0.021961666643619537,
0.14363... |
https://github.com/scikit-learn/scikit-learn/issues/27955 | [
"New Feature",
"Needs Triage"
] | Unable to control warning logs generated by GridSearchCV fit method when setting n_jobs to >1 for parallel processing
### Describe the workflow you want to enable
I am running GridSearchCV with n_jobs set to value which is > 1. The grid search is writing log of convergence and other warnings to the console. I want to... | 27,955 | [
-0.03614266961812973,
0.024682477116584778,
0.006038789637386799,
0.004504545591771603,
0.04594431445002556,
0.0027924608439207077,
-0.004208390135318041,
0.045792728662490845,
0.037272192537784576,
0.025330709293484688,
0.049198247492313385,
0.05284715071320534,
-0.08409494161605835,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 | [
-0.005432166624814272,
-0.03260962292551994,
0.036195073276758194,
-0.03853771090507507,
0.06311050802469254,
0.007999449968338013,
-0.00045584081090055406,
-0.018822088837623596,
0.01966000720858574,
0.001453123171813786,
0.01355157420039177,
0.007680133916437626,
-0.013934893533587456,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 | [
-0.005432166624814272,
-0.03260962292551994,
0.036195073276758194,
-0.03853771090507507,
0.06311050802469254,
0.007999449968338013,
-0.00045584081090055406,
-0.018822088837623596,
0.01966000720858574,
0.001453123171813786,
0.01355157420039177,
0.007680133916437626,
-0.013934893533587456,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 | [
-0.005432166624814272,
-0.03260962292551994,
0.036195073276758194,
-0.03853771090507507,
0.06311050802469254,
0.007999449968338013,
-0.00045584081090055406,
-0.018822088837623596,
0.01966000720858574,
0.001453123171813786,
0.01355157420039177,
0.007680133916437626,
-0.013934893533587456,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 | [
-0.005432166624814272,
-0.03260962292551994,
0.036195073276758194,
-0.03853771090507507,
0.06311050802469254,
0.007999449968338013,
-0.00045584081090055406,
-0.018822088837623596,
0.01966000720858574,
0.001453123171813786,
0.01355157420039177,
0.007680133916437626,
-0.013934893533587456,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27953 | [
"Bug",
"Needs Triage"
] | CalibratedClassifierCV gives a NotFittedError when accessing the underlying XGBoostClassifier feature_importances property
### Describe the bug
I am using CalibratedClassifierCV and XGBoost in a Pipeline and was able to train the model and use it to make predictions, etc. But I cannot access the underlying property o... | 27,953 | [
-0.005432166624814272,
-0.03260962292551994,
0.036195073276758194,
-0.03853771090507507,
0.06311050802469254,
0.007999449968338013,
-0.00045584081090055406,
-0.018822088837623596,
0.01966000720858574,
0.001453123171813786,
0.01355157420039177,
0.007680133916437626,
-0.013934893533587456,
0... |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 | [
-0.021432342007756233,
0.020751483738422394,
0.014426548965275288,
0.02072552591562271,
0.03911592811346054,
-0.006024148315191269,
0.03814729303121567,
0.057970739901065826,
-0.0103554492816329,
-0.02841597981750965,
-0.0208680871874094,
0.04363898187875748,
-0.002572041703388095,
0.01365... |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 | [
-0.021432342007756233,
0.020751483738422394,
0.014426548965275288,
0.02072552591562271,
0.03911592811346054,
-0.006024148315191269,
0.03814729303121567,
0.057970739901065826,
-0.0103554492816329,
-0.02841597981750965,
-0.0208680871874094,
0.04363898187875748,
-0.002572041703388095,
0.01365... |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 | [
-0.021432342007756233,
0.020751483738422394,
0.014426548965275288,
0.02072552591562271,
0.03911592811346054,
-0.006024148315191269,
0.03814729303121567,
0.057970739901065826,
-0.0103554492816329,
-0.02841597981750965,
-0.0208680871874094,
0.04363898187875748,
-0.002572041703388095,
0.01365... |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 | [
-0.021432342007756233,
0.020751483738422394,
0.014426548965275288,
0.02072552591562271,
0.03911592811346054,
-0.006024148315191269,
0.03814729303121567,
0.057970739901065826,
-0.0103554492816329,
-0.02841597981750965,
-0.0208680871874094,
0.04363898187875748,
-0.002572041703388095,
0.01365... |
https://github.com/scikit-learn/scikit-learn/issues/27952 | [
"Bug"
] | HistGradientBoosting pickle portability between 64bit and 32bit arch
### Describe the bug
HistGradinetBoosting models use ```np.intp``` to represent the ```feature_idx``` in TreePredictor nodes
https://github.com/scikit-learn/scikit-learn/blob/0f8a7775ad248b9aa4be63291ae71d9212a46e6c/sklearn/ensemble/_hist_gradien... | 27,952 | [
-0.021432342007756233,
0.020751483738422394,
0.014426548965275288,
0.02072552591562271,
0.03911592811346054,
-0.006024148315191269,
0.03814729303121567,
0.057970739901065826,
-0.0103554492816329,
-0.02841597981750965,
-0.0208680871874094,
0.04363898187875748,
-0.002572041703388095,
0.01365... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27948 | [
"Bug"
] | Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### Describe the bug
## Pairwise distances (single precision) throwing seg fault on AWS c6i.metal instances
### The Issue
Applying pairwise (Euclidean) distances on a matrix of size 5000x5000.
```python
import numpy as np
... | 27,948 | [
-0.04257705807685852,
-0.035265371203422546,
-0.028354765847325325,
0.03742600604891777,
0.03898504748940468,
0.015628090128302574,
0.04303673654794693,
0.021757211536169052,
-0.03433915600180626,
-0.016120851039886475,
0.011278741993010044,
0.03494514897465706,
-0.0027352869510650635,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 | [
-0.000514100946020335,
0.1113428846001625,
0.021463261917233467,
-0.04300476610660553,
0.05453065410256386,
0.017325859516859055,
0.03473539650440216,
0.04171708598732948,
-0.08386479318141937,
-0.00026823108782991767,
0.03687431290745735,
-0.05656035989522934,
-0.031300511211156845,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 | [
-0.000514100946020335,
0.1113428846001625,
0.021463261917233467,
-0.04300476610660553,
0.05453065410256386,
0.017325859516859055,
0.03473539650440216,
0.04171708598732948,
-0.08386479318141937,
-0.00026823108782991767,
0.03687431290745735,
-0.05656035989522934,
-0.031300511211156845,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 | [
-0.000514100946020335,
0.1113428846001625,
0.021463261917233467,
-0.04300476610660553,
0.05453065410256386,
0.017325859516859055,
0.03473539650440216,
0.04171708598732948,
-0.08386479318141937,
-0.00026823108782991767,
0.03687431290745735,
-0.05656035989522934,
-0.031300511211156845,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/27947 | [
"New Feature"
] | Allowing to group infrequent categories in `HistGradientBoosting`
### Describe the workflow you want to enable
`HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` have built-in support for categorical features and use an `OrdinalEncoder` to encode them. Each feature must have less than `max_bins` (25... | 27,947 | [
-0.000514100946020335,
0.1113428846001625,
0.021463261917233467,
-0.04300476610660553,
0.05453065410256386,
0.017325859516859055,
0.03473539650440216,
0.04171708598732948,
-0.08386479318141937,
-0.00026823108782991767,
0.03687431290745735,
-0.05656035989522934,
-0.031300511211156845,
0.009... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27931 | [
"New Feature",
"module:tree"
] | ENH support for missing values in ExtraTrees
### Describe the workflow you want to enable
Inspired by https://github.com/scikit-learn/scikit-learn/pull/26391 I think that support for missing values for ExtraTrees regressor and classifier should/could also be provided.
### Describe your proposed solution
I think a ... | 27,931 | [
0.010632463730871677,
0.0828951820731163,
0.02625693753361702,
-0.0062447842210531235,
0.06351157277822495,
-0.01101621799170971,
-0.03059149906039238,
0.009893112815916538,
-0.03504788130521774,
0.011584067717194557,
0.029089175164699554,
0.023160995915532112,
-0.040329460054636,
0.041828... |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 | [
0.04565773159265518,
0.034861188381910324,
0.010868346318602562,
0.03640872240066528,
0.05417148396372795,
-0.0051070889458060265,
0.0364585779607296,
0.008223449811339378,
-0.04419859126210213,
-0.016806969419121742,
0.03047599457204342,
0.0659344345331192,
0.00004234463267493993,
0.06101... |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 | [
0.04565773159265518,
0.034861188381910324,
0.010868346318602562,
0.03640872240066528,
0.05417148396372795,
-0.0051070889458060265,
0.0364585779607296,
0.008223449811339378,
-0.04419859126210213,
-0.016806969419121742,
0.03047599457204342,
0.0659344345331192,
0.00004234463267493993,
0.06101... |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 | [
0.04565773159265518,
0.034861188381910324,
0.010868346318602562,
0.03640872240066528,
0.05417148396372795,
-0.0051070889458060265,
0.0364585779607296,
0.008223449811339378,
-0.04419859126210213,
-0.016806969419121742,
0.03047599457204342,
0.0659344345331192,
0.00004234463267493993,
0.06101... |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 | [
0.04565773159265518,
0.034861188381910324,
0.010868346318602562,
0.03640872240066528,
0.05417148396372795,
-0.0051070889458060265,
0.0364585779607296,
0.008223449811339378,
-0.04419859126210213,
-0.016806969419121742,
0.03047599457204342,
0.0659344345331192,
0.00004234463267493993,
0.06101... |
https://github.com/scikit-learn/scikit-learn/issues/27930 | [
"Enhancement"
] | PR proposal to solve "Bunch object returns a regular dict when calling `copy` method on it"
### Describe the bug
If I do
```python
bunch = Bunch (message='hello')
should_be_bunch = bunch.copy()
print (should_be_bunch.message)
```
I get a (for me) unexpected error, because `should_be_bunch` is actually a `... | 27,930 | [
0.04565773159265518,
0.034861188381910324,
0.010868346318602562,
0.03640872240066528,
0.05417148396372795,
-0.0051070889458060265,
0.0364585779607296,
0.008223449811339378,
-0.04419859126210213,
-0.016806969419121742,
0.03047599457204342,
0.0659344345331192,
0.00004234463267493993,
0.06101... |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 | [
0.004889793694019318,
0.003171910997480154,
0.0223858579993248,
0.009778385981917381,
0.08014695346355438,
-0.01974588632583618,
-0.025870466604828835,
0.04537082463502884,
0.0020364527590572834,
0.013514839112758636,
0.027097180485725403,
-0.006413693074136972,
-0.01951937936246395,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 | [
0.004889793694019318,
0.003171910997480154,
0.0223858579993248,
0.009778385981917381,
0.08014695346355438,
-0.01974588632583618,
-0.025870466604828835,
0.04537082463502884,
0.0020364527590572834,
0.013514839112758636,
0.027097180485725403,
-0.006413693074136972,
-0.01951937936246395,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 | [
0.004889793694019318,
0.003171910997480154,
0.0223858579993248,
0.009778385981917381,
0.08014695346355438,
-0.01974588632583618,
-0.025870466604828835,
0.04537082463502884,
0.0020364527590572834,
0.013514839112758636,
0.027097180485725403,
-0.006413693074136972,
-0.01951937936246395,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27928 | [
"Bug",
"help wanted"
] | LASSO Solve badly when alpha is extremely small
### Describe the bug
There are 2 problem:
- when `tol=1e-4`(default), the solver does not give a warning when it solved badly.
- when `alpha` is extrimely small (like 1e-8), the solver could not find solution properly.
### Steps/Code to Reproduce
In this case, sol... | 27,928 | [
0.004889793694019318,
0.003171910997480154,
0.0223858579993248,
0.009778385981917381,
0.08014695346355438,
-0.01974588632583618,
-0.025870466604828835,
0.04537082463502884,
0.0020364527590572834,
0.013514839112758636,
0.027097180485725403,
-0.006413693074136972,
-0.01951937936246395,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 | [
0.004135213792324066,
-0.05861535668373108,
0.026940390467643738,
0.03239798545837402,
0.06157804653048515,
0.010081687942147255,
0.05444779247045517,
0.0009595628362149,
-0.029468553140759468,
-0.007344130892306566,
0.0012931758537888527,
-0.030416050925850868,
0.05083966255187988,
0.0367... |
https://github.com/scikit-learn/scikit-learn/issues/27927 | [
"Bug"
] | `classification_report` gives micro averages when `labels` is a superset of the observed labels
### Describe the bug
When the value of the `labels` parameter is a superset of all observed classes in `y_true` and `y_pred`, `classification_report()` gives separate macro average values for precision, recall, and F1, alt... | 27,927 | [
0.004135213792324066,
-0.05861535668373108,
0.026940390467643738,
0.03239798545837402,
0.06157804653048515,
0.010081687942147255,
0.05444779247045517,
0.0009595628362149,
-0.029468553140759468,
-0.007344130892306566,
0.0012931758537888527,
-0.030416050925850868,
0.05083966255187988,
0.0367... |
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