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/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23997 | [
"Blocker",
"module:decomposition"
] | Remove `sign_flip` from FastICA
From https://github.com/scikit-learn/scikit-learn/pull/23935#discussion_r922842213, we may want to remove `sign_flip` from `FastICA`:
https://github.com/scikit-learn/scikit-learn/blob/f0af898de5bfa93916b659438b4cf0b86fffd974/sklearn/decomposition/_fastica.py#L264
This parameter ... | 23,997 | [
-0.03840930759906769,
-0.03502975404262543,
-0.01472802460193634,
0.0042966739274561405,
0.03773145377635956,
0.03427906706929207,
0.021028518676757812,
-0.007801893167197704,
-0.0004694227536674589,
-0.029332410544157028,
0.0027821273542940617,
0.021529553458094597,
0.02474881522357464,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23988 | [
"Bug"
] | birch clustering does not work with version 1.1.1
### Describe the bug
Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)... | 23,988 | [
0.00030759762739762664,
-0.05845442786812782,
0.011276142671704292,
0.0028620921075344086,
0.05099194124341011,
-0.015031182207167149,
0.028568996116518974,
0.04792744666337967,
0.031011076644062996,
0.03169584274291992,
0.013568336144089699,
0.05174872279167175,
0.009592759422957897,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23983 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Add option to RocCurveDisplay to display the average of different length ROC curves
### Describe the workflow you want to enable
When using k-fold cross-validation the resulting ROC curves can vary in length if there are a different number of positive and/or negative samples in each fold. I would like to add an opt... | 23,983 | [
-0.06272134929895401,
-0.07994581013917923,
-0.010481510311365128,
0.0312877893447876,
0.011875590309500694,
0.004969662055373192,
0.007926493883132935,
-0.013327112421393394,
0.023384446278214455,
0.0015522007597610354,
0.009986883029341698,
0.04792546108365059,
-0.013256410136818886,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23976 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44954&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Jul 24, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no l... | 23,976 | [
0.025235148146748543,
0.009789492003619671,
-0.021571870893239975,
-0.08317974954843521,
0.019548136740922928,
0.025614742189645767,
0.024961519986391068,
0.0490243174135685,
0.011844314634799957,
0.043724868446588516,
0.034796614199876785,
0.034084104001522064,
-0.023552365601062775,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/23976 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI is still failing on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44954&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Jul 24, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
Closing since... | 23,976 | [
0.02886711247265339,
-0.0066207097843289375,
-0.020493637770414352,
-0.08298508822917938,
0.02194383181631565,
0.018418442457914352,
0.024163881316781044,
0.04576035216450691,
0.014904684387147427,
0.03695979714393616,
0.05303562432527542,
0.04786595329642296,
-0.019734248518943787,
0.0452... |
https://github.com/scikit-learn/scikit-learn/issues/23970 | [
"module:linear_model",
"Needs Investigation"
] | Test Failed on CICD Pipeline with varied LinearRegression predictions
- Ref: https://stackoverflow.com/questions/73067554/test-failed-on-cicd-pipeline-with-varied-linearregression-predictions
We have a very simple use case, when we take some data `X`, `y` and fir it to `LinearRegression()` like below. and try to pr... | 23,970 | [
-0.034642986953258514,
0.04984026402235031,
0.004554274026304483,
0.04985061287879944,
0.038326047360897064,
-0.017112189903855324,
0.06043893098831177,
0.04078805819153786,
0.021604588255286217,
0.03617873042821884,
0.0364859513938427,
-0.013646306470036507,
0.004837170708924532,
0.067250... |
https://github.com/scikit-learn/scikit-learn/issues/23970 | [
"module:linear_model",
"Needs Investigation"
] | Test Failed on CICD Pipeline with varied LinearRegression predictions
- Ref: https://stackoverflow.com/questions/73067554/test-failed-on-cicd-pipeline-with-varied-linearregression-predictions
We have a very simple use case, when we take some data `X`, `y` and fir it to `LinearRegression()` like below. and try to pr... | 23,970 | [
-0.042874474078416824,
0.03939478471875191,
-0.00782806146889925,
0.026430601254105568,
0.039326976984739304,
-0.020281841978430748,
0.049370910972356796,
0.03265008702874184,
-0.0011077418457716703,
0.058321040123701096,
0.03447212278842926,
-0.011367314495146275,
0.006886070128530264,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23970 | [
"module:linear_model",
"Needs Investigation"
] | Test Failed on CICD Pipeline with varied LinearRegression predictions
- Ref: https://stackoverflow.com/questions/73067554/test-failed-on-cicd-pipeline-with-varied-linearregression-predictions
We have a very simple use case, when we take some data `X`, `y` and fir it to `LinearRegression()` like below. and try to pr... | 23,970 | [
-0.05793716385960579,
0.05553361773490906,
-0.00950041227042675,
0.05171829089522362,
0.029954932630062103,
-0.029802005738019943,
0.05890186131000519,
0.032850272953510284,
0.010764194652438164,
0.04640278220176697,
0.0204462967813015,
-0.026200484484434128,
-0.00301310489885509,
0.056522... |
https://github.com/scikit-learn/scikit-learn/issues/23970 | [
"module:linear_model",
"Needs Investigation"
] | Test Failed on CICD Pipeline with varied LinearRegression predictions
- Ref: https://stackoverflow.com/questions/73067554/test-failed-on-cicd-pipeline-with-varied-linearregression-predictions
We have a very simple use case, when we take some data `X`, `y` and fir it to `LinearRegression()` like below. and try to pr... | 23,970 | [
-0.026900773867964745,
0.05542346462607384,
0.0017473562620580196,
0.04386153817176819,
0.04203329607844353,
-0.010128633119165897,
0.06937926262617111,
0.03355596587061882,
0.013620629906654358,
0.03793039545416832,
0.04859726130962372,
-0.023798517882823944,
0.00025279945111833513,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03690822422504425,
-0.0020214435644447803,
-0.023580124601721764,
-0.023576516658067703,
0.028474697843194008,
0.013358279131352901,
0.04573961719870567,
-0.02277202531695366,
0.0528499074280262,
0.04352815821766853,
0.037707503885030746,
0.03132542595267296,
0.015493569895625114,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.036317773163318634,
0.016637660562992096,
-0.020635584369301796,
-0.022858548909425735,
0.0367894321680069,
0.010685286484658718,
0.061774883419275284,
-0.02645096369087696,
0.05574444308876991,
0.04064992442727089,
0.032515592873096466,
0.03615165501832962,
0.01028676237910986,
-0.0268... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03560362756252289,
0.017546242102980614,
-0.0206146240234375,
-0.023010913282632828,
0.036870818585157394,
0.010555128566920757,
0.06202315539121628,
-0.026417501270771027,
0.056862570345401764,
0.04068372771143913,
0.032342590391635895,
0.0369933657348156,
0.009122921153903008,
-0.0270... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03860671818256378,
-0.01557755284011364,
-0.018601803109049797,
-0.0235811285674572,
0.03456219285726547,
0.0149281220510602,
0.046187229454517365,
-0.026353906840085983,
0.05066584050655365,
0.038234904408454895,
0.030400406569242477,
0.042124032974243164,
0.0191554743796587,
-0.017479... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.033468540757894516,
-0.01484702993184328,
-0.023759182542562485,
-0.0037063537165522575,
0.047745101153850555,
0.011956901289522648,
0.03674007207155228,
-0.029047086834907532,
0.0402948297560215,
0.022731352597475052,
0.028742382302880287,
0.04610833153128624,
0.015583556145429611,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.031481094658374786,
0.005584420636296272,
-0.022532368078827858,
-0.01437301840633154,
0.022999949753284454,
0.008496868424117565,
0.05255436897277832,
-0.02791699767112732,
0.05996672809123993,
0.04449576139450073,
0.028028929606080055,
0.03478804603219032,
0.0016607893630862236,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03608224168419838,
-0.010666304267942905,
-0.00879010371863842,
0.000696912466082722,
0.019746901467442513,
0.012745688669383526,
0.045222923159599304,
-0.026641184464097023,
0.05650414153933525,
0.03304826840758324,
0.01501025352627039,
0.025370772927999496,
-0.01821991428732872,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03778834268450737,
0.0017130803316831589,
-0.024895265698432922,
-0.01601746305823326,
0.029665455222129822,
0.01582919806241989,
0.04974038898944855,
-0.024347318336367607,
0.05194059759378433,
0.042438652366399765,
0.033587802201509476,
0.02830818109214306,
0.004996166098862886,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.033551134169101715,
-0.013160665519535542,
-0.02268543466925621,
-0.023674383759498596,
0.018377922475337982,
0.019977599382400513,
0.04731583595275879,
-0.026677483692765236,
0.03726561740040779,
0.040255606174468994,
0.03594357520341873,
0.029260359704494476,
0.022835154086351395,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03469886630773544,
0.0055626435205340385,
-0.021501759067177773,
-0.031388018280267715,
0.019140779972076416,
0.017652934417128563,
0.050594761967659,
-0.024277614429593086,
0.05111983045935631,
0.0432327501475811,
0.037626586854457855,
0.028718339279294014,
0.015288502909243107,
-0.019... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.035073377192020416,
0.004986817482858896,
-0.02186284400522709,
-0.031280048191547394,
0.019063618034124374,
0.018002385273575783,
0.051145702600479126,
-0.02502567321062088,
0.05029976740479469,
0.04313226789236069,
0.036958247423172,
0.026932567358016968,
0.016196822747588158,
-0.0199... |
https://github.com/scikit-learn/scikit-learn/issues/23967 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Unsupervised K-Means Clustering Algorithm
### Describe the workflow you want to enable
"the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we c... | 23,967 | [
-0.03956862911581993,
-0.013573624193668365,
-0.023799337446689606,
-0.02168133109807968,
0.02800028957426548,
0.014241117984056473,
0.04680797457695007,
-0.02647537738084793,
0.05066314712166786,
0.04488740861415863,
0.03561735898256302,
0.03583197295665741,
0.018940357491374016,
-0.02326... |
https://github.com/scikit-learn/scikit-learn/issues/23963 | [
"Bug",
"module:preprocessing",
"Needs Investigation"
] | MinMaxScaler on Loaded DataFrame fails with TypeError : NoneType
### Describe the bug
Hey, I've been re-running my code on a different machine and I realized my old version was 0.24.1 but in the latest version of 1.02 , in the [_handle_zeros_in_scale](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/pre... | 23,963 | [
-0.012150664813816547,
-0.02105514332652092,
0.034186773002147675,
-0.036126986145973206,
0.07080695778131485,
0.004113366361707449,
0.08396018296480179,
0.06294649094343185,
-0.008516917936503887,
-0.02514929138123989,
0.045481327921152115,
-0.010953841730952263,
0.009813005104660988,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23963 | [
"Bug",
"module:preprocessing",
"Needs Investigation"
] | MinMaxScaler on Loaded DataFrame fails with TypeError : NoneType
### Describe the bug
Hey, I've been re-running my code on a different machine and I realized my old version was 0.24.1 but in the latest version of 1.02 , in the [_handle_zeros_in_scale](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/pre... | 23,963 | [
-0.012150664813816547,
-0.02105514332652092,
0.034186773002147675,
-0.036126986145973206,
0.07080695778131485,
0.004113366361707449,
0.08396018296480179,
0.06294649094343185,
-0.008516917936503887,
-0.02514929138123989,
0.045481327921152115,
-0.010953841730952263,
0.009813005104660988,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23963 | [
"Bug",
"module:preprocessing",
"Needs Investigation"
] | MinMaxScaler on Loaded DataFrame fails with TypeError : NoneType
### Describe the bug
Hey, I've been re-running my code on a different machine and I realized my old version was 0.24.1 but in the latest version of 1.02 , in the [_handle_zeros_in_scale](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/pre... | 23,963 | [
-0.012150664813816547,
-0.02105514332652092,
0.034186773002147675,
-0.036126986145973206,
0.07080695778131485,
0.004113366361707449,
0.08396018296480179,
0.06294649094343185,
-0.008516917936503887,
-0.02514929138123989,
0.045481327921152115,
-0.010953841730952263,
0.009813005104660988,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23963 | [
"Bug",
"module:preprocessing",
"Needs Investigation"
] | MinMaxScaler on Loaded DataFrame fails with TypeError : NoneType
### Describe the bug
Hey, I've been re-running my code on a different machine and I realized my old version was 0.24.1 but in the latest version of 1.02 , in the [_handle_zeros_in_scale](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/pre... | 23,963 | [
-0.012150664813816547,
-0.02105514332652092,
0.034186773002147675,
-0.036126986145973206,
0.07080695778131485,
0.004113366361707449,
0.08396018296480179,
0.06294649094343185,
-0.008516917936503887,
-0.02514929138123989,
0.045481327921152115,
-0.010953841730952263,
0.009813005104660988,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23939 | [
"New Feature",
"module:naive_bayes",
"Needs Decision - Include Feature"
] | Sparse Bernoulli Naive Bayes
### Describe the workflow you want to enable
The idea seems simple enough as described in this paper: https://arxiv.org/pdf/1905.09884.pdf
> Due to its linear complexity, naive Bayes classification remains an attractive su-
> pervised learning method, especially in very large-scale ... | 23,939 | [
-0.01694989576935768,
0.08301996439695358,
0.013668187893927097,
-0.01763536036014557,
-0.02462766133248806,
0.02553558722138405,
0.06132945045828819,
0.06063098460435867,
0.040855199098587036,
-0.07652651518583298,
0.057916950434446335,
-0.03564944490790367,
-0.045262500643730164,
0.05463... |
https://github.com/scikit-learn/scikit-learn/issues/23939 | [
"New Feature",
"module:naive_bayes",
"Needs Decision - Include Feature"
] | Sparse Bernoulli Naive Bayes
### Describe the workflow you want to enable
The idea seems simple enough as described in this paper: https://arxiv.org/pdf/1905.09884.pdf
> Due to its linear complexity, naive Bayes classification remains an attractive su-
> pervised learning method, especially in very large-scale ... | 23,939 | [
-0.0071209874004125595,
0.0711606815457344,
0.013652587309479713,
-0.012502442114055157,
-0.02564711682498455,
0.026714099571108818,
0.05392502248287201,
0.0573793426156044,
0.04268913343548775,
-0.0827583447098732,
0.0619635172188282,
-0.02613328956067562,
-0.025958171114325523,
0.0361582... |
https://github.com/scikit-learn/scikit-learn/issues/23933 | [
"New Feature"
] | SLEP006: mypy can't find `set_{method}_request` methods
`set_{method}_request` methods are added to all estimators inheriting from `BaseEstimator`, around this part of the code:
https://github.com/scikit-learn/scikit-learn/blob/e9f5676c5ed025d0e71c0a3970d8544a25075933/sklearn/utils/_metadata_requests.py#L1004-L1013... | 23,933 | [
0.010927489027380943,
0.06734371185302734,
0.03382693976163864,
0.01880563609302044,
0.08984053879976273,
-0.0028406870551407337,
0.03690190985798836,
0.038097456097602844,
0.05340404435992241,
-0.04367687553167343,
0.021556062623858452,
0.07591357827186584,
-0.015102112665772438,
0.026178... |
https://github.com/scikit-learn/scikit-learn/issues/23932 | [
"Needs Triage"
] | Inconsistence between components in `PCA` and `SparsePCA`
The components provided by `PCA` and `SparsePCA` are not consistent when we explicitly do not impose any sparsity constraint in `SparsePCA`. It is only a sign flipped meaning that this might not be dramatic.
```python
import numpy as np
from sklearn.decomp... | 23,932 | [
0.027613135054707527,
-0.006060049403458834,
0.007679015398025513,
0.05388142541050911,
0.059932682663202286,
-0.03823108226060867,
-0.030679035931825638,
-0.01669817604124546,
-0.06617826223373413,
-0.00855986773967743,
0.03850376605987549,
0.0016970313154160976,
0.06807185709476471,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/23932 | [
"Needs Triage"
] | Inconsistence between components in `PCA` and `SparsePCA`
The components provided by `PCA` and `SparsePCA` are not consistent when we explicitly do not impose any sparsity constraint in `SparsePCA`. It is only a sign flipped meaning that this might not be dramatic.
```python
import numpy as np
from sklearn.decomp... | 23,932 | [
0.028735067695379257,
-0.00007018136966507882,
0.009060850366950035,
0.05686481297016144,
0.058699995279312134,
-0.03727735951542854,
-0.03134994953870773,
-0.013761473819613457,
-0.06458915770053864,
-0.011254536919295788,
0.04203537106513977,
0.005757451523095369,
0.07353123277425766,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23930 | [
"Bug",
"Build / CI"
] | scikit-learn tests segfault on armel
### Describe the bug
Hi,
scikit-learn version 1.1.1 seems to segfault on armel architecture. Full build log [here](https://buildd.debian.org/status/fetch.php?pkg=scikit-learn&arch=armel&ver=1.1.1-1&stamp=1653343638&raw=0)
### Steps/Code to Reproduce
Normally running testsuite... | 23,930 | [
-0.003310560481622815,
-0.03355494514107704,
-0.023056460544466972,
-0.00503860367462039,
0.04898207634687424,
0.03370807319879532,
0.03631290793418884,
0.0537252202630043,
0.01905164122581482,
-0.03236137703061104,
0.04626413807272911,
0.06278352439403534,
-0.008700390346348286,
0.0112003... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23928 | [
"API",
"RFC"
] | RFC SLEP006: verbose vs non-verbose declaration in meta-estimator
As the proposal and the implementation of meta-estimator routing (SLEP006) stands, if the user wants to use `sample_weight`, they need to be quite verbose in how they declare the estimators. Taking `AdaBoostClassifier` as an example, and imagining if `A... | 23,928 | [
0.04628167301416397,
0.09256419539451599,
0.03377661108970642,
-0.040588729083538055,
-0.007686599623411894,
-0.04835553467273712,
0.07655643671751022,
-0.02082829922437668,
0.0056194425560534,
-0.007355865556746721,
0.045542161911726,
0.03485097363591194,
0.013475704938173294,
0.001267378... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08246245980262756,
0.05798686668276787,
-0.0018575822468847036,
-0.04806280881166458,
-0.017758693546056747,
-0.0012855632230639458,
0.08961084485054016,
0.01369604654610157,
-0.01582731306552887,
0.01703059673309326,
0.12380431592464447,
-0.0018992969999089837,
-0.0565924346446991,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08827419579029083,
0.04863277077674866,
-0.0005136109539307654,
-0.059059202671051025,
-0.00753470603376627,
0.0012608448741957545,
0.07652469724416733,
0.014413727447390556,
-0.020196249708533287,
0.02844245731830597,
0.13437962532043457,
-0.0010813430417329073,
-0.04688367620110512,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08041805773973465,
0.05361110344529152,
-0.00006339594256132841,
-0.05884351208806038,
-0.018685167655348778,
0.002880231710150838,
0.07407594472169876,
0.010219373740255833,
-0.017762793228030205,
0.02008715644478798,
0.13163089752197266,
0.002458221511915326,
-0.05013248696923256,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08551188558340073,
0.05359366163611412,
-0.00015619753685314208,
-0.0580279566347599,
-0.007764225825667381,
0.003434235230088234,
0.0797262191772461,
0.009904182516038418,
-0.018845364451408386,
0.02785271406173706,
0.12838280200958252,
-0.004468967672437429,
-0.05025870352983475,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07844866812229156,
0.06157887727022171,
0.00415447261184454,
-0.06017889827489853,
-0.005971225909888744,
0.006271780002862215,
0.07506624609231949,
0.016023358330130577,
-0.022899597883224487,
0.02277744747698307,
0.12538065016269684,
0.0030764311086386442,
-0.04646790772676468,
0.0777... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08510084450244904,
0.05449198931455612,
-0.0033486003521829844,
-0.05605383217334747,
-0.01218542829155922,
0.003501514671370387,
0.09043149650096893,
0.01813274435698986,
-0.016005471348762512,
0.025551263242959976,
0.12916575372219086,
-0.005441863089799881,
-0.0477999709546566,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07618606835603714,
0.05534577742218971,
0.0024581346660852432,
-0.056912150233983994,
-0.0013961412478238344,
0.0008095612865872681,
0.08609262853860855,
0.016456637531518936,
-0.03146251663565636,
0.024137822911143303,
0.1269836276769638,
-0.010059141553938389,
-0.05492081865668297,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08599641919136047,
0.05334852263331413,
-0.0028254538774490356,
-0.056718554347753525,
-0.012579413130879402,
0.003244446124881506,
0.089073047041893,
0.017458800226449966,
-0.016152551397681236,
0.025490086525678635,
0.1299530565738678,
-0.0064117154106497765,
-0.04747433587908745,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07669386267662048,
0.04328582063317299,
-0.004256309941411018,
-0.05344916507601738,
-0.011876162141561508,
0.0053208996541798115,
0.08666890859603882,
0.00921176839619875,
-0.021616308018565178,
0.02447107806801796,
0.1244557723402977,
-0.0023981588892638683,
-0.045377206057310104,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07851385325193405,
0.05890728160738945,
0.0037801179569214582,
-0.05524951592087746,
-0.010191025212407112,
-0.003259351011365652,
0.08967888355255127,
0.01362854428589344,
-0.008151542395353317,
0.01571573130786419,
0.12342534214258194,
0.013245812617242336,
-0.041005369275808334,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08082426339387894,
0.0615692175924778,
-0.0007632608176209033,
-0.0564887560904026,
-0.015252201817929745,
-0.003472481854259968,
0.0826326459646225,
0.009618237614631653,
-0.01918724551796913,
0.02538643218576908,
0.12721416354179382,
-0.001043046941049397,
-0.059340815991163254,
0.081... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.0850265696644783,
0.05023939907550812,
-0.0008702012710273266,
-0.05977924168109894,
-0.0068303621374070644,
0.0024824931751936674,
0.07874257117509842,
0.012957261875271797,
-0.017497094348073006,
0.031683191657066345,
0.12809516489505768,
-0.005433673970401287,
-0.04871705174446106,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.083857461810112,
0.049346499145030975,
0.0011189548531547189,
-0.057633861899375916,
-0.004418469499796629,
0.003424848197028041,
0.07940858602523804,
0.010207196697592735,
-0.018519539386034012,
0.025977732613682747,
0.12999357283115387,
-0.003655205247923732,
-0.04599633440375328,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.0850021243095398,
0.0632760152220726,
0.002568039810284972,
-0.0541527196764946,
-0.014332807622849941,
-0.004454157315194607,
0.07231130450963974,
0.01903502270579338,
-0.017976710572838783,
0.024569706991314888,
0.12875081598758698,
-0.012206724844872952,
-0.049003973603248596,
0.0830... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08171813189983368,
0.06607339531183243,
-0.0003788706089835614,
-0.06177062541246414,
-0.006329044699668884,
0.00495248893275857,
0.07478341460227966,
-0.0005209048977121711,
-0.029124431312084198,
0.03009984642267227,
0.12828576564788818,
0.003656248562037945,
-0.051264502108097076,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07750935852527618,
0.05968353897333145,
0.008613687008619308,
-0.06593802571296692,
-0.012955126352608204,
0.0005379345966503024,
0.07622667402029037,
0.015435709618031979,
-0.014318175613880157,
0.0331130214035511,
0.14375239610671997,
-0.01482776366174221,
-0.0488031730055809,
0.07298... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.0856749415397644,
0.05537877604365349,
0.004865678958594799,
-0.061667248606681824,
-0.009068750776350498,
0.00343139236792922,
0.0743684321641922,
0.0032249181531369686,
0.0028878794983029366,
0.028781967237591743,
0.1231459453701973,
0.0030358806252479553,
-0.05243672803044319,
0.0767... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08314196020364761,
0.05760993808507919,
-0.0020090530160814524,
-0.06191568076610565,
-0.014797342009842396,
-0.0021058155689388514,
0.07922570407390594,
0.009373621083796024,
-0.025616418570280075,
0.02626170963048935,
0.11631675064563751,
0.006656411103904247,
-0.05333798751235008,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.07862637937068939,
0.06660256534814835,
0.00206036027520895,
-0.06810422986745834,
-0.014360080473124981,
-0.009563209488987923,
0.07558019459247589,
0.005630217958241701,
-0.007733364123851061,
0.02873004414141178,
0.1309410035610199,
0.008748500607907772,
-0.054997239261865616,
0.0935... |
https://github.com/scikit-learn/scikit-learn/issues/23923 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit Needs a version that allows users to specify initial time
### Describe the workflow you want to enable
I would like to add a new `TimeSeriesSplit` [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html) called `TimeSeriesRollingOriginSplit`. In the curren... | 23,923 | [
-0.08497024327516556,
0.055447086691856384,
0.0030530840158462524,
-0.058301959186792374,
-0.005302207078784704,
0.0036764987744390965,
0.07795389741659164,
0.009042313322424889,
-0.018337754532694817,
0.026061901822686195,
0.13023492693901062,
-0.0013482613721862435,
-0.05160849168896675,
... |
https://github.com/scikit-learn/scikit-learn/issues/23922 | [
"Documentation",
"module:model_selection"
] | GroupKFold: "The same group will not appear in two different folds..."
https://github.com/scikit-learn/scikit-learn/blob/baf0ea25d6dd034403370fea552b21a6776bef18/sklearn/model_selection/_split.py#L456-L457
Hi, just was wondering what exactly was meant by the above lines in sklearn's docs for GroupKFold. Based on th... | 23,922 | [
-0.007344928104430437,
-0.06001422181725502,
0.006292755715548992,
0.0025023852940648794,
-0.02889864705502987,
0.04047344624996185,
0.11400093883275986,
0.018945792689919472,
0.04251141473650932,
-0.034393422305583954,
0.02025812305510044,
-0.0010643871501088142,
0.03522805869579315,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/23922 | [
"Documentation",
"module:model_selection"
] | GroupKFold: "The same group will not appear in two different folds..."
https://github.com/scikit-learn/scikit-learn/blob/baf0ea25d6dd034403370fea552b21a6776bef18/sklearn/model_selection/_split.py#L456-L457
Hi, just was wondering what exactly was meant by the above lines in sklearn's docs for GroupKFold. Based on th... | 23,922 | [
-0.01053616963326931,
-0.05971559137105942,
0.005653869826346636,
0.01637902297079563,
-0.018932631239295006,
0.040884144604206085,
0.13013038039207458,
0.014136956073343754,
0.04091383516788483,
-0.03530838340520859,
0.006844918243587017,
-0.0025959897320717573,
0.038350772112607956,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/23922 | [
"Documentation",
"module:model_selection"
] | GroupKFold: "The same group will not appear in two different folds..."
https://github.com/scikit-learn/scikit-learn/blob/baf0ea25d6dd034403370fea552b21a6776bef18/sklearn/model_selection/_split.py#L456-L457
Hi, just was wondering what exactly was meant by the above lines in sklearn's docs for GroupKFold. Based on th... | 23,922 | [
-0.010175161994993687,
-0.056830644607543945,
0.004897726234048605,
0.01654805801808834,
-0.018315542489290237,
0.0396028570830822,
0.13133150339126587,
0.013739781454205513,
0.03982141613960266,
-0.036161668598651886,
0.007696216925978661,
-0.0023308931849896908,
0.039899930357933044,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
-0.005001183599233627,
0.041021279990673065,
0.04748303070664406,
-0.03277471661567688,
0.05063905566930771,
-0.01766485534608364,
0.013275131583213806,
-0.008004843257367611,
0.008601510897278786,
0.0009035732364282012,
0.06604689359664917,
0.006821112707257271,
-0.011823233217000961,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
0.00002620118175400421,
0.026383135467767715,
0.048143960535526276,
-0.02467789128422737,
0.0423678420484066,
-0.02476615458726883,
0.015218177810311317,
-0.006314104422926903,
-0.00554285803809762,
-0.0034809373319149017,
0.0614200085401535,
0.013263036496937275,
-0.03250117227435112,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
0.007075773552060127,
0.052855320274829865,
0.05231911689043045,
-0.03482972830533981,
0.03489581495523453,
-0.023366518318653107,
0.023401152342557907,
-0.006146571598947048,
-0.014016336761415005,
-0.01674782484769821,
0.0617607980966568,
-0.00016335799591615796,
-0.007938437163829803,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
-0.0008935174555517733,
0.031323861330747604,
0.0466899648308754,
-0.021298127248883247,
0.0374176912009716,
-0.034634754061698914,
0.007234442047774792,
-0.00021856628882233053,
-0.00938076339662075,
-0.011834152974188328,
0.054701756685972214,
0.019810708239674568,
-0.024176668375730515,
... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
-0.013418029993772507,
0.030083127319812775,
0.04416283592581749,
-0.010859735310077667,
0.0328381285071373,
-0.028878342360258102,
0.012769483029842377,
-0.004600428976118565,
-0.006128120236098766,
-0.01029888540506363,
0.06145455315709114,
0.0076924581080675125,
-0.017636222764849663,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
-0.004147174768149853,
0.043426740914583206,
0.04662991315126419,
-0.034884918481111526,
0.046417366713285446,
-0.015576138161122799,
0.017317570745944977,
-0.009999054484069347,
0.006604267284274101,
-0.0015005093300715089,
0.06612291187047958,
0.006430729757994413,
-0.007742360699921846,
... |
https://github.com/scikit-learn/scikit-learn/issues/23920 | [
"API",
"RFC"
] | RFC SLEP006: allow users to enable a "strict" mode in metadata routing
ref: https://github.com/scikit-learn/scikit-learn/pull/22986/files#r862344847
Along the way we've had discussions on whether we should raise if a metadata is requested but not provided.
Mirroring the existing behavior, SLEP006 and the impleme... | 23,920 | [
0.010048477910459042,
0.05260273441672325,
0.04897184297442436,
-0.032514944672584534,
0.012735234573483467,
-0.04104297608137131,
0.0377231203019619,
-0.008374106138944626,
-0.01315347757190466,
-0.0058145527727901936,
0.05269370973110199,
0.02654670923948288,
-0.03021910786628723,
0.0459... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.