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/29567 | [
"Bug"
] | ⚠️ CI failed on Wheel builder (last failure: Jul 27, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10120613947)** (Jul 27, 2024)
COMMENT:
For further reference, there was a aimilar bus error on July 27 for cp311-macosx_arm64 and cp312-macosx_arm64 see [bui... | 29,567 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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0.007... |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/29565 | [
"New Feature"
] | Override precompute check in LassoCV
### Describe the workflow you want to enable
I am trying to use precompute=True for LassoCV. To save memory, I am passing in the inputs as float32's. However, I get an error that the Gram matrix precompute didn't match the true Gram matrix, where the error is some small epsilon li... | 29,565 | [
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https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
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https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
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0.017655033618211746,
0.02484048157930374,
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-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
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0.027508506551384926,
0.027744485065340996,
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0.06723874807357788,
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... |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
-0.0428207628428936,
0.02312449924647808,
0.018854226917028427,
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0.07147965580224991,
0.0013150654267519712,
-0.013606029562652111,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
-0.0411788709461689,
0.02152581512928009,
0.02453906089067459,
-0.006846597883850336,
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0.001991297584027052,
0.06912041455507278,
0.0005258029559627175,
-0.018306516110897064,
-0.0407... |
https://github.com/scikit-learn/scikit-learn/issues/29558 | [
"RFC"
] | RFC Should cross-validation splitters validate that all classes are represented in each split?
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the following: a CV estimator will... | 29,558 | [
-0.03430496156215668,
0.02438008412718773,
0.03355473652482033,
0.0004895285237580538,
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-0.002601644489914179,
-0.01837313361465931,
-0.0319746... |
https://github.com/scikit-learn/scikit-learn/issues/29556 | [
"Bug",
"Needs Triage"
] | Sklearn metric module - mean squared error
### Describe the bug
```python
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
axis_X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]).reshape(-1, 1)
axis_X_train = ax... | 29,556 | [
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0.011668439954519272,
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0.06699523329734802,
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0.0260936... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29554 | [
"Documentation"
] | RFECV cross-validation generator (`cv`) parameter
### Describe the issue linked to the documentation
Hello,
if I'm not mistaken, I think that the [documentation of RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html) about the `cv` parameter might be incorrect regarding ... | 29,554 | [
-0.00511628994718194,
-0.06128697469830513,
0.021638745442032814,
0.0034518432803452015,
0.03608068451285362,
0.007740948349237442,
0.0484175831079483,
-0.008990656584501266,
-0.010081209242343903,
-0.032102491706609726,
0.06694946438074112,
0.039662089198827744,
0.018254298716783524,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29551 | [
"Bug"
] | BUG Problem when `CalibratedClassifierCV` train contains 2 classes but data contains more
### Describe the bug
In `CalibratedClassifierCV` when a train split contains 2 classes (binary) but the data contains more (>=3) classes, we assume the data is binary:
https://github.com/scikit-learn/scikit-learn/blob/d20e0b9... | 29,551 | [
0.02791655994951725,
-0.02278129570186138,
-0.007798729930073023,
0.05558672919869423,
0.07050608098506927,
-0.0014381257351487875,
0.00938000064343214,
0.04117044433951378,
-0.03419410437345505,
-0.02207224629819393,
0.04555445909500122,
0.035065628588199615,
0.029746709391474724,
-0.0036... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29549 | [
"Array API"
] | Follow-up after mean_poisson_deviance array API PR
As a follow-up for #29227.
The following command fails locally:
```
pytest -vl sklearn/metrics/tests/test_common.py -k 'api_regression_metric and mean_poisson_deviance'
```
see full error in https://github.com/scikit-learn/scikit-learn/pull/29227#issuecomment-2... | 29,549 | [
-0.024826688691973686,
0.06311353296041489,
0.009407968260347843,
-0.005364411976188421,
0.031060436740517616,
0.007403276395052671,
0.08599070459604263,
0.04198668152093887,
0.04899860918521881,
0.009820960462093353,
0.03599654138088226,
0.01474082376807928,
-0.01757543906569481,
0.015683... |
https://github.com/scikit-learn/scikit-learn/issues/29547 | [
"Documentation"
] | GridSearchCV support for 'precomputed' kernel not documented
### Describe the issue linked to the documentation
GridSearchCV seems to work even with a precomputed kernel but there is nothing about it in the documentation. Is there a reason for this or did it just go unnoticed?
### Suggest a potential alternative/fix... | 29,547 | [
-0.010781271383166313,
0.011651912704110146,
0.00780043238773942,
0.010813607834279537,
0.044576290994882584,
-0.025125322863459587,
0.04105520248413086,
0.02088971994817257,
0.031433846801519394,
0.012215073220431805,
0.06651386618614197,
0.04935774579644203,
0.017368771135807037,
0.00684... |
https://github.com/scikit-learn/scikit-learn/issues/29547 | [
"Documentation"
] | GridSearchCV support for 'precomputed' kernel not documented
### Describe the issue linked to the documentation
GridSearchCV seems to work even with a precomputed kernel but there is nothing about it in the documentation. Is there a reason for this or did it just go unnoticed?
### Suggest a potential alternative/fix... | 29,547 | [
-0.015762001276016235,
0.001413535326719284,
-0.009604623541235924,
0.005771663971245289,
0.04333360865712166,
-0.03240099176764488,
0.03601643443107605,
0.017677273601293564,
0.01068670954555273,
0.018522122874855995,
0.07863040268421173,
0.02137676440179348,
0.010161031037569046,
-0.0150... |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 | [
-0.026126602664589882,
0.005679056979715824,
-0.01598355546593666,
-0.02638711780309677,
0.047764308750629425,
0.01606505736708641,
-0.041121017187833786,
0.004968032240867615,
-0.015057246200740337,
-0.024270715191960335,
0.07145170122385025,
0.03855912759900093,
-0.019134702160954475,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 | [
-0.026126602664589882,
0.005679056979715824,
-0.01598355546593666,
-0.02638711780309677,
0.047764308750629425,
0.01606505736708641,
-0.041121017187833786,
0.004968032240867615,
-0.015057246200740337,
-0.024270715191960335,
0.07145170122385025,
0.03855912759900093,
-0.019134702160954475,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29546 | [
"Build / CI"
] | CI Investigate timeout in no-OpenMP build with Meson 1.5
https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516
> So the no-OpenMP build still times out ... from the [diff](https://github.com/scikit-learn/scikit-learn/pull/29486/files#diff-5dfc3d97f64b11902494f92b685545d78f4aa020b235c55db0d... | 29,546 | [
-0.026126602664589882,
0.005679056979715824,
-0.01598355546593666,
-0.02638711780309677,
0.047764308750629425,
0.01606505736708641,
-0.041121017187833786,
0.004968032240867615,
-0.015057246200740337,
-0.024270715191960335,
0.07145170122385025,
0.03855912759900093,
-0.019134702160954475,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29543 | [
"Documentation"
] | "Choosing the right estimator"-widget links broken
### Describe the bug
The links in the helper graph (think it's called machine learning map) to guide choosing an estimator (link: https://scikit-learn.org/stable/machine_learning_map.html#ml-map) is broken - the links are not up-to-date to reflect the url-structure... | 29,543 | [
0.016164543107151985,
0.004948979709297419,
-0.027471961453557014,
-0.02316218614578247,
0.020542705431580544,
0.022864796221256256,
0.05642329156398773,
0.04058079048991203,
0.05344919487833977,
-0.007041817530989647,
0.0013211374171078205,
0.05126794055104256,
0.028695615008473396,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.02314324490725994,
0.0771779716014862,
0.007612578570842743,
-0.016330182552337646,
0.025685736909508705,
-0.0374167375266552,
-0.015039866790175438,
0.03132093325257301,
-0.04709544777870178,
-0.021647470071911812,
0.05180668085813522,
0.002196903806179762,
-0.013553098775446415,
0.0416... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.027590861544013023,
0.06649953126907349,
0.00774279423058033,
-0.013748184777796268,
0.023430150002241135,
-0.03535781055688858,
-0.01592966914176941,
0.02811066433787346,
-0.05032416805624962,
-0.022783750668168068,
0.04655861482024193,
0.0014861103845760226,
-0.010669377632439137,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.020789964124560356,
0.07657171040773392,
0.005757317878305912,
-0.01511180680245161,
0.026186536997556686,
-0.03860101476311684,
-0.016055433079600334,
0.030451718717813492,
-0.04631728306412697,
-0.02108241803944111,
0.05115041881799698,
0.0034123072400689125,
-0.014784215949475765,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021850189194083214,
0.07672461867332458,
0.006673922296613455,
-0.015635084360837936,
0.027105968445539474,
-0.037764690816402435,
-0.01648464798927307,
0.030210739001631737,
-0.04684692993760109,
-0.020560193806886673,
0.0512140728533268,
0.003520144149661064,
-0.014264082536101341,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021586820483207703,
0.0779130607843399,
0.006766308099031448,
-0.017127051949501038,
0.024604009464383125,
-0.03809022530913353,
-0.01712987571954727,
0.029442718252539635,
-0.0481644831597805,
-0.019598381593823433,
0.051922913640737534,
0.003457138314843178,
-0.018595023080706596,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.0212507676333189,
0.07470229268074036,
0.005791311617940664,
-0.015407610684633255,
0.02620611898601055,
-0.038103137165308,
-0.01605966128408909,
0.030679335817694664,
-0.0458943247795105,
-0.020907185971736908,
0.050756342709064484,
0.003183456603437662,
-0.014414866454899311,
0.044087... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.023676685988903046,
0.07631627470254898,
0.0056908088736236095,
-0.015239888802170753,
0.027274640277028084,
-0.03588496148586273,
-0.020628632977604866,
0.02896292507648468,
-0.04706806316971779,
-0.02227294072508812,
0.04832787439227104,
0.006508008576929569,
-0.014019850641489029,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021050943061709404,
0.07565139979124069,
0.004469177685678005,
-0.015431419014930725,
0.025440750643610954,
-0.03894985467195511,
-0.013333533890545368,
0.032032374292612076,
-0.04549426957964897,
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0.05550955981016159,
-0.0007765797199681401,
-0.015052108094096184,
... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021387062966823578,
0.07823560386896133,
0.006814441177994013,
-0.01552520226687193,
0.026624536141753197,
-0.037911463528871536,
-0.016998302191495895,
0.02994970791041851,
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-0.0194704569876194,
0.05165928229689598,
0.004427895415574312,
-0.01611432433128357,
0.0453... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.022109651938080788,
0.07351963967084885,
0.008376242592930794,
-0.014993399381637573,
0.02804480493068695,
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0.029614437371492386,
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-0.019491011276841164,
0.04860891029238701,
0.0028717918321490288,
-0.014024874195456505,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021501310169696808,
0.07687745243310928,
0.006073820870369673,
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0.024920227006077766,
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0.05119955167174339,
0.0011775912716984749,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.022193463519215584,
0.07165399938821793,
0.010460869409143925,
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0.023390496149659157,
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0.029515055939555168,
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0.054200075566768646,
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-0.014404607936739922,
... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.022507168352603912,
0.07355199754238129,
0.006625870242714882,
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0.0260934978723526,
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0.03113539144396782,
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0.05230913311243057,
0.0020145014859735966,
-0.015051218681037426,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.022362083196640015,
0.07635567337274551,
0.006706416141241789,
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0.025737861171364784,
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0.03038070723414421,
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0.05043083056807518,
0.00250830827280879,
-0.013983288779854774,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29542 | [
"help wanted",
"module:tree",
"cython"
] | FEA Add missing-value support to sparse splitter in RandomForest and ExtraTrees
### Summary
While missing-value support for decision trees have been added recently, they only work when encoded in a dense array. Since `RandomForest*` and `ExtraTrees*` both support sparse `X`, if a user encodes `np.nan` inside sparse `... | 29,542 | [
0.021576398983597755,
0.07343235611915588,
0.007350186817348003,
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0.022799761965870857,
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0.03007277101278305,
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0.05203435197472572,
0.0014635096304118633,
-0.011591840535402298,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 | [
-0.054131537675857544,
0.04400412365794182,
0.013905597850680351,
-0.040995042771101,
0.0450948029756546,
0.0066209714859724045,
0.0738886222243309,
0.024498935788869858,
0.0543719045817852,
0.004598303698003292,
0.09577082842588425,
0.05331895127892494,
-0.052410248667001724,
0.0884526297... |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 | [
-0.05591368302702904,
0.029544221237301826,
0.018719663843512535,
-0.036328744143247604,
0.05726410821080208,
0.023489272221922874,
0.08285216242074966,
0.02505330927670002,
0.041799794882535934,
-0.018599217757582664,
0.07709844410419464,
0.05345575883984566,
-0.039918944239616394,
0.0869... |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 | [
-0.04754410684108734,
0.059656012803316116,
0.026949578896164894,
-0.056498389691114426,
0.0632166787981987,
0.011022893711924553,
0.08068348467350006,
0.02448437735438347,
0.0240000169724226,
-0.00037712653283961117,
0.09327547252178192,
0.06086331233382225,
-0.03841017186641693,
0.081178... |
https://github.com/scikit-learn/scikit-learn/issues/29539 | [
"New Feature"
] | Tag for identifying capability to handle non-numeric data in input
### Describe the workflow you want to enable
I want to be able to find out whether an estimator supports non-numeric features in the input data passed to it in fit/transform. Example : `OneHotEncoder`, `LabelEncoder` supports this while `StandardScale... | 29,539 | [
-0.06693223863840103,
0.034214895218610764,
0.015820946544408798,
-0.045001320540905,
0.050443343818187714,
0.004545378964394331,
0.07646598666906357,
0.01965734362602234,
0.04275926575064659,
0.004769650287926197,
0.094460129737854,
0.04933878779411316,
-0.04409200698137283,
0.09683346748... |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 | [
-0.02125440537929535,
0.022120008245110512,
0.022434305399656296,
0.02371874637901783,
0.06776022911071777,
-0.021733762696385384,
-0.03563743084669113,
0.01752506010234356,
-0.0017222388414666057,
0.014708836562931538,
0.04447639361023903,
0.030158165842294693,
0.0020065787248313427,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 | [
-0.02125440537929535,
0.022120008245110512,
0.022434305399656296,
0.02371874637901783,
0.06776022911071777,
-0.021733762696385384,
-0.03563743084669113,
0.01752506010234356,
-0.0017222388414666057,
0.014708836562931538,
0.04447639361023903,
0.030158165842294693,
0.0020065787248313427,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 | [
-0.02125440537929535,
0.022120008245110512,
0.022434305399656296,
0.02371874637901783,
0.06776022911071777,
-0.021733762696385384,
-0.03563743084669113,
0.01752506010234356,
-0.0017222388414666057,
0.014708836562931538,
0.04447639361023903,
0.030158165842294693,
0.0020065787248313427,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29534 | [
"Bug"
] | decomposition.PCA(svd_solver='covariance_eigh') is less stable with numpy==2.0
### Describe the bug
`decomposition.PCA(svd_solver='covariance_eigh')` is less stable with numpy==2.0
I noticed this issue as some tests started failing at the downstream [dask-ml/#997](https://github.com/dask/dask-ml/pull/997)
For a... | 29,534 | [
-0.02125440537929535,
0.022120008245110512,
0.022434305399656296,
0.02371874637901783,
0.06776022911071777,
-0.021733762696385384,
-0.03563743084669113,
0.01752506010234356,
-0.0017222388414666057,
0.014708836562931538,
0.04447639361023903,
0.030158165842294693,
0.0020065787248313427,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29533 | [
"New Feature",
"Needs Triage"
] | Add FN and FP weight parameter in MCC
### Describe the workflow you want to enable
Introducing a weight parameter for false negatives (FN) and false positives (FP) in Matthews Correlation Coefficient (MCC) would enhance the metric’s flexibility and applicability, particularly in contexts where the costs of differen... | 29,533 | [
-0.031447894871234894,
0.031588900834321976,
0.020488468930125237,
-0.04130105674266815,
0.002465547528117895,
-0.001594632281921804,
-0.02408585511147976,
0.014492061920464039,
-0.07269010692834854,
0.0413336306810379,
0.014645672403275967,
0.028282612562179565,
0.01659730263054371,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/29533 | [
"New Feature",
"Needs Triage"
] | Add FN and FP weight parameter in MCC
### Describe the workflow you want to enable
Introducing a weight parameter for false negatives (FN) and false positives (FP) in Matthews Correlation Coefficient (MCC) would enhance the metric’s flexibility and applicability, particularly in contexts where the costs of differen... | 29,533 | [
-0.031447894871234894,
0.031588900834321976,
0.020488468930125237,
-0.04130105674266815,
0.002465547528117895,
-0.001594632281921804,
-0.02408585511147976,
0.014492061920464039,
-0.07269010692834854,
0.0413336306810379,
0.014645672403275967,
0.028282612562179565,
0.01659730263054371,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29531 | [
"Bug"
] | RFE results are inconsistent between machines with ties in feature importances at threshold
### Describe the bug
RFE uses np.argsort on the feature_importances from the estimator, this is not repeatable across machines. This only matters when there are ties in the feature importances that overlap with the threshold... | 29,531 | [
0.007382706739008427,
0.04337228834629059,
0.017292236909270287,
0.006546149030327797,
0.02841426618397236,
-0.023556029424071312,
-0.0006449155625887215,
0.023508865386247635,
0.024578385055065155,
-0.043374203145504,
0.012302008457481861,
0.048356130719184875,
0.012403800152242184,
0.000... |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 | [
0.031509652733802795,
0.013998370617628098,
-0.010637877508997917,
-0.0222536139190197,
0.021510103717446327,
0.02032308652997017,
0.0697818398475647,
0.002538554836064577,
0.00836443342268467,
-0.015499282628297806,
0.027175698429346085,
0.03031427040696144,
0.015432764776051044,
0.001540... |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 | [
0.020476924255490303,
0.04778538644313812,
-0.003643221454694867,
-0.013326628133654594,
0.006452626083046198,
0.017083920538425446,
0.0648389384150505,
-0.016616955399513245,
-0.00033713458105921745,
-0.021503878757357597,
0.023594334721565247,
0.036085017025470734,
0.005690024234354496,
... |
https://github.com/scikit-learn/scikit-learn/issues/29530 | [
"Documentation"
] | Community section: add link to GitHub discussions
### Describe the issue linked to the documentation
Can we add a link to GitHub discussions in the footer of the home page?
- home page: https://scikit-learn.org/stable/
- link to add: https://github.com/scikit-learn/scikit-learn/discussions
### Suggest a potent... | 29,530 | [
0.015760265290737152,
-0.002095896750688553,
-0.017320847138762474,
-0.012526191771030426,
0.015398194082081318,
0.024805491790175438,
0.06982230395078659,
0.0017954825889319181,
0.01372008491307497,
-0.0077751572243869305,
0.03948337212204933,
0.040433961898088455,
0.03021947294473648,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29524 | [
"New Feature",
"Needs Triage"
] | GaussianMixture takes very long in pathological cases
### Describe the workflow you want to enable
In general, fitting a GaussianMixture works well and quickly (~1s). However in certain cases it takes very long, even though the data set is not very big. A simple example that takes almost a minute (5.5 minutes of CP... | 29,524 | [
-0.018825476989150047,
0.07403054088354111,
0.02861030213534832,
-0.013558992184698582,
0.06932300329208374,
0.03411567956209183,
-0.011167753487825394,
0.04298526793718338,
-0.05007890611886978,
0.014097512699663639,
0.04538922756910324,
0.0067709037102758884,
0.03138108178973198,
0.05235... |
https://github.com/scikit-learn/scikit-learn/issues/29523 | [
"Bug",
"Needs Triage"
] | KNNImputer - output shape not equal input shape
### Describe the bug
The output of the fit_tranform is not equal to the input shape, when the NaN's are all in one column
### Steps/Code to Reproduce
```
from sklearn.impute import KNNImputer
input = np.random.rand(5, 5)
input[0,4]=np.nan
input[1,4]=np.nan
input... | 29,523 | [
0.003723469562828541,
-0.05583786591887474,
0.010973796248435974,
0.03265346959233284,
0.03567851334810257,
-0.05979275703430176,
0.028349895030260086,
0.016979191452264786,
-0.015638932585716248,
-0.010809345170855522,
-0.007362074218690395,
0.05000416934490204,
0.04584595933556557,
0.048... |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 | [
-0.02175283432006836,
-0.005284324288368225,
0.044422298669815063,
-0.005082768388092518,
0.014252619817852974,
0.013396115973591805,
0.032528266310691833,
0.008872314356267452,
-0.00464396458119154,
0.018728096038103104,
0.03899113088846207,
0.019395938143134117,
0.017550157383084297,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 | [
-0.02175283432006836,
-0.005284324288368225,
0.044422298669815063,
-0.005082768388092518,
0.014252619817852974,
0.013396115973591805,
0.032528266310691833,
0.008872314356267452,
-0.00464396458119154,
0.018728096038103104,
0.03899113088846207,
0.019395938143134117,
0.017550157383084297,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 | [
-0.02175283432006836,
-0.005284324288368225,
0.044422298669815063,
-0.005082768388092518,
0.014252619817852974,
0.013396115973591805,
0.032528266310691833,
0.008872314356267452,
-0.00464396458119154,
0.018728096038103104,
0.03899113088846207,
0.019395938143134117,
0.017550157383084297,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 | [
-0.02175283432006836,
-0.005284324288368225,
0.044422298669815063,
-0.005082768388092518,
0.014252619817852974,
0.013396115973591805,
0.032528266310691833,
0.008872314356267452,
-0.00464396458119154,
0.018728096038103104,
0.03899113088846207,
0.019395938143134117,
0.017550157383084297,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29521 | [
"Bug",
"help wanted"
] | NDCG in case of abscence of relevant items
### Describe the bug
In `sklearn.metrics._ndcg_sample_scores`, there is a counterintuitive handling of the case where all true relevances are equal to zero for some samples. In this case, DCG = 0, IDCG = 0, and the whole NDCG is not defined. In `sklearn` implementation it is... | 29,521 | [
-0.02175283432006836,
-0.005284324288368225,
0.044422298669815063,
-0.005082768388092518,
0.014252619817852974,
0.013396115973591805,
0.032528266310691833,
0.008872314356267452,
-0.00464396458119154,
0.018728096038103104,
0.03899113088846207,
0.019395938143134117,
0.017550157383084297,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29515 | [
"New Feature",
"Needs Decision"
] | Handle all-zeros cases for multioutput metrics
### Describe the workflow you want to enable
For multioutput problems, all-zero label columns (or in general constant label columns) can sometimes happen, for example when using cross-validation. Most metrics (e.g. precision, recall, F1, AUPRC/average recall) return 0.0 ... | 29,515 | [
-0.036614082753658295,
0.017748074606060982,
0.05429726094007492,
-0.014571825042366982,
0.06030164659023285,
0.006599529180675745,
0.002068119589239359,
0.015312963165342808,
0.00249855755828321,
-0.010035229846835136,
0.012247700244188309,
0.00319700432009995,
-0.038573745638132095,
0.06... |
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