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/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29697 | [
"Bug"
] | GaussianProcessRegressor: wrong std and cov results when n_features>1 and no y normalization
### Describe the bug
When `n_features > 1` and `normalization_y` is `False`, the `GaussianProcessRegressor.predict` seems to return bad std and cov results, as it doesn't consider the scale of the different features (while it... | 29,697 | [
-0.02885998599231243,
-0.0006426681065931916,
0.042549241334199905,
0.008215626701712608,
0.08391483873128891,
-0.028812257573008537,
0.07617073506116867,
-0.014698958955705166,
-0.00032015854958444834,
0.046867966651916504,
0.021013757213950157,
0.0018736358033493161,
0.03182714432477951,
... |
https://github.com/scikit-learn/scikit-learn/issues/29695 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 21, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10483139590)** (Aug 21, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10501460588) on Aug 22... | 29,695 | [
-0.033843398094177246,
0.04995068907737732,
-0.018209554255008698,
-0.012469052337110043,
0.011754452250897884,
0.009769708849489689,
0.01391623541712761,
0.04226750507950783,
-0.049445219337940216,
0.038639187812805176,
0.08661279082298279,
0.02463846653699875,
-0.01591048575937748,
0.084... |
https://github.com/scikit-learn/scikit-learn/issues/29692 | [
"New Feature",
"Needs Triage"
] | Add Diebold Mariano test for distinguishing forecasts
### Describe the workflow you want to enable
I would like to be able to compare whether one forecast is statistically better than another.
### Describe your proposed solution
Under certain conditions, the *Diebold-Mariano* test achieves this. There's an example ... | 29,692 | [
-0.047405440360307693,
0.10719233751296997,
0.00828808918595314,
0.0033958242274820805,
-0.024187713861465454,
0.004441362805664539,
0.04651986435055733,
-0.0012680599465966225,
0.03823945298790932,
0.049113281071186066,
0.027220753952860832,
0.010844682343304157,
-0.02521662414073944,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29684 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 17, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10429290896)** (Aug 17, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10437578688) on Aug 18... | 29,684 | [
-0.03307618573307991,
0.04742718115448952,
-0.016850508749485016,
-0.015124152414500713,
0.011410828679800034,
0.010982421226799488,
0.015212927013635635,
0.04146287962794304,
-0.048001985996961594,
0.03828180953860283,
0.08678757399320602,
0.026071736589074135,
-0.01507872436195612,
0.084... |
https://github.com/scikit-learn/scikit-learn/issues/29679 | [
"Bug",
"Needs Triage"
] | Arguments in train_test_split not being recognised.
### Describe the bug
When using the train_test_split function, arguments such as "test_size" and "random_state" are not being recognized, generating an unexpected keyword argument TypeError.
### Steps/Code to Reproduce
```
x_train, x_test, y_train, y_test = tra... | 29,679 | [
0.017323845997452736,
-0.03236859664320946,
-0.0017573174554854631,
0.009973789565265179,
0.03783130273222923,
-0.00332155404612422,
0.11599356681108475,
0.05634629726409912,
0.030544113367795944,
-0.055490538477897644,
0.023807646706700325,
-0.015056307427585125,
-0.027050066739320755,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29679 | [
"Bug",
"Needs Triage"
] | Arguments in train_test_split not being recognised.
### Describe the bug
When using the train_test_split function, arguments such as "test_size" and "random_state" are not being recognized, generating an unexpected keyword argument TypeError.
### Steps/Code to Reproduce
```
x_train, x_test, y_train, y_test = tra... | 29,679 | [
0.017323845997452736,
-0.03236859664320946,
-0.0017573174554854631,
0.009973789565265179,
0.03783130273222923,
-0.00332155404612422,
0.11599356681108475,
0.05634629726409912,
0.030544113367795944,
-0.055490538477897644,
0.023807646706700325,
-0.015056307427585125,
-0.027050066739320755,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29678 | [
"Bug"
] | root_mean_squared_log_error & mean_squared_log_error: ValueError should be raised only if y_true or y_pred contain a value below -1, not below 0
### Describe the bug
For the `sklearn.metrics.root_mean_squared_log_error(y_true, y_pred)` & `sklearn.metrics.mean_squared_log_error(y_true, y_pred)` evaluation metrics, if ... | 29,678 | [
-0.02097829431295395,
-0.0720432847738266,
0.045022737234830856,
-0.05362161993980408,
0.08187448233366013,
-0.018307967111468315,
0.021642496809363365,
0.006094550248235464,
-0.003961367532610893,
-0.01613021269440651,
0.03530960902571678,
-0.04824459180235863,
0.010451365262269974,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29673 | [
"New Feature",
"Needs Investigation",
"Array API"
] | Array API backends support for MLX
It would be great to get the scikit-learn Array API back-end to be compatible with MLX (which is mostly conformant with the array API).
Here is an example which currently does not work for a few reasons:
```python
from sklearn.datasets import make_classification
from sklearn... | 29,673 | [
-0.030486151576042175,
0.04818673059344292,
0.0065563153475522995,
0.02545657753944397,
0.05114701762795448,
0.004074106924235821,
0.09351459890604019,
0.014843890443444252,
0.018232744187116623,
-0.05055474489927292,
-0.034583356231451035,
0.06342631578445435,
-0.042220838367938995,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/29670 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 14, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10381054335)** (Aug 14, 2024)
COMMENT:
Seems false positive, there's a download error. | 29,670 | [
-0.030802138149738312,
0.01004048902541399,
-0.015623812563717365,
-0.014180955477058887,
-0.0011550653725862503,
0.020610414445400238,
-0.004334877245128155,
0.05099179223179817,
-0.019406450912356377,
0.017228975892066956,
0.0684562623500824,
0.026796724647283554,
-0.01401551440358162,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29665 | [
"Performance",
"Regression",
"module:manifold"
] | TSNE performance regression in 1.5
### Describe the bug
The performance of TSNE transformation reduces when using n_jobs as 25 for the newer version w.r.t. 1.3.1.
version 1.3.1
```
df = np.random.rand(30000, 3)
tsne = TSNE(n_components=2, random_state=42, n_jobs=25, verbose=10, n_iter=1500)
```
1.5.1
```
df ... | 29,665 | [
-0.02984398789703846,
-0.02010856196284294,
-0.013060822151601315,
0.010133686475455761,
-0.03676026314496994,
-0.03061475232243538,
0.06292644888162613,
0.02556518092751503,
-0.07815796881914139,
-0.027773167937994003,
0.018232449889183044,
0.007607362233102322,
0.02969963289797306,
0.029... |
https://github.com/scikit-learn/scikit-learn/issues/29663 | [
"Bug",
"Needs Triage"
] | `fetch_20newsgroups_vectorized` gives HTTP Error 403 Forbidden
### Describe the bug
This was also recently reported on [StackOverflow](https://stackoverflow.com/questions/78398259/lda-in-python-shows-403-error-in-fetching-20newsgroups-dataset). It appears that https://ndownloader.figshare.com is down.
### Steps/Code... | 29,663 | [
0.017864586785435677,
0.06619741022586823,
0.004949682392179966,
0.024863671511411667,
0.05293894559144974,
0.0291898176074028,
0.03993958234786987,
0.05107273906469345,
0.00017081190890166909,
0.02933594025671482,
-0.06686589866876602,
-0.03760722652077675,
0.010454828850924969,
-0.024644... |
https://github.com/scikit-learn/scikit-learn/issues/29655 | [
"Bug",
"Needs Triage"
] | GradientBoostingClassifier feature_importances_ is all zero
### Describe the bug
I'm using GradientBoostingClassifier on a rather small dataset (n=75) for classification & feature selection.
I'm grid searching (in cross validation) the best hyper-parameters for my data and on some grids I get 0 importance for every ... | 29,655 | [
-0.00929208006709814,
-0.020964065566658974,
0.02171352505683899,
0.0127241350710392,
0.05940455570816994,
0.005281568970531225,
0.01777384802699089,
0.00975404679775238,
-0.025460276752710342,
-0.0017178298439830542,
-0.014981068670749664,
-0.000661382800899446,
0.02828061953186989,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29655 | [
"Bug",
"Needs Triage"
] | GradientBoostingClassifier feature_importances_ is all zero
### Describe the bug
I'm using GradientBoostingClassifier on a rather small dataset (n=75) for classification & feature selection.
I'm grid searching (in cross validation) the best hyper-parameters for my data and on some grids I get 0 importance for every ... | 29,655 | [
-0.00929208006709814,
-0.020964065566658974,
0.02171352505683899,
0.0127241350710392,
0.05940455570816994,
0.005281568970531225,
0.01777384802699089,
0.00975404679775238,
-0.025460276752710342,
-0.0017178298439830542,
-0.014981068670749664,
-0.000661382800899446,
0.02828061953186989,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29655 | [
"Bug",
"Needs Triage"
] | GradientBoostingClassifier feature_importances_ is all zero
### Describe the bug
I'm using GradientBoostingClassifier on a rather small dataset (n=75) for classification & feature selection.
I'm grid searching (in cross validation) the best hyper-parameters for my data and on some grids I get 0 importance for every ... | 29,655 | [
-0.00929208006709814,
-0.020964065566658974,
0.02171352505683899,
0.0127241350710392,
0.05940455570816994,
0.005281568970531225,
0.01777384802699089,
0.00975404679775238,
-0.025460276752710342,
-0.0017178298439830542,
-0.014981068670749664,
-0.000661382800899446,
0.02828061953186989,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29652 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 11, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10336780837)** (Aug 11, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/10345565152) on Aug 12... | 29,652 | [
-0.03176969662308693,
0.050788577646017075,
-0.018194638192653656,
-0.013760117813944817,
0.009497568011283875,
0.010386771522462368,
0.015244766138494015,
0.0409894734621048,
-0.04918425902724266,
0.03839755058288574,
0.08700934797525406,
0.025205062702298164,
-0.01591568812727928,
0.0839... |
https://github.com/scikit-learn/scikit-learn/issues/29650 | [
"Documentation",
"Build / CI"
] | Expand build from source docs for debugging with meson
From https://github.com/scikit-learn/scikit-learn/pull/29594#issuecomment-2260154987 and https://github.com/scikit-learn/scikit-learn/pull/29594#issuecomment-2260158387:
> Could you please open a follow-up PR that expands either our "build from source" document... | 29,650 | [
-0.007715233135968447,
0.013051425106823444,
0.000561521272175014,
-0.05807298794388771,
0.028722576797008514,
-0.010021953843533993,
0.006738640833646059,
-0.027898471802473068,
-0.025238489732146263,
-0.020990867167711258,
0.07441242039203644,
0.07344090938568115,
-0.04100663959980011,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29648 | [
"Bug",
"Needs Triage"
] | GaussianNB(priors=...) is useless
### Describe the bug
If I set the class priors to be very small for classes 0 and 2 and very large for class 1, I expect my predictions to be of class 1. However, I get class 0. It seems to be that `GaussianNB(priors=...)` is useless.
### Steps/Code to Reproduce
```python
fr... | 29,648 | [
-0.00357073824852705,
0.03334963321685791,
0.036621496081352234,
0.03285180777311325,
0.04471125081181526,
-0.022005733102560043,
0.04166864603757858,
0.011099468916654587,
-0.05567963421344757,
-0.04049723222851753,
0.0022963006049394608,
-0.014506122097373009,
0.02090311236679554,
0.0053... |
https://github.com/scikit-learn/scikit-learn/issues/29648 | [
"Bug",
"Needs Triage"
] | GaussianNB(priors=...) is useless
### Describe the bug
If I set the class priors to be very small for classes 0 and 2 and very large for class 1, I expect my predictions to be of class 1. However, I get class 0. It seems to be that `GaussianNB(priors=...)` is useless.
### Steps/Code to Reproduce
```python
fr... | 29,648 | [
-0.00357073824852705,
0.03334963321685791,
0.036621496081352234,
0.03285180777311325,
0.04471125081181526,
-0.022005733102560043,
0.04166864603757858,
0.011099468916654587,
-0.05567963421344757,
-0.04049723222851753,
0.0022963006049394608,
-0.014506122097373009,
0.02090311236679554,
0.0053... |
https://github.com/scikit-learn/scikit-learn/issues/29648 | [
"Bug",
"Needs Triage"
] | GaussianNB(priors=...) is useless
### Describe the bug
If I set the class priors to be very small for classes 0 and 2 and very large for class 1, I expect my predictions to be of class 1. However, I get class 0. It seems to be that `GaussianNB(priors=...)` is useless.
### Steps/Code to Reproduce
```python
fr... | 29,648 | [
-0.00357073824852705,
0.03334963321685791,
0.036621496081352234,
0.03285180777311325,
0.04471125081181526,
-0.022005733102560043,
0.04166864603757858,
0.011099468916654587,
-0.05567963421344757,
-0.04049723222851753,
0.0022963006049394608,
-0.014506122097373009,
0.02090311236679554,
0.0053... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.07065202295780182,
0.014877455309033394,
-0.018983835354447365,
-0.05833215266466141,
0.023648900911211967,
0.05016909912228584,
0.003988563548773527,
0.017191991209983826,
-0.01681055873632431,
-0.018772747367620468,
-0.0011233668774366379,
0.047290001064538956,
0.006417290307581425,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.08151703327894211,
0.026976145803928375,
-0.016243191435933113,
-0.04833400249481201,
0.022282395511865616,
0.05150417238473892,
0.012314225547015667,
0.014593754895031452,
-0.011776656843721867,
-0.01910712569952011,
0.006024658679962158,
0.055219847708940506,
0.0009371218620799482,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.07993241399526596,
0.02613256871700287,
-0.019979864358901978,
-0.04596361145377159,
0.02230731211602688,
0.047618113458156586,
0.011453566141426563,
-0.0022864469792693853,
-0.020947018638253212,
-0.007998528890311718,
0.001420487998984754,
0.04426005855202675,
0.002295686863362789,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.06567567586898804,
0.019208798184990883,
-0.01977638714015484,
-0.05535340681672096,
0.024061497300863266,
0.04896154999732971,
0.008291198872029781,
0.015418833121657372,
-0.01387709379196167,
-0.01360037550330162,
-0.007677244022488594,
0.05200548097491264,
0.005985219497233629,
0.0035... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.06623439490795135,
0.016843443736433983,
-0.02374584600329399,
-0.05188518017530441,
0.02250933274626732,
0.0453285351395607,
0.012831300497055054,
0.01543612964451313,
-0.01519838348031044,
-0.011895228177309036,
-0.004254926927387714,
0.049229562282562256,
0.008052562363445759,
0.00731... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.06712218374013901,
0.013691779226064682,
-0.020199870690703392,
-0.05566515401005745,
0.023567117750644684,
0.05054888874292374,
0.0030625746585428715,
0.021548790857195854,
-0.01028858870267868,
-0.021234504878520966,
0.002148818224668503,
0.05245702341198921,
0.004252975340932608,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/29643 | [
"Documentation",
"Needs Triage"
] | Update Twitter to X Throughout the Repository
### Describe the issue linked to the documentation
With the recent rebranding of Twitter to X, several references to **Twitter** in the `scikit-learn` repository need to be updated to reflect this change. This includes updating URLs and any textual references across multi... | 29,643 | [
0.06667716056108475,
0.010872622020542622,
-0.020165883004665375,
-0.058021605014801025,
0.025515403598546982,
0.05073898658156395,
0.003049299120903015,
0.016743982210755348,
-0.013059135526418686,
-0.017341820523142815,
-0.002314549870789051,
0.05036497116088867,
0.005734721664339304,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29642 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Aug 09, 2024) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69335&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Aug 09, 2024)
- Test Collection Failure
COMMENT:
##... | 29,642 | [
-0.007073931396007538,
0.042530130594968796,
-0.021976811811327934,
-0.03264547139406204,
0.039802294224500656,
0.0073744808323681355,
0.03777455538511276,
0.047845397144556046,
-0.02557070553302765,
0.028027629479765892,
0.045134395360946655,
0.03229323774576187,
-0.004215556662529707,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29640 | [
"Bug",
"Needs Triage"
] | BinMapper within HGBT does not handle sample weights
### Describe the bug
BinMapper under _hist_gradient_boosting does not accept sample weights as input leading to mismatch of bin thresholds outputted when calculating weighted versus repeated samples. Linked to Issue #27117
### Steps/Code to Reproduce
```pyt... | 29,640 | [
-0.012668708339333534,
-0.022112105041742325,
0.047112103551626205,
-0.025730721652507782,
0.028862396255135536,
-0.037511665374040604,
-0.013639818876981735,
0.06081859767436981,
0.003441271372139454,
-0.00942961499094963,
0.019490111619234085,
0.0436430424451828,
0.0059540048241615295,
-... |
https://github.com/scikit-learn/scikit-learn/issues/29640 | [
"Bug",
"Needs Triage"
] | BinMapper within HGBT does not handle sample weights
### Describe the bug
BinMapper under _hist_gradient_boosting does not accept sample weights as input leading to mismatch of bin thresholds outputted when calculating weighted versus repeated samples. Linked to Issue #27117
### Steps/Code to Reproduce
```pyt... | 29,640 | [
-0.012668708339333534,
-0.022112105041742325,
0.047112103551626205,
-0.025730721652507782,
0.028862396255135536,
-0.037511665374040604,
-0.013639818876981735,
0.06081859767436981,
0.003441271372139454,
-0.00942961499094963,
0.019490111619234085,
0.0436430424451828,
0.0059540048241615295,
-... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29633 | [
"Bug"
] | test_svm fails on i386 with scipy 1.13
### Describe the bug
scipy 1.13 is triggering test failure in test_svc_ovr_tie_breaking[NuSVC] on i386 architecture.
The error can be seeing in debian CI tests, https://ci.debian.net/packages/s/scikit-learn/unstable/i386/
Full test log at https://ci.debian.net/packages/s/s... | 29,633 | [
0.00006925622437847778,
-0.030257003381848335,
0.003999031148850918,
0.006424678023904562,
0.052739936858415604,
0.014067410491406918,
0.022385375574231148,
0.09762705117464066,
0.016658607870340347,
-0.017813747748732567,
0.03279610723257065,
0.06391996890306473,
0.0013604395790025592,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.022441783919930458,
0.10851757973432541,
0.0014402623055502772,
-0.05181550979614258,
0.010443294420838356,
0.0028824389446526766,
-0.005987887736409903,
0.0349760502576828,
0.011989624239504337,
-0.03208949789404869,
0.08995243161916733,
0.05430486053228378,
-0.039455562829971313,
0.068... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.015136592090129852,
0.11641878634691238,
0.0017866793787106872,
-0.05698181688785553,
-0.0011569424532353878,
-0.006471352186053991,
-0.007486737798899412,
0.031746067106723785,
0.0015910903457552195,
-0.036179643124341965,
0.08557619899511337,
0.03745386749505997,
-0.03354514762759209,
... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.016741862520575523,
0.11643041670322418,
0.0005021546385250986,
-0.05433260649442673,
0.01017912570387125,
-0.0010256872046738863,
-0.004397832788527012,
0.03262803703546524,
0.019107060506939888,
-0.02956659533083439,
0.08942496031522751,
0.05767204985022545,
-0.044413696974515915,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.013194875791668892,
0.11261098086833954,
-0.009391266852617264,
-0.0502738393843174,
-0.007144290488213301,
-0.00006521619798149914,
-0.008115019649267197,
0.030610855668783188,
0.007733501959592104,
-0.03236057236790657,
0.0927305594086647,
0.05924922972917557,
-0.04160640761256218,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.013952493667602539,
0.1219954788684845,
0.002540482906624675,
-0.05150028318166733,
0.0076650758273899555,
0.006895665545016527,
0.0009553435374982655,
0.03349051997065544,
0.020256442949175835,
-0.02541212923824787,
0.07881928980350494,
0.04709475114941597,
-0.04534422978758812,
0.08669... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.013256624341011047,
0.1168292835354805,
0.00010845336510101333,
-0.05009312182664871,
0.009803865104913712,
-0.00012281244562473148,
-0.0022268507163971663,
0.03964262828230858,
0.01682567223906517,
-0.03218531608581543,
0.08463581651449203,
0.060159388929605484,
-0.04097690433263779,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.019461266696453094,
0.11425713449716568,
-0.005284145940095186,
-0.048043377697467804,
0.000771846272982657,
0.0013456917367875576,
-0.004155279137194157,
0.03906584531068802,
0.013952024281024933,
-0.030453965067863464,
0.07944082468748093,
0.04404181241989136,
-0.02968769520521164,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.01548367366194725,
0.08675163239240646,
-0.002294015372171998,
-0.05456174165010452,
0.002883723471313715,
0.004693467170000076,
0.004215192515403032,
0.03720298036932945,
0.024776292964816093,
-0.01830318383872509,
0.07416640967130661,
0.06943398714065552,
-0.03257396072149277,
0.046722... |
https://github.com/scikit-learn/scikit-learn/issues/29630 | [
"New Feature"
] | Maintenance releases for 1.1.x and 1.2.x with numpy < 2.0?
### Describe the workflow you want to enable
Having an environment file or requirement file with scikit-learn=1.1 or scikit-learn=1.2 will break, since neither supports numpy 2.0 but doesn't declare that.
Example:
```bash
$ conda create -n sklearn_nump... | 29,630 | [
0.01779874786734581,
0.11552497744560242,
0.0005266936495900154,
-0.05126665160059929,
0.007951676845550537,
0.0011143548181280494,
-0.00048078448162414134,
0.031546834856271744,
0.014886369928717613,
-0.03469912335276604,
0.08656935393810272,
0.05733658745884895,
-0.04206523671746254,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29629 | [
"Bug",
"Needs Triage"
] | plot_tree fails with ValueError Invalid RGBA argument
### Describe the bug
When using `plot_tree` with `filled=True` (so the nodes are colored), one sometimes gets a `ValueError` such as
```
Invalid RGBA argument: '#cb 3-8d'
```
The same `plot_tree` will work fine if `filled=False`, and draw a decision tree. Be... | 29,629 | [
0.01628878340125084,
-0.03254128620028496,
-0.0003552371053956449,
0.002572032157331705,
0.04020530730485916,
-0.0011838035425171256,
-0.05433874949812889,
0.02610965259373188,
-0.02103620581328869,
-0.025230368599295616,
0.01946449652314186,
0.03492217883467674,
0.011877891607582569,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/29627 | [
"Bug",
"Needs Triage"
] | Performance Degradation in FeatureUnion with String Columns when concatenate the outputs of the transformers
### Describe the bug
I am experiencing significant performance degradation when using FeatureUnion in a Pipeline with DataFrames that include string columns set to be concatenated in the passthrough, the execu... | 29,627 | [
-0.025673959404230118,
0.04092581942677498,
0.02510634809732437,
-0.011457202024757862,
0.04774407669901848,
0.006887112278491259,
0.05048287659883499,
-0.0006430764333344996,
-0.03770262748003006,
-0.021372299641370773,
0.029700927436351776,
-0.01504639070481062,
0.03926929831504822,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29627 | [
"Bug",
"Needs Triage"
] | Performance Degradation in FeatureUnion with String Columns when concatenate the outputs of the transformers
### Describe the bug
I am experiencing significant performance degradation when using FeatureUnion in a Pipeline with DataFrames that include string columns set to be concatenated in the passthrough, the execu... | 29,627 | [
-0.025673959404230118,
0.04092581942677498,
0.02510634809732437,
-0.011457202024757862,
0.04774407669901848,
0.006887112278491259,
0.05048287659883499,
-0.0006430764333344996,
-0.03770262748003006,
-0.021372299641370773,
0.029700927436351776,
-0.01504639070481062,
0.03926929831504822,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29627 | [
"Bug",
"Needs Triage"
] | Performance Degradation in FeatureUnion with String Columns when concatenate the outputs of the transformers
### Describe the bug
I am experiencing significant performance degradation when using FeatureUnion in a Pipeline with DataFrames that include string columns set to be concatenated in the passthrough, the execu... | 29,627 | [
-0.025673959404230118,
0.04092581942677498,
0.02510634809732437,
-0.011457202024757862,
0.04774407669901848,
0.006887112278491259,
0.05048287659883499,
-0.0006430764333344996,
-0.03770262748003006,
-0.021372299641370773,
0.029700927436351776,
-0.01504639070481062,
0.03926929831504822,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29627 | [
"Bug",
"Needs Triage"
] | Performance Degradation in FeatureUnion with String Columns when concatenate the outputs of the transformers
### Describe the bug
I am experiencing significant performance degradation when using FeatureUnion in a Pipeline with DataFrames that include string columns set to be concatenated in the passthrough, the execu... | 29,627 | [
-0.025673959404230118,
0.04092581942677498,
0.02510634809732437,
-0.011457202024757862,
0.04774407669901848,
0.006887112278491259,
0.05048287659883499,
-0.0006430764333344996,
-0.03770262748003006,
-0.021372299641370773,
0.029700927436351776,
-0.01504639070481062,
0.03926929831504822,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29627 | [
"Bug",
"Needs Triage"
] | Performance Degradation in FeatureUnion with String Columns when concatenate the outputs of the transformers
### Describe the bug
I am experiencing significant performance degradation when using FeatureUnion in a Pipeline with DataFrames that include string columns set to be concatenated in the passthrough, the execu... | 29,627 | [
-0.025673959404230118,
0.04092581942677498,
0.02510634809732437,
-0.011457202024757862,
0.04774407669901848,
0.006887112278491259,
0.05048287659883499,
-0.0006430764333344996,
-0.03770262748003006,
-0.021372299641370773,
0.029700927436351776,
-0.01504639070481062,
0.03926929831504822,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.012880703434348106,
0.06420958042144775,
0.035151418298482895,
-0.014486829750239849,
0.03040865622460842,
-0.04272351786494255,
0.04135558009147644,
-0.017133833840489388,
-0.011466462165117264,
0.0154626639559865,
0.01943318359553814,
0.038762371987104416,
-0.03635205328464508,
0.0707... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.013071957975625992,
0.07311797142028809,
0.035220272839069366,
-0.023617012426257133,
0.03347218409180641,
-0.03522792458534241,
0.0297915767878294,
-0.015508572570979595,
-0.013159912079572678,
0.03464280441403389,
0.028453955426812172,
0.035807449370622635,
-0.03910914435982704,
0.077... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.00955498218536377,
0.07845287770032883,
0.03463725745677948,
-0.02287406474351883,
0.023284928873181343,
-0.047471314668655396,
0.057090215384960175,
-0.027058465406298637,
-0.012894966639578342,
0.0289737731218338,
0.013797533698379993,
0.032561615109443665,
-0.02579493634402752,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
0.00929561909288168,
0.05519098788499832,
0.03152818605303764,
-0.004068413283675909,
0.017890803515911102,
-0.02779647894203663,
0.06547015905380249,
-0.004845174495130777,
0.012270482257008553,
0.01793118566274643,
-0.0072044567205011845,
0.03200314939022064,
-0.01983165554702282,
0.0339... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
0.0016720599960535765,
0.07551824301481247,
0.033965274691581726,
-0.005882553290575743,
0.019719121977686882,
-0.046275991946458817,
0.05832275003194809,
-0.027230869978666306,
0.017417091876268387,
0.017917856574058533,
0.004822030197829008,
0.032743025571107864,
-0.019824929535388947,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.020667806267738342,
0.05872461572289467,
0.029404740780591965,
-0.018875477835536003,
0.030902624130249023,
-0.04188133031129837,
0.046070441603660583,
-0.018691837787628174,
-0.011620412580668926,
0.022623952478170395,
0.021301884204149246,
0.04734596982598305,
-0.029368557035923004,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.02337784133851528,
0.05879199504852295,
0.029678519815206528,
-0.021103141829371452,
0.0299056563526392,
-0.03861760348081589,
0.05038349702954292,
-0.020716048777103424,
-0.011770418845117092,
0.025342904031276703,
0.02154320478439331,
0.04465803876519203,
-0.035000309348106384,
0.0837... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.018373893573880196,
0.053886815905570984,
0.0285883080214262,
-0.019544687122106552,
0.030924662947654724,
-0.04003949463367462,
0.049264095723629,
-0.019252104684710503,
-0.01025870256125927,
0.024737969040870667,
0.0233006589114666,
0.047586046159267426,
-0.027114933356642723,
0.07883... |
https://github.com/scikit-learn/scikit-learn/issues/29626 | [
"Enhancement",
"module:neighbors"
] | Add optional return of STD for kNeighboursRegressor
### Describe the workflow you want to enable
I would like to propose to add the option to get the standard deviation from the KNeighborsRegressor. The `.predict()` function already delivers the mean, as that's the way the target is calculated, so adding the standard... | 29,626 | [
-0.014876075088977814,
0.061068095266819,
0.023383863270282745,
-0.017596535384655,
0.013853303156793118,
-0.05500951036810875,
0.05498313531279564,
-0.018237991258502007,
0.00860494002699852,
0.019292227923870087,
0.021518979221582413,
0.036552123725414276,
-0.033351194113492966,
0.059998... |
https://github.com/scikit-learn/scikit-learn/issues/29621 | [
"Bug"
] | mirrors-prettier pre-commit has been archived so maybe should be replaced
### Describe the bug
Noticed your [mirrors-prettier pre-commit](https://github.com/pre-commit/mirrors-prettier) has been archived. I was going to suggest you remove and/or look for alternative linters for the scss / js files.
### Steps/Code to... | 29,621 | [
0.007065231911838055,
0.013322336599230766,
0.002332152798771858,
-0.03673144429922104,
0.04108140245079994,
-0.04776639863848686,
0.01660173386335373,
0.058229923248291016,
-0.04518891125917435,
-0.007157582323998213,
-0.0022684172727167606,
-0.0038647670298814774,
0.027197841554880142,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29621 | [
"Bug"
] | mirrors-prettier pre-commit has been archived so maybe should be replaced
### Describe the bug
Noticed your [mirrors-prettier pre-commit](https://github.com/pre-commit/mirrors-prettier) has been archived. I was going to suggest you remove and/or look for alternative linters for the scss / js files.
### Steps/Code to... | 29,621 | [
0.0023586819879710674,
0.031131887808442116,
0.005299089010804892,
-0.04403620585799217,
0.027166930958628654,
-0.05069446191191673,
0.02109920233488083,
0.05303176864981651,
-0.057961706072092056,
-0.014300374314188957,
0.004825158976018429,
-0.005937206093221903,
0.02169775404036045,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29621 | [
"Bug"
] | mirrors-prettier pre-commit has been archived so maybe should be replaced
### Describe the bug
Noticed your [mirrors-prettier pre-commit](https://github.com/pre-commit/mirrors-prettier) has been archived. I was going to suggest you remove and/or look for alternative linters for the scss / js files.
### Steps/Code to... | 29,621 | [
0.009413382038474083,
0.023245103657245636,
0.008654629811644554,
-0.0420830063521862,
0.04465970769524574,
-0.044868554919958115,
0.020455915480852127,
0.04693694785237312,
-0.04948703572154045,
-0.005186667665839195,
-0.00019926043751183897,
0.005843773949891329,
0.022248748689889908,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29620 | [
"API",
"help wanted"
] | `base_estimator` in `Chain` classes while `estimator` is the convention in `Bagging` and `MultiOutput` classes?
### Describe the issue linked to the documentation
Currently most ensembling methods in `scikit-learn` such as [bagging methods](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingCla... | 29,620 | [
0.028422128409147263,
0.05010109767317772,
0.024626832455396652,
-0.021004870533943176,
-0.004207812715321779,
0.009488248266279697,
0.12765344977378845,
-0.0029877079650759697,
-0.017211342230439186,
-0.0028121336363255978,
0.0848921537399292,
0.013153322041034698,
0.010173502378165722,
-... |
https://github.com/scikit-learn/scikit-learn/issues/29620 | [
"API",
"help wanted"
] | `base_estimator` in `Chain` classes while `estimator` is the convention in `Bagging` and `MultiOutput` classes?
### Describe the issue linked to the documentation
Currently most ensembling methods in `scikit-learn` such as [bagging methods](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingCla... | 29,620 | [
0.026567691937088966,
0.04375934600830078,
0.025311417877674103,
-0.01894199103116989,
-0.0033744946122169495,
0.005667670164257288,
0.12690934538841248,
-0.006668425165116787,
-0.016464687883853912,
-0.0004695639945566654,
0.08285191655158997,
0.011848339810967445,
0.013649354688823223,
-... |
https://github.com/scikit-learn/scikit-learn/issues/29620 | [
"API",
"help wanted"
] | `base_estimator` in `Chain` classes while `estimator` is the convention in `Bagging` and `MultiOutput` classes?
### Describe the issue linked to the documentation
Currently most ensembling methods in `scikit-learn` such as [bagging methods](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingCla... | 29,620 | [
0.02946164272725582,
0.05486840382218361,
0.02628154121339321,
-0.016890523955225945,
0.0006121177575550973,
0.006539885886013508,
0.11521364748477936,
-0.006065567024052143,
-0.02060701698064804,
0.00017403317906428128,
0.08498277515172958,
0.01560002937912941,
0.016986770555377007,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/29620 | [
"API",
"help wanted"
] | `base_estimator` in `Chain` classes while `estimator` is the convention in `Bagging` and `MultiOutput` classes?
### Describe the issue linked to the documentation
Currently most ensembling methods in `scikit-learn` such as [bagging methods](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingCla... | 29,620 | [
0.027136676013469696,
0.04506934806704521,
0.024384809657931328,
-0.017235619947314262,
-0.0008921484113670886,
0.008098469115793705,
0.12492924183607101,
-0.005963620729744434,
-0.018278682604432106,
-0.0016147439600899816,
0.083943210542202,
0.0150757459923625,
0.012175184674561024,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29616 | [
"New Feature"
] | Student-t Mixture Model
### Describe the workflow you want to enable
Gaussian mixtures are extremely useful, but many datasets are noisy enough that a GMM fit can be challenging. In these cases, adding a degree of freedom by using a t distribution instead of a normal distribution can make fitting significantly simple... | 29,616 | [
0.020006980746984482,
0.03257332742214203,
0.015605161897838116,
-0.0023550493642687798,
0.04277323931455612,
0.004370999056845903,
0.0035993545316159725,
0.021184829995036125,
-0.0036626565270125866,
0.008333186618983746,
0.002114471746608615,
0.05130818858742714,
-0.026995349675416946,
0... |
https://github.com/scikit-learn/scikit-learn/issues/29616 | [
"New Feature"
] | Student-t Mixture Model
### Describe the workflow you want to enable
Gaussian mixtures are extremely useful, but many datasets are noisy enough that a GMM fit can be challenging. In these cases, adding a degree of freedom by using a t distribution instead of a normal distribution can make fitting significantly simple... | 29,616 | [
0.023768117651343346,
0.02644295059144497,
0.011403802782297134,
-0.008649161085486412,
0.04101709648966789,
0.0009205055539496243,
-0.008272974751889706,
0.032325275242328644,
-0.005286895204335451,
0.004262175410985947,
0.004849384538829327,
0.03657357022166252,
-0.027100486680865288,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29616 | [
"New Feature"
] | Student-t Mixture Model
### Describe the workflow you want to enable
Gaussian mixtures are extremely useful, but many datasets are noisy enough that a GMM fit can be challenging. In these cases, adding a degree of freedom by using a t distribution instead of a normal distribution can make fitting significantly simple... | 29,616 | [
0.024272551760077477,
0.01814272254705429,
0.008807488717138767,
-0.01572335511445999,
0.03152931481599808,
0.003771876683458686,
0.009667214937508106,
0.022317660972476006,
-0.010624502785503864,
0.0037309860344976187,
0.020947575569152832,
0.031157134100794792,
-0.021504633128643036,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/29616 | [
"New Feature"
] | Student-t Mixture Model
### Describe the workflow you want to enable
Gaussian mixtures are extremely useful, but many datasets are noisy enough that a GMM fit can be challenging. In these cases, adding a degree of freedom by using a t distribution instead of a normal distribution can make fitting significantly simple... | 29,616 | [
0.00868469849228859,
0.014865838922560215,
0.005969821475446224,
0.003200181992724538,
0.036447279155254364,
0.0019435336580500007,
0.0012876883847638965,
0.026740238070487976,
-0.011040925979614258,
0.009296673350036144,
0.005206541158258915,
0.037720464169979095,
-0.02391350083053112,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/29610 | [
"Bug",
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: Aug 08, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/10295577740)** (Aug 08, 2024)
COMMENT:
- The failure for the `cp313t-manylinux_x86_64-manylinux2014` build seems related to https://github.com/scikit-learn/s... | 29,610 | [
-0.011750889010727406,
0.040517840534448624,
-0.021143397316336632,
-0.023472266271710396,
-0.0019803233444690704,
0.05656792223453522,
0.025371836498379707,
0.04075063019990921,
-0.07265280187129974,
0.0015049013309180737,
0.06552275270223618,
0.01969398930668831,
-0.01553075760602951,
0.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.