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/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26401 | [
"Bug",
"module:linear_model"
] | Numpy Array Error when Training LogisticRegressionCV
### Describe the bug
When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
The inputs to ... | 26,401 | [
-0.01405349187552929,
-0.025075901299715042,
0.036797404289245605,
0.02275005541741848,
0.10777649283409119,
0.015199917368590832,
0.0625230073928833,
0.05177592113614082,
-0.0183577798306942,
0.02695980668067932,
0.019121939316391945,
-0.007563501130789518,
-0.006539834663271904,
0.027214... |
https://github.com/scikit-learn/scikit-learn/issues/26398 | [
"Bug",
"Needs Triage"
] | Custom Tie Breaking Criterion Voting Ensemble
### Describe the bug
We were wondering whether there is anything regarding customise the tie breaking criterion the Voting ensemble does ?
### Steps/Code to Reproduce
N/A
### Expected Results
N/A
### Actual Results
N/A
### Versions
```shell
N/A
```
COMMENT:
Close... | 26,398 | [
-0.00535349827259779,
-0.0030539496801793575,
-0.015073113143444061,
0.049258820712566376,
-0.023578934371471405,
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0.014739652164280415,
-0.021962197497487068,
0.07924520969390869,
0.015243465080857277,
0.012159827165305614,
... |
https://github.com/scikit-learn/scikit-learn/issues/26395 | [
"New Feature"
] | GridSearchCV support callback for MLFlow
### Describe the workflow you want to enable
I would like to save off the results of all runs in GridSearchCV to MLFlow. MLFlow
```python
for param in params:
with mlflow.start_run():
est = ElasticNet(**param)
est.fit(train_x, train_y)
m... | 26,395 | [
-0.03054739162325859,
0.039792995899915695,
0.02525923028588295,
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0.04381755739450455,
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0.003677366068586707,
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0.028885379433631897,
-0.015984054654836655,
-0.0029048901051282883,
0.08153825253248215,
-0.08488059043884277,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
-0.0542055107653141,
0.040325772017240524,
0.034781523048877716,
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0.05314524471759796,
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0.0039154463447630405,
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0.018007947131991386,
0.0040353029035031796,
0.05604879558086395,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
-0.057020027190446854,
0.04103882983326912,
0.032333020120859146,
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0.01973782666027546,
0.006388687063008547,
0.052907612174749374,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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0.033488351851701736,
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0.03895001485943794,
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0.06047549098730087,
... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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0.03183748200535774,
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0.05274811014533043,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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0.002493406180292368,
0.042222630232572556,
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0.0489732027053833,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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0.02752010151743889,
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0.06445058435201645,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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0.0031905705109238625,
0.05987550690770149,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
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https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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https://github.com/scikit-learn/scikit-learn/issues/26392 | [
"help wanted",
"module:decomposition"
] | NMF fit transform without updating H should not require the user to input "n_components"
The `_fit_transform` function of the `_nmf` module has the option to set `update_H=False`, where the H matrix is left constant. the private method `_fit_transform` is called by the exposed `non_negative_factorization` function.
I... | 26,392 | [
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https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.03877290338277817,
0.06183074787259102,
0.023155033588409424,
0.011133384890854359,
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0.03593011945486069,
0.07109... |
https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.04349273815751076,
0.05830078572034836,
0.024446746334433556,
0.009406021796166897,
0.012900063768029213,
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0.030351845547556877,
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0.009680476039648056,
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0.02774159237742424,
0.0792... |
https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.04106805473566055,
0.06535081565380096,
0.021113082766532898,
0.013388033956289291,
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0.02566082775592804,
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0.003761939238756895,
0.08469562232494354,
-0.005948708392679691,
0.03481869399547577,
0.0775... |
https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.04072137549519539,
0.06390088051557541,
0.020629623904824257,
0.012654491700232029,
0.009471253491938114,
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0.027844777330756187,
-0.021065739914774895,
-0.043034620583057404,
0.00394458370283246,
0.08640523999929428,
-0.013067199848592281,
0.034322984516620636,
0.07... |
https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.04266626760363579,
0.058529991656541824,
0.02061314508318901,
0.01238332875072956,
0.012287085875868797,
-0.04288601130247116,
0.026792043820023537,
-0.020545415580272675,
-0.03994201123714447,
0.005664689466357231,
0.08325950056314468,
-0.012675805948674679,
0.03554011508822441,
0.0697... |
https://github.com/scikit-learn/scikit-learn/issues/26390 | [
"Bug"
] | SplineTransformer(extrapolate="periodic") outputs nan values for constant features
While reviewing #24145 I discovered the following bug:
```python
In [1]: import numpy as np
In [2]: from sklearn.preprocessing import SplineTransformer
In [3]: SplineTransformer(extrapolation="periodic").fit_transform(np.ones(... | 26,390 | [
-0.04217721149325371,
0.05967029929161072,
0.021094562485814095,
0.012317143380641937,
0.012133724987506866,
-0.0415441058576107,
0.026928475126624107,
-0.020654473453760147,
-0.03981214016675949,
0.006973970681428909,
0.08499816060066223,
-0.011776220053434372,
0.034792058169841766,
0.069... |
https://github.com/scikit-learn/scikit-learn/issues/26370 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/6501559970824192)** (May 15, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/4585352225095680) on May 16, 2023 | 26,370 | [
-0.0210878923535347,
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0.011430979706346989,
0.0269... |
https://github.com/scikit-learn/scikit-learn/issues/26369 | [
"Bug",
"module:feature_selection"
] | SequentialFeatureSelector in backward auto mode will always remove one feature
https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/feature_selection/_sequential.py#L273
The initial value of `old_score` is incorrect if `direction == 'backward'`. With the current initial... | 26,369 | [
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0.01508384570479393,
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0.051207300275564194,
0.06545790284872055,
0.02434072643518448,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/26369 | [
"Bug",
"module:feature_selection"
] | SequentialFeatureSelector in backward auto mode will always remove one feature
https://github.com/scikit-learn/scikit-learn/blob/364c77e047ca08a95862becf40a04fe9d4cd2c98/sklearn/feature_selection/_sequential.py#L273
The initial value of `old_score` is incorrect if `direction == 'backward'`. With the current initial... | 26,369 | [
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0.02900262363255024,
0.0683499127626419,
0.0422336608171463,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/26364 | [
"New Feature"
] | Adding dark mode for pipeline diagram
### Describe the workflow you want to enable
The current diagram truly hurts eyes when everything else in dark mode. It would be a very nice feature to add.
![Screenshot 2023-05-14 100107](https://github.com/scikit-learn/scikit-learn/assets/95470962/bf9d7275-be3a-4864-82ce-362a7... | 26,364 | [
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0.010188816115260124,
0.028825554996728897,
-0.008101853542029858,
... |
https://github.com/scikit-learn/scikit-learn/issues/26364 | [
"New Feature"
] | Adding dark mode for pipeline diagram
### Describe the workflow you want to enable
The current diagram truly hurts eyes when everything else in dark mode. It would be a very nice feature to add.
![Screenshot 2023-05-14 100107](https://github.com/scikit-learn/scikit-learn/assets/95470962/bf9d7275-be3a-4864-82ce-362a7... | 26,364 | [
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0.008609777316451073,
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0.010188816115260124,
0.028825554996728897,
-0.008101853542029858,
... |
https://github.com/scikit-learn/scikit-learn/issues/26364 | [
"New Feature"
] | Adding dark mode for pipeline diagram
### Describe the workflow you want to enable
The current diagram truly hurts eyes when everything else in dark mode. It would be a very nice feature to add.
![Screenshot 2023-05-14 100107](https://github.com/scikit-learn/scikit-learn/assets/95470962/bf9d7275-be3a-4864-82ce-362a7... | 26,364 | [
-0.03387624770402908,
0.008609777316451073,
-0.009139124304056168,
-0.023291701450943947,
-0.007617396302521229,
-0.014442874118685722,
0.04500017687678337,
0.026630980893969536,
0.010553109459578991,
-0.03125043585896492,
0.010188816115260124,
0.028825554996728897,
-0.008101853542029858,
... |
https://github.com/scikit-learn/scikit-learn/issues/26364 | [
"New Feature"
] | Adding dark mode for pipeline diagram
### Describe the workflow you want to enable
The current diagram truly hurts eyes when everything else in dark mode. It would be a very nice feature to add.
![Screenshot 2023-05-14 100107](https://github.com/scikit-learn/scikit-learn/assets/95470962/bf9d7275-be3a-4864-82ce-362a7... | 26,364 | [
-0.03387624770402908,
0.008609777316451073,
-0.009139124304056168,
-0.023291701450943947,
-0.007617396302521229,
-0.014442874118685722,
0.04500017687678337,
0.026630980893969536,
0.010553109459578991,
-0.03125043585896492,
0.010188816115260124,
0.028825554996728897,
-0.008101853542029858,
... |
https://github.com/scikit-learn/scikit-learn/issues/26364 | [
"New Feature"
] | Adding dark mode for pipeline diagram
### Describe the workflow you want to enable
The current diagram truly hurts eyes when everything else in dark mode. It would be a very nice feature to add.
![Screenshot 2023-05-14 100107](https://github.com/scikit-learn/scikit-learn/assets/95470962/bf9d7275-be3a-4864-82ce-362a7... | 26,364 | [
-0.03387624770402908,
0.008609777316451073,
-0.009139124304056168,
-0.023291701450943947,
-0.007617396302521229,
-0.014442874118685722,
0.04500017687678337,
0.026630980893969536,
0.010553109459578991,
-0.03125043585896492,
0.010188816115260124,
0.028825554996728897,
-0.008101853542029858,
... |
https://github.com/scikit-learn/scikit-learn/issues/26363 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/6515525694521344)** (May 14, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/6501559970824192) on May 15, 2023 | 26,363 | [
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-0.007112826686352491,
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0.013536203652620316,
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0.02143963612616062,
0.050453219562768936,
0.009522705338895321,
0.011770973913371563,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
-0.042932286858558655,
0.024655988439917564,
0.009710336104035378,
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0.043130770325660706,
-0.0029078421648591757,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
-0.03858224302530289,
0.017931558191776276,
0.018453504890203476,
0.003880868200212717,
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0.05290251597762108,
0.018353428691625595,
0.04783... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
-0.035644154995679855,
0.0018992124823853374,
0.020748799666762352,
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0.07845439016819,
0.007482217159122229,
0.05098008... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
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0.004745386075228453,
0.02742951177060604,
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0.006025391165167093,
0.046372... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
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0.013400819152593613,
0.0440... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
-0.034744229167699814,
0.05609144642949104,
0.022648684680461884,
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0.044579099863767624,
-0.008043616078794003,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26359 | [
"Documentation",
"module:linear_model"
] | Selecting Lasso via cross-validation data leakage
### Describe the issue linked to the documentation
```py
model = make_pipeline(StandardScaler(), LassoCV(cv=20)).fit(X, y)
```
Optimizing Alpha for lasso involves data leakage because standard scaling is applied on X and y in one go, instant for each fold.
I... | 26,359 | [
-0.039609890431165695,
0.008576002903282642,
0.025026444345712662,
0.0005811084993183613,
0.07798663526773453,
-0.020997976884245872,
0.0452047623693943,
0.011846429668366909,
0.08103469759225845,
0.024779414758086205,
-0.012567606754601002,
0.07691328227519989,
0.00887588132172823,
0.0539... |
https://github.com/scikit-learn/scikit-learn/issues/26358 | [
"New Feature",
"spam",
"Needs Triage"
] | Mean Absolute Percentage Error
### Describe the workflow you want to enable
Mean Absolute Percentage Error (MAPE): MAPE is a new evaluation metric for a regression problem. It is calculated as the mean absolute percentage error between the predicted and actual values. MAPE is a more robust metric than other metrics s... | 26,358 | [
-0.061488162726163864,
0.03289287909865379,
0.01329308282583952,
-0.03638669475913048,
0.014135906472802162,
0.009056845679879189,
-0.050233613699674606,
0.020854167640209198,
0.023591386154294014,
0.020976535975933075,
0.04563256725668907,
-0.03745537996292114,
-0.016710899770259857,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26358 | [
"New Feature",
"spam",
"Needs Triage"
] | Mean Absolute Percentage Error
### Describe the workflow you want to enable
Mean Absolute Percentage Error (MAPE): MAPE is a new evaluation metric for a regression problem. It is calculated as the mean absolute percentage error between the predicted and actual values. MAPE is a more robust metric than other metrics s... | 26,358 | [
-0.061488162726163864,
0.03289287909865379,
0.01329308282583952,
-0.03638669475913048,
0.014135906472802162,
0.009056845679879189,
-0.050233613699674606,
0.020854167640209198,
0.023591386154294014,
0.020976535975933075,
0.04563256725668907,
-0.03745537996292114,
-0.016710899770259857,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26348 | [
"Array API"
] | Add common tests for estimators that support the Array API
This issue is about agreeing on what to do about common tests for estimators that support the Array API.
There are two things we need to test for every estimator for which we add Array API support:
1. does it work with a selection of Array API implementati... | 26,348 | [
-0.006173721514642239,
0.09831628203392029,
-0.005646625533699989,
-0.0009202549699693918,
-0.013902775943279266,
-0.004321807995438576,
0.09952815622091293,
0.04059259966015816,
0.052614666521549225,
-0.011212168261408806,
0.01455863006412983,
0.0016896625747904181,
0.01431808341294527,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26348 | [
"Array API"
] | Add common tests for estimators that support the Array API
This issue is about agreeing on what to do about common tests for estimators that support the Array API.
There are two things we need to test for every estimator for which we add Array API support:
1. does it work with a selection of Array API implementati... | 26,348 | [
-0.006173721514642239,
0.09831628203392029,
-0.005646625533699989,
-0.0009202549699693918,
-0.013902775943279266,
-0.004321807995438576,
0.09952815622091293,
0.04059259966015816,
0.052614666521549225,
-0.011212168261408806,
0.01455863006412983,
0.0016896625747904181,
0.01431808341294527,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26348 | [
"Array API"
] | Add common tests for estimators that support the Array API
This issue is about agreeing on what to do about common tests for estimators that support the Array API.
There are two things we need to test for every estimator for which we add Array API support:
1. does it work with a selection of Array API implementati... | 26,348 | [
-0.006173721514642239,
0.09831628203392029,
-0.005646625533699989,
-0.0009202549699693918,
-0.013902775943279266,
-0.004321807995438576,
0.09952815622091293,
0.04059259966015816,
0.052614666521549225,
-0.011212168261408806,
0.01455863006412983,
0.0016896625747904181,
0.01431808341294527,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26348 | [
"Array API"
] | Add common tests for estimators that support the Array API
This issue is about agreeing on what to do about common tests for estimators that support the Array API.
There are two things we need to test for every estimator for which we add Array API support:
1. does it work with a selection of Array API implementati... | 26,348 | [
-0.006173721514642239,
0.09831628203392029,
-0.005646625533699989,
-0.0009202549699693918,
-0.013902775943279266,
-0.004321807995438576,
0.09952815622091293,
0.04059259966015816,
0.052614666521549225,
-0.011212168261408806,
0.01455863006412983,
0.0016896625747904181,
0.01431808341294527,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26347 | [
"New Feature",
"Needs Triage"
] | RandomForestRegressor() producing mainly constant forecast results over time-series data
### Describe the workflow you want to enable
Let's say I have [dataset](https://drive.google.com/file/d/18PGLNnOI44LVFignYriBWQFW9WBkTX5c/view?usp=share_link) contains a timestamp (non-standard timestamp column without datetime... | 26,347 | [
0.03507032245397568,
0.07523710280656815,
0.030744941905140877,
-0.03718052804470062,
0.01915314607322216,
-0.01239829882979393,
0.011818597093224525,
0.007596359588205814,
-0.017788615077733994,
0.030451921746134758,
0.031690798699855804,
-0.05358555540442467,
0.009806882590055466,
0.0743... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.0007072783773764968,
-0.009373686276376247,
-0.016025206074118614,
-0.05003567412495613,
0.0387096032500267,
0.009112800471484661,
0.05046005919575691,
0.01943599060177803,
0.0018556728027760983,
-0.0009260562364943326,
-0.005438775755465031,
0.04132409021258354,
-0.027623433619737625,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.0065216850489377975,
-0.024144532158970833,
-0.021220406517386436,
-0.05327523127198219,
0.032118506729602814,
0.013949595391750336,
0.049935270100831985,
0.029925137758255005,
0.00856588501483202,
-0.0051360176876187325,
-0.002191385952755809,
0.057267237454652786,
-0.02198711968958378,
... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.003546687075868249,
-0.024927135556936264,
-0.02389695681631565,
-0.05842575430870056,
0.03284751623868942,
0.016358185559511185,
0.049903254956007004,
0.027527224272489548,
0.004150843247771263,
-0.007241389714181423,
0.0006942374748177826,
0.05364206060767174,
-0.019964048638939857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.0005711976555176079,
-0.0204248558729887,
-0.025802399963140488,
-0.06151712313294411,
0.03362186253070831,
0.014003125950694084,
0.047202952206134796,
0.027109211310744286,
0.0024182561319321394,
-0.006592727731913328,
-0.0011984937591478229,
0.05607142299413681,
-0.01952410861849785,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.004510448779910803,
-0.02183305285871029,
-0.0213130135089159,
-0.05264155566692352,
0.03844476863741875,
0.01233663409948349,
0.04968021810054779,
0.03051302209496498,
0.00841862615197897,
-0.002330080373212695,
-0.004586514551192522,
0.052361875772476196,
-0.023133790120482445,
0.02432... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.0006338949897326529,
-0.0204265546053648,
-0.023306982591748238,
-0.05941295251250267,
0.029327286407351494,
0.012137601152062416,
0.04667143523693085,
0.0316813588142395,
0.008639061823487282,
-0.006828209385275841,
0.0021785555873066187,
0.05279970169067383,
-0.026035014539957047,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
-0.0015276530757546425,
-0.01680028811097145,
-0.022780029103159904,
-0.06093157082796097,
0.03202061355113983,
0.007731605786830187,
0.04973938688635826,
0.033576082438230515,
0.005316284950822592,
-0.01032585371285677,
0.0009742832044139504,
0.052209172397851944,
-0.025486821308732033,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.008464636281132698,
-0.010689659044146538,
-0.019288336858153343,
-0.057109296321868896,
0.03801116719841957,
0.010769406333565712,
0.05224214494228363,
0.028114473447203636,
0.006243874318897724,
-0.007207817863672972,
0.0023252808023244143,
0.05183393135666847,
-0.023398099467158318,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26343 | [
"Bug"
] | Warning due to `lscpu` on MacOS on nightly builds
I created a conda environment containing the `scipy-dev` packages (to solve the deprecation warning shown in our CI). Once I built scikit-learn, I get the following error when importing:
```python
> ipython
Python 3.10.10 | packaged by conda-forge | (main, Mar 24 ... | 26,343 | [
0.0007152568432502449,
-0.014281227253377438,
-0.023776322603225708,
-0.06096457317471504,
0.03599527105689049,
0.012823483906686306,
0.04627044498920441,
0.03287426009774208,
0.0068258848041296005,
-0.009958351962268353,
0.004581485874950886,
0.053280025720596313,
-0.02445153519511223,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26342 | [
"Needs Triage"
] | ⚠️ CI failed on linux_arm64_wheel ⚠️
**CI failed on [linux_arm64_wheel](https://cirrus-ci.com/build/6302053069225984)** (May 07, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://cirrus-ci.com/build/4827479530012672) on May 08, 2023 | 26,342 | [
-0.020707421004772186,
-0.01312301866710186,
-0.033766310662031174,
-0.027713043615221977,
0.013825667090713978,
0.03262975066900253,
0.012742034159600735,
0.04242544248700142,
-0.057742536067962646,
0.021134311333298683,
0.04846939817070961,
0.008307578973472118,
0.011721850372850895,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26336 | [
"Bug",
"Needs Triage"
] | No attribute classes_ during multi-class scoring
### Describe the bug
Regression we hit in MNE-Python's `pip-pre` run and bisected locally:
https://github.com/mne-tools/mne-python/actions/runs/4894775241/jobs/8739516519#step:17:4138
Local bisect suggests the culprit is #26037 by @glemaitre
It's entirely po... | 26,336 | [
-0.03642798960208893,
0.023906519636511803,
0.034959208220243454,
0.025494536384940147,
0.09851076453924179,
-0.010825986042618752,
0.033609818667173386,
0.04705754294991493,
-0.03909635916352272,
-0.03862348943948746,
0.0037384515162557364,
0.05902488902211189,
-0.007879849523305893,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26331 | [
"Needs Reproducible Code"
] | LinearSVC crashes with no errors when `max_iter` is hit
### Describe the bug
LinearSVC seems to fail with no warnings.
I think it is when the number of iterations hit `max_iter` .
I've set `verbose=1` and `max_iter=1000`, and the exact moment it printed `iter 1000` the program crashed
### Steps/Code to Repr... | 26,331 | [
-0.002756076864898205,
-0.013152167201042175,
0.024563072249293327,
0.03649076074361801,
0.11969587206840515,
0.0009643074590712786,
-0.030833492055535316,
0.05738499388098717,
0.023605631664395332,
0.015562169253826141,
0.056391775608062744,
0.047270163893699646,
-0.03669885918498039,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26329 | [
"API"
] | API Allow users to pass instances of `DistanceMetric` directly to `metric` keyword arguments
# Motivation
SIMD intrinsics can accelerate pairwise distance computation by a factors of ~2.5-3.5x for `float64` data, and ~5-6x for `float32` data (benchmarked by this gist: https://gist.github.com/Micky774/bd1b8394fdaa82... | 26,329 | [
-0.02658916264772415,
0.04085882380604744,
0.004761713091284037,
0.006801528390496969,
-0.03514943644404411,
0.004717547446489334,
0.05230012163519859,
0.03840790316462517,
-0.00039849799941293895,
-0.007871930487453938,
0.008761568926274776,
0.025226930156350136,
-0.02702736295759678,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26328 | [
"Bug",
"Needs Triage"
] | Cross validation error of a Gaussian process with noisy target
### Describe the bug
Hi,
I'm trying to use RandomizedSearchCV with GP with a vector of alpha's.
It seems that with the cross validation the alpha are not being split into train/test sets because I get the following error:
ValueError: alpha must be... | 26,328 | [
-0.007092604413628578,
-0.0011635294649749994,
0.034945499151945114,
0.02181434817612171,
0.11492225527763367,
-0.055021606385707855,
0.013683944940567017,
0.009124762378633022,
-0.00751228304579854,
0.008531864732503891,
0.009328585118055344,
0.02056567184627056,
0.008804322220385075,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26328 | [
"Bug",
"Needs Triage"
] | Cross validation error of a Gaussian process with noisy target
### Describe the bug
Hi,
I'm trying to use RandomizedSearchCV with GP with a vector of alpha's.
It seems that with the cross validation the alpha are not being split into train/test sets because I get the following error:
ValueError: alpha must be... | 26,328 | [
-0.007092604413628578,
-0.0011635294649749994,
0.034945499151945114,
0.02181434817612171,
0.11492225527763367,
-0.055021606385707855,
0.013683944940567017,
0.009124762378633022,
-0.00751228304579854,
0.008531864732503891,
0.009328585118055344,
0.02056567184627056,
0.008804322220385075,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26326 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | Add a facility that allows random forest classifiers to be combined after training
### Describe the workflow you want to enable
In a federated environment, I have federation elements that build private random forest classifiers, which I would like to combine after the fact into a single random forest.
### Desc... | 26,326 | [
0.0029908940196037292,
0.10911037772893906,
0.00673880148679018,
-0.02149222418665886,
0.02840609662234783,
-0.0009483815520070493,
0.014043307863175869,
-0.03036980703473091,
-0.010006910189986229,
-0.00993532408028841,
-0.013620284385979176,
-0.04801523685455322,
0.020451005548238754,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26326 | [
"New Feature",
"module:ensemble",
"Needs Decision - Include Feature"
] | Add a facility that allows random forest classifiers to be combined after training
### Describe the workflow you want to enable
In a federated environment, I have federation elements that build private random forest classifiers, which I would like to combine after the fact into a single random forest.
### Desc... | 26,326 | [
0.0029908940196037292,
0.10911037772893906,
0.00673880148679018,
-0.02149222418665886,
0.02840609662234783,
-0.0009483815520070493,
0.014043307863175869,
-0.03036980703473091,
-0.010006910189986229,
-0.00993532408028841,
-0.013620284385979176,
-0.04801523685455322,
0.020451005548238754,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26324 | [
"Bug",
"Moderate",
"help wanted"
] | OPTICS not detecting last data as outlier
This needs investigation.
Thanks for reporting it @yagao7411
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/26304
<div type='discussions-op-text'>
<sup>Originally posted by **yagao7411** April 30, 2023</sup>
Hi, I am trying to use skle... | 26,324 | [
0.01975863426923752,
-0.07324114441871643,
0.012417448684573174,
0.03980202227830887,
0.019083529710769653,
-0.02378036454319954,
0.0348198339343071,
-0.010577002540230751,
0.01692901737987995,
0.021934112533926964,
0.0409667082130909,
0.056236110627651215,
0.032419297844171524,
-0.0111472... |
https://github.com/scikit-learn/scikit-learn/issues/26321 | [
"Bug",
"module:covariance"
] | Duality gap computation in covariance.GraphicalLasso yields negative values.
### Describe the bug
The computation of the duality gap in `_dual_gap(emp_cov, precision_, alpha)` of `GraphicalLasso` uses the definition from `Duchi et al., 2012`.
However, their duality gap is expressed given a _feasible_ dual variabl... | 26,321 | [
-0.026948444545269012,
-0.012503844685852528,
-0.0011484060669317842,
-0.0014352690195664763,
0.017526084557175636,
-0.005558086093515158,
-0.03500225767493248,
-0.008743248879909515,
-0.04689605161547661,
-0.008146755397319794,
0.003574835602194071,
-0.005068186204880476,
0.0524630397558212... |
https://github.com/scikit-learn/scikit-learn/issues/26321 | [
"Bug",
"module:covariance"
] | Duality gap computation in covariance.GraphicalLasso yields negative values.
### Describe the bug
The computation of the duality gap in `_dual_gap(emp_cov, precision_, alpha)` of `GraphicalLasso` uses the definition from `Duchi et al., 2012`.
However, their duality gap is expressed given a _feasible_ dual variabl... | 26,321 | [
-0.026948444545269012,
-0.012503844685852528,
-0.0011484060669317842,
-0.0014352690195664763,
0.017526084557175636,
-0.005558086093515158,
-0.03500225767493248,
-0.008743248879909515,
-0.04689605161547661,
-0.008146755397319794,
0.003574835602194071,
-0.005068186204880476,
0.0524630397558212... |
https://github.com/scikit-learn/scikit-learn/issues/26321 | [
"Bug",
"module:covariance"
] | Duality gap computation in covariance.GraphicalLasso yields negative values.
### Describe the bug
The computation of the duality gap in `_dual_gap(emp_cov, precision_, alpha)` of `GraphicalLasso` uses the definition from `Duchi et al., 2012`.
However, their duality gap is expressed given a _feasible_ dual variabl... | 26,321 | [
-0.026948444545269012,
-0.012503844685852528,
-0.0011484060669317842,
-0.0014352690195664763,
0.017526084557175636,
-0.005558086093515158,
-0.03500225767493248,
-0.008743248879909515,
-0.04689605161547661,
-0.008146755397319794,
0.003574835602194071,
-0.005068186204880476,
0.0524630397558212... |
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