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/28077 | [
"Needs Triage"
] | IsolationForest should maybe check for duplicate y values that are given to ExtraTreeRegressor.
My understanding is that IsolationForest uses ExtraTreeRegressor, which in turn inherits from DecisionTreeRegressor, with random y values to ensure that all leafs correspond to a single point (required for IsolationForest a... | 28,077 | [
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0.0031959605403244495,
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0.017371766269207,
-0.011... |
https://github.com/scikit-learn/scikit-learn/issues/28077 | [
"Needs Triage"
] | IsolationForest should maybe check for duplicate y values that are given to ExtraTreeRegressor.
My understanding is that IsolationForest uses ExtraTreeRegressor, which in turn inherits from DecisionTreeRegressor, with random y values to ensure that all leafs correspond to a single point (required for IsolationForest a... | 28,077 | [
0.017432590946555138,
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-0.008009213022887707,
0.012708127498626709,
... |
https://github.com/scikit-learn/scikit-learn/issues/28060 | [
"New Feature",
"Needs Triage"
] | Regression Probability Distribution & Multi-Quantile Output API
### Describe the workflow you want to enable
Scikit-learn has a `predict` and `predict_proba` method for Classification classes but only a `predict` method for regression, with the option of quantile. Scikit-learn is adding more quantile output functio... | 28,060 | [
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0.015458193607628345,
-0.0223983321338892,
0.07451... |
https://github.com/scikit-learn/scikit-learn/issues/28060 | [
"New Feature",
"Needs Triage"
] | Regression Probability Distribution & Multi-Quantile Output API
### Describe the workflow you want to enable
Scikit-learn has a `predict` and `predict_proba` method for Classification classes but only a `predict` method for regression, with the option of quantile. Scikit-learn is adding more quantile output functio... | 28,060 | [
-0.021182306110858917,
0.0707908421754837,
0.009809189476072788,
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0.015458193607628345,
-0.0223983321338892,
0.07451... |
https://github.com/scikit-learn/scikit-learn/issues/28060 | [
"New Feature",
"Needs Triage"
] | Regression Probability Distribution & Multi-Quantile Output API
### Describe the workflow you want to enable
Scikit-learn has a `predict` and `predict_proba` method for Classification classes but only a `predict` method for regression, with the option of quantile. Scikit-learn is adding more quantile output functio... | 28,060 | [
-0.021182306110858917,
0.0707908421754837,
0.009809189476072788,
-0.06147861108183861,
0.01852010004222393,
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0.058436863124370575,
0.011785048991441727,
0.01967606507241726,
-0.017596974968910217,
0.047555193305015564,
0.015458193607628345,
-0.0223983321338892,
0.07451... |
https://github.com/scikit-learn/scikit-learn/issues/28059 | [
"Enhancement"
] | ENH: Random Forest Classifier oob scaling/parallel
My team, working on a bioinformatics problem with high feature count (columns/dimensions in `X`), noticed that the `RandomForestClassifier` out of bag scoring doesn't scale with `n_jobs`. To be fair, `n_jobs` clearly says what it does support, though I do wonder if th... | 28,059 | [
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0.0013489945558831096,
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-0.0002185228659072891,
-0.026357660070061684,
... |
https://github.com/scikit-learn/scikit-learn/issues/28059 | [
"Enhancement"
] | ENH: Random Forest Classifier oob scaling/parallel
My team, working on a bioinformatics problem with high feature count (columns/dimensions in `X`), noticed that the `RandomForestClassifier` out of bag scoring doesn't scale with `n_jobs`. To be fair, `n_jobs` clearly says what it does support, though I do wonder if th... | 28,059 | [
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0.020307863131165504,
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0.014948522672057152,
0.003267662599682808,
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0.0013489945558831096,
-0.00246731867082417,
-0.0002185228659072891,
-0.026357660070061684,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28055 | [
"Bug"
] | Infinite Loop in K-means when relocating empty clusters
### Describe the bug
Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.
Kmeans is stuck ... | 28,055 | [
0.014311565086245537,
-0.045512933284044266,
-0.01108118798583746,
-0.002930400660261512,
0.05895204469561577,
-0.038722772151231766,
-0.029248250648379326,
-0.009712811559438705,
0.020859915763139725,
0.014063275419175625,
0.043670300394296646,
0.11152662336826324,
0.0039174361154437065,
... |
https://github.com/scikit-learn/scikit-learn/issues/28049 | [
"module:linear_model"
] | Plan for SGD and SAGA loss function migration
As a result of #15123, we now have a common private loss function module under `sklearn._loss`. In `sklearn.linear_models` we have 2 algorithms that need Cython version that calculate losses and gradients on single values (not on arrays), namely
- `_plain_sgd` as used in ... | 28,049 | [
-0.004473586566746235,
0.10300672799348831,
0.0047858720645308495,
-0.00032090110471472144,
0.022790709510445595,
-0.012226058170199394,
0.028319764882326126,
-0.028418676927685738,
-0.06775045394897461,
-0.036269403994083405,
0.027476605027914047,
0.01832181215286255,
-0.02235529199242592,
... |
https://github.com/scikit-learn/scikit-learn/issues/28049 | [
"module:linear_model"
] | Plan for SGD and SAGA loss function migration
As a result of #15123, we now have a common private loss function module under `sklearn._loss`. In `sklearn.linear_models` we have 2 algorithms that need Cython version that calculate losses and gradients on single values (not on arrays), namely
- `_plain_sgd` as used in ... | 28,049 | [
-0.001770923612639308,
0.10033056139945984,
0.003908311948180199,
0.0012458749115467072,
0.023123199120163918,
-0.014735650271177292,
0.029185228049755096,
-0.022774741053581238,
-0.06915439665317535,
-0.032446831464767456,
0.03457019850611687,
0.021015342324972153,
-0.019901495426893234,
... |
https://github.com/scikit-learn/scikit-learn/issues/28041 | [
"Bug"
] | Random object not being passed from to Kmeans
### Describe the bug
scikit-learn version 1.3.2, file _discretization.py line 308, the random object is not being passed to KMeans. As a result, runs are not reproducible even if you pass a random seed to KBinsDiscretizer.
### Steps/Code to Reproduce
kbd = KBinsDiscreti... | 28,041 | [
0.017841385677456856,
-0.0532526895403862,
-0.0031001712195575237,
0.006392080336809158,
0.019455626606941223,
-0.022422241047024727,
0.020550968125462532,
0.02338033728301525,
-0.014445914886891842,
-0.009481105022132397,
0.07973587512969971,
0.05705124884843826,
0.011265911161899567,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28041 | [
"Bug"
] | Random object not being passed from to Kmeans
### Describe the bug
scikit-learn version 1.3.2, file _discretization.py line 308, the random object is not being passed to KMeans. As a result, runs are not reproducible even if you pass a random seed to KBinsDiscretizer.
### Steps/Code to Reproduce
kbd = KBinsDiscreti... | 28,041 | [
0.02994389459490776,
-0.07069926708936691,
-0.0036804666742682457,
0.010752624832093716,
0.030357884243130684,
-0.026641380041837692,
0.012494934722781181,
0.03344851732254028,
0.008867807686328888,
-0.005897358991205692,
0.06653173267841339,
0.0640585646033287,
-0.0033752538729459047,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28026 | [
"Bug"
] | ValueError: buffer source array is read-only in check_estimator
### Describe the bug
I am trying to make a scikit-learn estimator `FMClassifier` based on Python wrapper `pyWFM` for C++ library `libFM` (yes :sweat_smile:).
```pytb
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/... | 28,026 | [
0.0023645926266908646,
-0.01877429336309433,
0.001799674704670906,
-0.003012882312759757,
0.07349596172571182,
-0.005140981636941433,
0.03927329182624817,
0.030981913208961487,
0.05049370601773262,
0.005579063203185797,
-0.022955765947699547,
0.02570049650967121,
-0.003535633906722069,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28026 | [
"Bug"
] | ValueError: buffer source array is read-only in check_estimator
### Describe the bug
I am trying to make a scikit-learn estimator `FMClassifier` based on Python wrapper `pyWFM` for C++ library `libFM` (yes :sweat_smile:).
```pytb
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/... | 28,026 | [
0.0023645926266908646,
-0.01877429336309433,
0.001799674704670906,
-0.003012882312759757,
0.07349596172571182,
-0.005140981636941433,
0.03927329182624817,
0.030981913208961487,
0.05049370601773262,
0.005579063203185797,
-0.022955765947699547,
0.02570049650967121,
-0.003535633906722069,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28026 | [
"Bug"
] | ValueError: buffer source array is read-only in check_estimator
### Describe the bug
I am trying to make a scikit-learn estimator `FMClassifier` based on Python wrapper `pyWFM` for C++ library `libFM` (yes :sweat_smile:).
```pytb
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/... | 28,026 | [
0.0023645926266908646,
-0.01877429336309433,
0.001799674704670906,
-0.003012882312759757,
0.07349596172571182,
-0.005140981636941433,
0.03927329182624817,
0.030981913208961487,
0.05049370601773262,
0.005579063203185797,
-0.022955765947699547,
0.02570049650967121,
-0.003535633906722069,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28026 | [
"Bug"
] | ValueError: buffer source array is read-only in check_estimator
### Describe the bug
I am trying to make a scikit-learn estimator `FMClassifier` based on Python wrapper `pyWFM` for C++ library `libFM` (yes :sweat_smile:).
```pytb
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/... | 28,026 | [
0.0023645926266908646,
-0.01877429336309433,
0.001799674704670906,
-0.003012882312759757,
0.07349596172571182,
-0.005140981636941433,
0.03927329182624817,
0.030981913208961487,
0.05049370601773262,
0.005579063203185797,
-0.022955765947699547,
0.02570049650967121,
-0.003535633906722069,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28011 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=62037&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Jan 04, 2024)
- Test Collection Failure
COMMENT:
/take | 28,011 | [
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0.052... |
https://github.com/scikit-learn/scikit-learn/issues/28009 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/7305992859)** (Dec 23, 2023)
COMMENT:
Fixed upstream in conda-forge | 28,009 | [
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https://github.com/scikit-learn/scikit-learn/issues/28008 | [
"Needs Triage"
] | ⚠️ CI failed on Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204 ⚠️
**CI failed on [Ubuntu_Jammy_Jellyfish.pymin_conda_forge_openblas_ubuntu_2204](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61831&view=logs&j=f71949a9-f9d9-549e-cf45-2e99c7b412d1)** (Dec 23, 2023)
Unable to find ... | 28,008 | [
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https://github.com/scikit-learn/scikit-learn/issues/28007 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI failed on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61831&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Dec 23, 2023)
Unable to find junit file. Please see link for details.
COMMENT:... | 28,007 | [
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https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
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0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
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0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
0.006109533831477165,
0.05953112617135048,
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0.06655675917863846,
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0.021948231384158134,
0.040670789778232574,
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-0.014097296632826328,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
0.006109533831477165,
0.05953112617135048,
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0.06655675917863846,
0.025479068979620934,
0.021948231384158134,
0.040670789778232574,
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0.030436938628554344,
0.0247992854565382,
-0.014097296632826328,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
0.006109533831477165,
0.05953112617135048,
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0.06655675917863846,
0.025479068979620934,
0.021948231384158134,
0.040670789778232574,
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0.030436938628554344,
0.0247992854565382,
-0.014097296632826328,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
0.006109533831477165,
0.05953112617135048,
-0.007357837166637182,
-0.04593588411808014,
0.06655675917863846,
0.025479068979620934,
0.021948231384158134,
0.040670789778232574,
-0.05383477360010147,
-0.006952808704227209,
0.030436938628554344,
0.0247992854565382,
-0.014097296632826328,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28004 | [
"Bug",
"Needs Triage"
] | CI is broken due to pydantic update to v2.5.3
### Describe the bug
Hi,
We are unable to run the CI.
Before:
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=61823&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=7d852497-2547-55fa-986f-0b436c028d7e
![image](https://github.com/sciki... | 28,004 | [
0.006109533831477165,
0.05953112617135048,
-0.007357837166637182,
-0.04593588411808014,
0.06655675917863846,
0.025479068979620934,
0.021948231384158134,
0.040670789778232574,
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-0.006952808704227209,
0.030436938628554344,
0.0247992854565382,
-0.014097296632826328,
0.059... |
https://github.com/scikit-learn/scikit-learn/issues/28003 | [
"Bug",
"Needs Triage"
] | NearestCentroid FutureWarning with cosine metric
### Describe the bug
NearestCentroid class throw a FutureWarning when using `metric='cosine'`.
Actually, the metric is use for two things:
- inside `.fit()` for the computation of the centroids:
- with euclidean: centroids are computed using the mean of featu... | 28,003 | [
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0.015868771821260452,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
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0.07298893481492996,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
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0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
-0.020000994205474854,
0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
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0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
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0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
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0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
-0.020000994205474854,
0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
-0.020000994205474854,
0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
-0.020000994205474854,
0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28001 | [
"Needs Info"
] | fail of installation of Scikit-learn in visual studio code.
### Describe the bug
I currently encountered a problem in that I could not install scikit-learn through pip in the terminal. I am currently using Python3 version 3.11.7 and when i tried to use pip to install the scikit-learn, it showed in the terminal that... | 28,001 | [
0.01476077176630497,
-0.03890633210539818,
-0.019100256264209747,
-0.04725969210267067,
0.03217235952615738,
0.035798270255327225,
-0.020000994205474854,
0.028005456551909447,
0.022610584273934364,
-0.010426356457173824,
0.017522061243653297,
0.07298893481492996,
-0.012014096602797508,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27996 | [
"Needs Triage"
] | Gradient of MLPs
Here is my gradient implementation for MLPs, and a test case. Would you be interested in a pull request to make this a `BaseMultilayerPerceptron` method?
`gradient.py`:
```python
# Authors: Issam H. Laradji <issam.laradji@gmail.com>
# Andreas Mueller
# Jiyuan Qian
# ... | 27,996 | [
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0.011402657255530357,
0.0027358117513358593,
... |
https://github.com/scikit-learn/scikit-learn/issues/27994 | [
"Documentation"
] | Consolidation of the naming of `y_pred_proba`, `y_score` vs `probas_pred`
### Describe the issue linked to the documentation
I am trying to leverage the classification metrics that rely on a posterior probability (i.e. P(Y | X=x)). This is commonly named `y_pred_proba` in the sklearn API.
However, I noticed a dis... | 27,994 | [
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0.011741000227630138,
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0.03441867232322693,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/27994 | [
"Documentation"
] | Consolidation of the naming of `y_pred_proba`, `y_score` vs `probas_pred`
### Describe the issue linked to the documentation
I am trying to leverage the classification metrics that rely on a posterior probability (i.e. P(Y | X=x)). This is commonly named `y_pred_proba` in the sklearn API.
However, I noticed a dis... | 27,994 | [
0.031565744429826736,
-0.014666862785816193,
0.011741000227630138,
-0.045889269560575485,
0.020946230739355087,
-0.011972097679972649,
0.067599356174469,
0.0019009707029908895,
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0.0186539925634861,
0.026648743078112602,
-0.020521145313978195,
0.03441867232322693,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/27994 | [
"Documentation"
] | Consolidation of the naming of `y_pred_proba`, `y_score` vs `probas_pred`
### Describe the issue linked to the documentation
I am trying to leverage the classification metrics that rely on a posterior probability (i.e. P(Y | X=x)). This is commonly named `y_pred_proba` in the sklearn API.
However, I noticed a dis... | 27,994 | [
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https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
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https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
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https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27993 | [
"New Feature"
] | [RFC] Allow handling of NaNs in multi-task Random Forests
### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle NaNs as a special case or would this break the API given that i... | 27,993 | [
0.02157146856188774,
0.03617933392524719,
0.03446625545620918,
0.006075566168874502,
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0.01... |
https://github.com/scikit-learn/scikit-learn/issues/27991 | [
"Bug",
"Needs Triage"
] | sklearn.mixture.gmm is not reproducible in version 1.3.2 vs 1.2.1
### Describe the bug
Code using sklearn.mixture.gmm with random seed, is not returning the same result when using scikit-learn versions 1.3.2 versus 1.2.1. The reason is that the function gmm.fit() is using, in some cases, the k-means++ algorithm. Th... | 27,991 | [
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https://github.com/scikit-learn/scikit-learn/issues/27991 | [
"Bug",
"Needs Triage"
] | sklearn.mixture.gmm is not reproducible in version 1.3.2 vs 1.2.1
### Describe the bug
Code using sklearn.mixture.gmm with random seed, is not returning the same result when using scikit-learn versions 1.3.2 versus 1.2.1. The reason is that the function gmm.fit() is using, in some cases, the k-means++ algorithm. Th... | 27,991 | [
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https://github.com/scikit-learn/scikit-learn/issues/27988 | [
"New Feature",
"Needs Triage"
] | Add __get_item__() to ColumnTransformer
### Describe the workflow you want to enable
This is really an extension to https://github.com/scikit-learn/scikit-learn/issues/24906 to retrieve state of column transformer components by name like other composite components.
### Describe your proposed solution
For exam... | 27,988 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27988 | [
"New Feature",
"Needs Triage"
] | Add __get_item__() to ColumnTransformer
### Describe the workflow you want to enable
This is really an extension to https://github.com/scikit-learn/scikit-learn/issues/24906 to retrieve state of column transformer components by name like other composite components.
### Describe your proposed solution
For exam... | 27,988 | [
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https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.0007507792906835675,
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0.02007342129945755,
0.059804774820804596,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.01734638400375843,
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0.0007507792906835675,
0.038419172167778015,
0.02007342129945755,
0.059804774820804596,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.038419172167778015,
0.02007342129945755,
0.059804774820804596,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.02007342129945755,
0.059804774820804596,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27987 | [
"Bug",
"Needs Triage"
] | `MinMaxScalar.fit_transform()` Returns Zero When All Elements Are Same
### Describe the bug
When using MinMaxScaler.fit_transform() from scikit-learn, if all elements in a column of data are the same, the scaler transforms these elements to zeros. This behavior might not be intuitive or desired in some cases, as us... | 27,987 | [
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0.04385030269622803,
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0.0007507792906835675,
0.038419172167778015,
0.02007342129945755,
0.059804774820804596,
0.0278... |
https://github.com/scikit-learn/scikit-learn/issues/27984 | [
"Needs Triage"
] | Question: Expanding the ERA Split Logic
Hi Jeffery,
I want to experiment by expanding the era splitting criterion and I wonder where the right place to implement that is.
The current implementation defines the era wise gain as the mean over all eras
![image](https://github.com/scikit-learn/scikit-learn/assets/... | 27,984 | [
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-0.01005272101610899,
0.0677... |
https://github.com/scikit-learn/scikit-learn/issues/27984 | [
"Needs Triage"
] | Question: Expanding the ERA Split Logic
Hi Jeffery,
I want to experiment by expanding the era splitting criterion and I wonder where the right place to implement that is.
The current implementation defines the era wise gain as the mean over all eras
![image](https://github.com/scikit-learn/scikit-learn/assets/... | 27,984 | [
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0.04547809064388275,
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0.030345860868692398,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/27982 | [
"Documentation",
"good first issue",
"help wanted"
] | Ensure that we have an example in the docstring of each public function or class
We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:... | 27,982 | [
0.03906597942113876,
0.005680167116224766,
-0.007519981823861599,
-0.016835596412420273,
0.056444596499204636,
0.04525092616677284,
0.07894443720579147,
0.017218483611941338,
-0.00040182869997806847,
-0.01535888947546482,
0.031169522553682327,
0.04998571053147316,
-0.01069309189915657,
0.0... |
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