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/28959 | [
"Numerical Stability"
] | Local testing of global_random_seed is not enough
When adding ``global_random_seed`` to a test, it's not enough to check it locally, i.e. on a single machine. Numerical precision issues can come from various factors like OS, CPU, BLAS, ...
When adding ``global_random_seed``, it's important to test **all** random se... | 28,959 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/28959 | [
"Numerical Stability"
] | Local testing of global_random_seed is not enough
When adding ``global_random_seed`` to a test, it's not enough to check it locally, i.e. on a single machine. Numerical precision issues can come from various factors like OS, CPU, BLAS, ...
When adding ``global_random_seed``, it's important to test **all** random se... | 28,959 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 | [
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0.050256... |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 | [
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0.0951... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
-0.029398687183856964,
0.09280817210674286,
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-0.033737894147634506,
... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
-0.024561192840337753,
0.11359294503927231,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
0.0034412441309541464,
0.10596583038568497,
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0.075... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
-0.01483310479670763,
0.1124272421002388,
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0.07419119775295258,
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0.0881... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
-0.024785974994301796,
0.10105683654546738,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 | [
-0.022690529003739357,
0.10883992910385132,
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0.07897677272558212,
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-0.036412451416254044,
... |
https://github.com/scikit-learn/scikit-learn/issues/28947 | [
"Performance"
] | Unable to allocate 24.0 GiB for an array ... But I have 64 GiB of memory
### Describe the bug
I have enough memory in my system, but I can fit my model
### Steps/Code to Reproduce
```
# X has 373 columns and 1.1 million rows
# Y has just 1 column and 1.1 million rows
def train(X,Y):
from sklearn.model... | 28,947 | [
0.03936859965324402,
0.06195308640599251,
0.028223883360624313,
0.002534020459279418,
0.08942011743783951,
0.04626832902431488,
0.02507907524704933,
0.04973301663994789,
0.028917841613292694,
0.017140323296189308,
0.0015312379691749811,
0.013170291669666767,
-0.0471031591296196,
0.03013209... |
https://github.com/scikit-learn/scikit-learn/issues/28947 | [
"Performance"
] | Unable to allocate 24.0 GiB for an array ... But I have 64 GiB of memory
### Describe the bug
I have enough memory in my system, but I can fit my model
### Steps/Code to Reproduce
```
# X has 373 columns and 1.1 million rows
# Y has just 1 column and 1.1 million rows
def train(X,Y):
from sklearn.model... | 28,947 | [
0.03936859965324402,
0.06195308640599251,
0.028223883360624313,
0.002534020459279418,
0.08942011743783951,
0.04626832902431488,
0.02507907524704933,
0.04973301663994789,
0.028917841613292694,
0.017140323296189308,
0.0015312379691749811,
0.013170291669666767,
-0.0471031591296196,
0.03013209... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
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0.06722664088010788,
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0.006158045493066311,
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0.003817621385678649,
-0.010891526006162167,
0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
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0.06722664088010788,
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0.006158045493066311,
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0.003817621385678649,
-0.010891526006162167,
0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
-0.05260901525616646,
0.06722664088010788,
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-0.00850985012948513,
0.006158045493066311,
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0.003817621385678649,
-0.010891526006162167,
0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
-0.05260901525616646,
0.06722664088010788,
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0.006158045493066311,
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0.003817621385678649,
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0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
-0.05260901525616646,
0.06722664088010788,
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0.006158045493066311,
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0.003817621385678649,
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0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
-0.05260901525616646,
0.06722664088010788,
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0.003817621385678649,
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0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 | [
0.007422272115945816,
-0.02398788370192051,
0.043794676661491394,
-0.05260901525616646,
0.06722664088010788,
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0.003817621385678649,
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0.030904710292816162,
0.040404655039310455,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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0.041516657918691635,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 | [
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https://github.com/scikit-learn/scikit-learn/issues/28943 | [
"Bug",
"Needs Triage"
] | MAPE approaching infinity with RandomForestRegressor
### Describe the bug
When using the current version of scikit-learn for learning a Random Forest Regressor (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn-ensemble-randomforestregressor) on the same dataset o... | 28,943 | [
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0.03598905727267265,
0.029066892340779305,
-0.017980093136429787,
-0.0065189809538424015,
... |
https://github.com/scikit-learn/scikit-learn/issues/28943 | [
"Bug",
"Needs Triage"
] | MAPE approaching infinity with RandomForestRegressor
### Describe the bug
When using the current version of scikit-learn for learning a Random Forest Regressor (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn-ensemble-randomforestregressor) on the same dataset o... | 28,943 | [
0.016516072675585747,
-0.00018239064957015216,
0.04268178716301918,
-0.031961649656295776,
0.05084191635251045,
-0.02158728986978531,
-0.049705345183610916,
0.030602240934967995,
0.03258584439754486,
0.03598905727267265,
0.029066892340779305,
-0.017980093136429787,
-0.0065189809538424015,
... |
https://github.com/scikit-learn/scikit-learn/issues/28939 | [
"Documentation",
"Needs Triage"
] | Rolling your own estimator
### Describe the issue linked to the documentation
The details on the Scikit-learn documentation page are at odds with the linked template.
According to the documentation, it suggests:
```class TemplateClassifier(BaseEstimator, ClassifierMixin)```
https://scikit-learn.org/stable... | 28,939 | [
0.016241012141108513,
-0.00588943948969245,
0.036102909594774246,
-0.02309831604361534,
0.022421324625611305,
0.004631219897419214,
0.06952440738677979,
-0.03059912845492363,
-0.019997157156467438,
-0.0027234198059886694,
0.0434069000184536,
0.05381966754794121,
0.010250930674374104,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 | [
-0.035358622670173645,
0.0029114156495779753,
0.01590631529688835,
-0.021921686828136444,
0.04097674414515495,
-0.011022135615348816,
-0.0405309721827507,
0.00006674265750916675,
-0.0035771559923887253,
-0.0248514786362648,
-0.019940733909606934,
0.03436972573399544,
-0.017293419688940048,
... |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 | [
-0.035358622670173645,
0.0029114156495779753,
0.01590631529688835,
-0.021921686828136444,
0.04097674414515495,
-0.011022135615348816,
-0.0405309721827507,
0.00006674265750916675,
-0.0035771559923887253,
-0.0248514786362648,
-0.019940733909606934,
0.03436972573399544,
-0.017293419688940048,
... |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 | [
-0.035358622670173645,
0.0029114156495779753,
0.01590631529688835,
-0.021921686828136444,
0.04097674414515495,
-0.011022135615348816,
-0.0405309721827507,
0.00006674265750916675,
-0.0035771559923887253,
-0.0248514786362648,
-0.019940733909606934,
0.03436972573399544,
-0.017293419688940048,
... |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 | [
-0.035358622670173645,
0.0029114156495779753,
0.01590631529688835,
-0.021921686828136444,
0.04097674414515495,
-0.011022135615348816,
-0.0405309721827507,
0.00006674265750916675,
-0.0035771559923887253,
-0.0248514786362648,
-0.019940733909606934,
0.03436972573399544,
-0.017293419688940048,
... |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 | [
-0.00573301687836647,
-0.003988391254097223,
0.01887170411646366,
0.0016596890054643154,
0.06151410564780235,
0.003426399314776063,
-0.027065081521868706,
0.027047554031014442,
0.01543375849723816,
-0.019837936386466026,
0.014912980608642101,
-0.029782621189951897,
0.024351296946406364,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 | [
-0.00573301687836647,
-0.003988391254097223,
0.01887170411646366,
0.0016596890054643154,
0.06151410564780235,
0.003426399314776063,
-0.027065081521868706,
0.027047554031014442,
0.01543375849723816,
-0.019837936386466026,
0.014912980608642101,
-0.029782621189951897,
0.024351296946406364,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 | [
-0.00573301687836647,
-0.003988391254097223,
0.01887170411646366,
0.0016596890054643154,
0.06151410564780235,
0.003426399314776063,
-0.027065081521868706,
0.027047554031014442,
0.01543375849723816,
-0.019837936386466026,
0.014912980608642101,
-0.029782621189951897,
0.024351296946406364,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 | [
-0.00573301687836647,
-0.003988391254097223,
0.01887170411646366,
0.0016596890054643154,
0.06151410564780235,
0.003426399314776063,
-0.027065081521868706,
0.027047554031014442,
0.01543375849723816,
-0.019837936386466026,
0.014912980608642101,
-0.029782621189951897,
0.024351296946406364,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/28933 | [
"Documentation"
] | DOC D2_log_loss_score is in wrong section
``D2_log_loss_score`` was added in https://github.com/scikit-learn/scikit-learn/pull/28351, but the function is documented in regression metrics with other D2 scores, while this one is a classification metric.
Ping @OmarManzoor for a follow-up PR maybe ?
COMMENT:
Sure than... | 28,933 | [
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0.013459296897053719,
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0.041593510657548904,
0.02854323945939541,
0.03619874268770218,
0.03568516671657562,
0.01552150584757328,
-0.022547150030732155,
0.058231379836797714,
0.004172861576080322,
0.04190734773874283,
0.00638... |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 | [
0.012756085954606533,
0.0341653898358345,
0.026711363345384598,
-0.04304880648851395,
0.07249826192855835,
0.05777960270643234,
0.04777393490076065,
-0.0036258234176784754,
0.02908235602080822,
-0.013933367095887661,
0.006078778766095638,
0.0648583322763443,
0.020473308861255646,
0.0105869... |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 | [
0.009274208918213844,
0.06602619588375092,
0.01729654148221016,
-0.053145330399274826,
0.023889930918812752,
0.04376620054244995,
0.04701077565550804,
0.06161665543913841,
0.06772799789905548,
-0.04381079971790314,
0.055430930107831955,
0.06016797199845314,
-0.013616379350423813,
0.0187590... |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 | [
0.004121054895222187,
0.052761778235435486,
0.014918864704668522,
-0.053026892244815826,
0.022560879588127136,
0.04220478609204292,
0.06231638044118881,
0.08324617147445679,
0.07595791667699814,
-0.026003245264291763,
0.08777168393135071,
0.06090947613120079,
-0.003193548182025552,
0.01880... |
https://github.com/scikit-learn/scikit-learn/issues/28930 | [
"Documentation",
"Moderate",
"help wanted",
"Pandas compatibility"
] | Update FAQ about pandas
Our FAQ is not up to date when it comes to pandas,
> [Why does scikit-learn not directly work with, for example, ](https://scikit-learn.org/1.4/faq.html#id13)[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)?
>
>The homogene... | 28,930 | [
0.005686634685844183,
0.09402203559875488,
0.04335956647992134,
-0.02670869044959545,
0.03926536440849304,
0.04629024490714073,
0.10777194052934647,
-0.0015501823509112,
0.03171238675713539,
-0.044344525784254074,
0.0075709400698542595,
-0.021229669451713562,
0.04826761409640312,
0.0764552... |
https://github.com/scikit-learn/scikit-learn/issues/28930 | [
"Documentation",
"Moderate",
"help wanted",
"Pandas compatibility"
] | Update FAQ about pandas
Our FAQ is not up to date when it comes to pandas,
> [Why does scikit-learn not directly work with, for example, ](https://scikit-learn.org/1.4/faq.html#id13)[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)?
>
>The homogene... | 28,930 | [
0.005686634685844183,
0.09402203559875488,
0.04335956647992134,
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0.03926536440849304,
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0.10777194052934647,
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0.03171238675713539,
-0.044344525784254074,
0.0075709400698542595,
-0.021229669451713562,
0.04826761409640312,
0.0764552... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 | [
-0.009984981268644333,
0.0111941983923316,
0.04117709770798683,
-0.0036058907862752676,
0.08854895830154419,
-0.0037129689007997513,
0.043738462030887604,
0.05157934129238129,
0.022346025332808495,
0.00023089857131708413,
0.0010033486178144813,
0.020616721361875534,
0.03483676537871361,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 | [
-0.036329176276922226,
-0.046471524983644485,
0.04021625220775604,
0.01347875315696001,
0.03806258738040924,
-0.02287254109978676,
-0.0046762884594500065,
0.0034480190370231867,
-0.0015815825900062919,
0.011146962642669678,
0.05340345948934555,
0.01883750595152378,
0.02998241037130356,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 | [
-0.036329176276922226,
-0.046471524983644485,
0.04021625220775604,
0.01347875315696001,
0.03806258738040924,
-0.02287254109978676,
-0.0046762884594500065,
0.0034480190370231867,
-0.0015815825900062919,
0.011146962642669678,
0.05340345948934555,
0.01883750595152378,
0.02998241037130356,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 | [
-0.036329176276922226,
-0.046471524983644485,
0.04021625220775604,
0.01347875315696001,
0.03806258738040924,
-0.02287254109978676,
-0.0046762884594500065,
0.0034480190370231867,
-0.0015815825900062919,
0.011146962642669678,
0.05340345948934555,
0.01883750595152378,
0.02998241037130356,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 | [
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https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
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0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
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-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
-0.039739008992910385,
-0.03393890708684921,
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-0.003990682773292065,
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-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
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-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
-0.017539476975798607,
-0.06097317859530449,
-0.012792622670531273,
-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
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-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
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-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
-0.039739008992910385,
-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
-0.017539476975798607,
-0.06097317859530449,
-0.012792622670531273,
-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
-0.039739008992910385,
-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
-0.017539476975798607,
-0.06097317859530449,
-0.012792622670531273,
-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
-0.039739008992910385,
-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
-0.017539476975798607,
-0.06097317859530449,
-0.012792622670531273,
-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 | [
-0.005858979653567076,
-0.039739008992910385,
-0.03393890708684921,
-0.005416938569396734,
-0.003990682773292065,
-0.017539476975798607,
-0.06097317859530449,
-0.012792622670531273,
-0.08764024823904037,
-0.01137523166835308,
0.034984294325113297,
0.06038602441549301,
0.0156831257045269,
-... |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 | [
0.019637545570731163,
0.012543872930109501,
0.015652120113372803,
0.024871964007616043,
0.03667822480201721,
-0.05824025720357895,
-0.019889134913682938,
0.009287328459322453,
0.002445138292387128,
-0.020362120121717453,
0.0039506517350673676,
0.011825389228761196,
0.03810277581214905,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 | [
0.019637545570731163,
0.012543872930109501,
0.015652120113372803,
0.024871964007616043,
0.03667822480201721,
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0.009287328459322453,
0.002445138292387128,
-0.020362120121717453,
0.0039506517350673676,
0.011825389228761196,
0.03810277581214905,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 | [
0.019637545570731163,
0.012543872930109501,
0.015652120113372803,
0.024871964007616043,
0.03667822480201721,
-0.05824025720357895,
-0.019889134913682938,
0.009287328459322453,
0.002445138292387128,
-0.020362120121717453,
0.0039506517350673676,
0.011825389228761196,
0.03810277581214905,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 | [
0.019637545570731163,
0.012543872930109501,
0.015652120113372803,
0.024871964007616043,
0.03667822480201721,
-0.05824025720357895,
-0.019889134913682938,
0.009287328459322453,
0.002445138292387128,
-0.020362120121717453,
0.0039506517350673676,
0.011825389228761196,
0.03810277581214905,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28913 | [
"New Feature",
"Needs Triage"
] | mypy errors when depending on sklearn
### Describe the workflow you want to enable
less errors when analyzing python code relying on sklearn using mypy
### Describe your proposed solution
Better code?
Typing annotations in the right places?
### Describe alternatives you've considered, if relevant
N/A
### Addi... | 28,913 | [
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0.04540211707353592,
0.02128453552722931,
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0.08108524233102798,
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0.06183972209692001,
0.09483581781387329,
-0.046572886407375336,
0.0359914... |
https://github.com/scikit-learn/scikit-learn/issues/28911 | [
"Documentation"
] | DOC Add Tidelift to sponsor list
### Describe the issue linked to the documentation
Add Tidelift to sponsor list https://scikit-learn.org/stable/about.html#funding
### Suggest a potential alternative/fix
_No response_
COMMENT:
Indeed.
@adrinjalali @thomasjpfan any suggestion on the phrasing?
Shall we link to... | 28,911 | [
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0.06574733555316925,
0.030833037570118904,
0.05097200348973274,
0.089... |
https://github.com/scikit-learn/scikit-learn/issues/28911 | [
"Documentation"
] | DOC Add Tidelift to sponsor list
### Describe the issue linked to the documentation
Add Tidelift to sponsor list https://scikit-learn.org/stable/about.html#funding
### Suggest a potential alternative/fix
_No response_
COMMENT:
I don't mind adding Tidelift. And yes it seems from February the money is halved! I don'... | 28,911 | [
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0.0658687874674797,
0.011805419810116291,
0.03423972800374031,
... |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 | [
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0.03557728976011276,
0.08679860830307007,
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0.0374753... |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 | [
0.05397345498204231,
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0.03557728976011276,
0.08679860830307007,
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0.0374753... |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 | [
0.05397345498204231,
0.06083134561777115,
0.01614219695329666,
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0.007905441336333752,
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0.04103195294737816,
0.011276263743638992,
0.06475064158439636,
-0.026049990206956863,
0.03557728976011276,
0.08679860830307007,
-0.04328429698944092,
0.0374753... |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 | [
0.05397345498204231,
0.06083134561777115,
0.01614219695329666,
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0.007905441336333752,
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0.04103195294737816,
0.011276263743638992,
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-0.026049990206956863,
0.03557728976011276,
0.08679860830307007,
-0.04328429698944092,
0.0374753... |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 | [
0.05397345498204231,
0.06083134561777115,
0.01614219695329666,
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0.007905441336333752,
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0.04103195294737816,
0.011276263743638992,
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-0.026049990206956863,
0.03557728976011276,
0.08679860830307007,
-0.04328429698944092,
0.0374753... |
https://github.com/scikit-learn/scikit-learn/issues/28903 | [
"Documentation"
] | Parameter Validation Documentation?
While implementing a custom estimator, I noticed that the BaseEstimator class brings in a `_validate_params` method. Looking through this repo's history, it looks like it came in back during 2022 as part of PR https://github.com/scikit-learn/scikit-learn/pull/22722
```python
... | 28,903 | [
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0.022956207394599915,
0.06057309731841087,
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0.03573044016957283,
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0.08370452374219894,
0.00022772654483560473,
0.03584430739283562,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28903 | [
"Documentation"
] | Parameter Validation Documentation?
While implementing a custom estimator, I noticed that the BaseEstimator class brings in a `_validate_params` method. Looking through this repo's history, it looks like it came in back during 2022 as part of PR https://github.com/scikit-learn/scikit-learn/pull/22722
```python
... | 28,903 | [
0.04276152327656746,
0.022956207394599915,
0.06057309731841087,
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0.03751432150602341,
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0.08370452374219894,
0.00022772654483560473,
0.03584430739283562,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28899 | [
"Bug"
] | Validation step fails when using shared memory with `multiprocessing.managers.BaseManager`
### Describe the bug
Original issue: https://github.com/kedro-org/kedro/issues/3674
Relates to https://github.com/scikit-learn/scikit-learn/issues/28781
We use multiprocessing managers to work with shared memory for pip... | 28,899 | [
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0.02642776258289814,
0.024862127378582954,
0.0036859107203781605,
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0.04016566276550293,
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0.025752639397978783,
0.01984526589512825,
-0.010814261622726917,
-0.020183373242616653,
... |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 | [
-0.006023592781275511,
0.008182598277926445,
0.02241908386349678,
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0.0721736028790474,
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0.022307217121124268,
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0.0040... |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 | [
-0.006023592781275511,
0.008182598277926445,
0.02241908386349678,
-0.04158565029501915,
0.0721736028790474,
-0.03240321949124336,
0.04908740893006325,
0.0252824816852808,
0.019461365416646004,
-0.016995582729578018,
0.022307217121124268,
-0.025223013013601303,
-0.006851130165159702,
0.0040... |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 | [
-0.006023592781275511,
0.008182598277926445,
0.02241908386349678,
-0.04158565029501915,
0.0721736028790474,
-0.03240321949124336,
0.04908740893006325,
0.0252824816852808,
0.019461365416646004,
-0.016995582729578018,
0.022307217121124268,
-0.025223013013601303,
-0.006851130165159702,
0.0040... |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 | [
-0.012771429494023323,
0.0843033567070961,
0.012059519998729229,
-0.027850506827235222,
0.04080925136804581,
0.0000878110877238214,
0.02156716398894787,
0.011884988285601139,
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-0.005862359423190355,
0.05700569227337837,
0.02000446431338787,
-0.009142542257905006,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 | [
-0.011333945207297802,
0.11987921595573425,
0.012086347676813602,
-0.019420325756072998,
0.06470814347267151,
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0.003982819616794586,
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0.003180282423272729,
0.06504058092832565,
0.005658674985170364,
-0.019045211374759674,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 | [
-0.010806293226778507,
0.11224716156721115,
0.019762730225920677,
-0.01595412567257881,
0.06116361543536186,
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0.002711578970775008,
0.04624243080615997,
-0.001459202147088945,
-0.017112229019403458,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 | [
-0.0038474895991384983,
0.12717458605766296,
0.01197231188416481,
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0.04355476051568985,
0.0008298749453388155,
-0.0253596194088459,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 | [
0.012440124526619911,
0.10420680046081543,
-0.017481543123722076,
-0.044522304087877274,
0.03221937268972397,
0.03064485639333725,
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0.013809995725750923,
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-0.01432916708290577,
0.06865087896585464,
-0.02928103692829609,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 | [
0.0006576853338629007,
0.10379841178655624,
-0.003625238547101617,
-0.033902060240507126,
0.05335967615246773,
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0.0030581350438296795,
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-0.007221488747745752,
0.03911120444536209,
0.041335102170705795,
-0.03218061104416847,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 | [
0.018004002049565315,
0.12140514701604843,
-0.01756373792886734,
-0.026361294090747833,
0.043207839131355286,
0.023032210767269135,
-0.05962352454662323,
0.009060963056981564,
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-0.02739984169602394,
0.0025321899447590113,
0.03439341485500336,
-0.04174591228365898,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 | [
0.018140530213713646,
0.11543232947587967,
0.0029560874681919813,
-0.056182634085416794,
0.047089144587516785,
-0.022776156663894653,
-0.049402184784412384,
-0.012232603505253792,
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0.028817282989621162,
0.0671374648809433,
0.041392870247364044,
-0.029072798788547516,
0... |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 | [
0.028670569881796837,
0.09470284730195999,
0.013031954877078533,
-0.06296023726463318,
0.04761934280395508,
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-0.035874661058187485,
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0.026349497959017754,
0.0700189396739006,
0.04223255813121796,
-0.02821609564125538,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 | [
0.01856943964958191,
0.10746252536773682,
0.00121232436504215,
-0.05832606554031372,
0.0485665500164032,
-0.027641071006655693,
-0.039814870804548264,
-0.0085137989372015,
-0.0456053726375103,
0.025584271177649498,
0.0629415363073349,
0.03813690319657326,
-0.03388816863298416,
0.0391737669... |
https://github.com/scikit-learn/scikit-learn/issues/28884 | [
"Bug",
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 26, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/8842793782)** (Apr 26, 2024)
COMMENT:
`conda` command not found in the osx jobs | 28,884 | [
-0.021634437143802643,
0.02283739671111107,
-0.03397132083773613,
-0.018722129985690117,
0.009189442731440067,
0.012625559233129025,
0.020092599093914032,
0.03464813530445099,
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0.007571384776383638,
0.0515848845243454,
0.04266540706157684,
-0.004153361078351736,
0.0803... |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 | [
-0.04783713445067406,
0.01596410572528839,
-0.020528340712189674,
0.054356649518013,
-0.02870570868253708,
0.013185457326471806,
0.027967244386672974,
0.005820814054459333,
-0.04433896392583847,
-0.0018267398700118065,
0.004191522020846605,
0.022247696295380592,
-0.02221485786139965,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 | [
-0.04783713445067406,
0.01596410572528839,
-0.020528340712189674,
0.054356649518013,
-0.02870570868253708,
0.013185457326471806,
0.027967244386672974,
0.005820814054459333,
-0.04433896392583847,
-0.0018267398700118065,
0.004191522020846605,
0.022247696295380592,
-0.02221485786139965,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 | [
-0.04783713445067406,
0.01596410572528839,
-0.020528340712189674,
0.054356649518013,
-0.02870570868253708,
0.013185457326471806,
0.027967244386672974,
0.005820814054459333,
-0.04433896392583847,
-0.0018267398700118065,
0.004191522020846605,
0.022247696295380592,
-0.02221485786139965,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 | [
-0.04783713445067406,
0.01596410572528839,
-0.020528340712189674,
0.054356649518013,
-0.02870570868253708,
0.013185457326471806,
0.027967244386672974,
0.005820814054459333,
-0.04433896392583847,
-0.0018267398700118065,
0.004191522020846605,
0.022247696295380592,
-0.02221485786139965,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 | [
-0.02960675209760666,
0.07914909720420837,
0.029881270602345467,
-0.013371973298490047,
0.060304466634988785,
-0.02649490162730217,
0.06424198299646378,
0.03467711806297302,
-0.09699881821870804,
0.01849834993481636,
0.01148536428809166,
0.05234546959400177,
0.0052088177762925625,
0.020794... |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 | [
-0.0355362594127655,
0.07044588029384613,
0.029052341356873512,
-0.015618713572621346,
0.05764033645391464,
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0.06295851618051529,
0.03372209891676903,
-0.11092469841241837,
0.010754114016890526,
0.015398437157273293,
0.06194612756371498,
0.005722553934901953,
0.019990... |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 | [
-0.03561338782310486,
0.07427407056093216,
0.028367595747113228,
-0.014546103775501251,
0.05551476404070854,
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0.06077492609620094,
0.03410200774669647,
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0.010520652867853642,
0.013323299586772919,
0.06121237203478813,
0.004633572418242693,
0.02102... |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 | [
-0.03313283994793892,
0.05097927898168564,
0.024386154487729073,
0.014313547872006893,
0.05767163261771202,
-0.025308804586529732,
0.034143704921007156,
0.008056397549808025,
-0.10448051989078522,
0.012362607754766941,
0.026439182460308075,
0.058952126652002335,
0.0170147567987442,
0.03920... |
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