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https://github.com/scikit-learn/scikit-learn/issues/24749 | [
"Bug"
] | sklearn installation error on python 3.11
### Describe the bug
Unable to pip install sklearn on macOS Monterey 12.6 python 3.11
It is failing when trying to prepare metadata
```
Collecting scikit-learn
Using cached scikit-learn-1.1.2.tar.gz (7.0 MB)
Installing build dependencies ... done
Getting require... | 24,749 | [
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0.010... |
https://github.com/scikit-learn/scikit-learn/issues/24749 | [
"Bug"
] | sklearn installation error on python 3.11
### Describe the bug
Unable to pip install sklearn on macOS Monterey 12.6 python 3.11
It is failing when trying to prepare metadata
```
Collecting scikit-learn
Using cached scikit-learn-1.1.2.tar.gz (7.0 MB)
Installing build dependencies ... done
Getting require... | 24,749 | [
0.038467202335596085,
-0.010147818364202976,
-0.0008741528727114201,
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0.007804885972291231,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/24749 | [
"Bug"
] | sklearn installation error on python 3.11
### Describe the bug
Unable to pip install sklearn on macOS Monterey 12.6 python 3.11
It is failing when trying to prepare metadata
```
Collecting scikit-learn
Using cached scikit-learn-1.1.2.tar.gz (7.0 MB)
Installing build dependencies ... done
Getting require... | 24,749 | [
0.038467202335596085,
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0.10672372579574585,
0.007804885972291231,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/24749 | [
"Bug"
] | sklearn installation error on python 3.11
### Describe the bug
Unable to pip install sklearn on macOS Monterey 12.6 python 3.11
It is failing when trying to prepare metadata
```
Collecting scikit-learn
Using cached scikit-learn-1.1.2.tar.gz (7.0 MB)
Installing build dependencies ... done
Getting require... | 24,749 | [
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0.010... |
https://github.com/scikit-learn/scikit-learn/issues/24748 | [
"New Feature"
] | Add option gamma='scale' and 'auto' to RBFSampler
### Describe the workflow you want to enable
Right now SVM supports gamma parameter to be set to 'scale' or 'auto', which will automatically determine gamma parameter based on data, but RBFSampler does not support this.
On top of that there is no check on n_comp... | 24,748 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24748 | [
"New Feature"
] | Add option gamma='scale' and 'auto' to RBFSampler
### Describe the workflow you want to enable
Right now SVM supports gamma parameter to be set to 'scale' or 'auto', which will automatically determine gamma parameter based on data, but RBFSampler does not support this.
On top of that there is no check on n_comp... | 24,748 | [
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0.110... |
https://github.com/scikit-learn/scikit-learn/issues/24748 | [
"New Feature"
] | Add option gamma='scale' and 'auto' to RBFSampler
### Describe the workflow you want to enable
Right now SVM supports gamma parameter to be set to 'scale' or 'auto', which will automatically determine gamma parameter based on data, but RBFSampler does not support this.
On top of that there is no check on n_comp... | 24,748 | [
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https://github.com/scikit-learn/scikit-learn/issues/24745 | [
"New Feature",
"RFC",
"frontend",
"Validation"
] | [RFC] Always convert lists of lists of numbers to numpy arrays during input validation.
### Describe the workflow you want to enable
Transformers and Estimators accept list of lists of numbers as valid for inputs like `X`.
Yet, when it comes to access to some basic attributes of the datasets (like the shape and ... | 24,745 | [
-0.024941284209489822,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/24745 | [
"New Feature",
"RFC",
"frontend",
"Validation"
] | [RFC] Always convert lists of lists of numbers to numpy arrays during input validation.
### Describe the workflow you want to enable
Transformers and Estimators accept list of lists of numbers as valid for inputs like `X`.
Yet, when it comes to access to some basic attributes of the datasets (like the shape and ... | 24,745 | [
-0.024941284209489822,
0.02475859597325325,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/24745 | [
"New Feature",
"RFC",
"frontend",
"Validation"
] | [RFC] Always convert lists of lists of numbers to numpy arrays during input validation.
### Describe the workflow you want to enable
Transformers and Estimators accept list of lists of numbers as valid for inputs like `X`.
Yet, when it comes to access to some basic attributes of the datasets (like the shape and ... | 24,745 | [
-0.024941284209489822,
0.02475859597325325,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/24745 | [
"New Feature",
"RFC",
"frontend",
"Validation"
] | [RFC] Always convert lists of lists of numbers to numpy arrays during input validation.
### Describe the workflow you want to enable
Transformers and Estimators accept list of lists of numbers as valid for inputs like `X`.
Yet, when it comes to access to some basic attributes of the datasets (like the shape and ... | 24,745 | [
-0.024941284209489822,
0.02475859597325325,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/24745 | [
"New Feature",
"RFC",
"frontend",
"Validation"
] | [RFC] Always convert lists of lists of numbers to numpy arrays during input validation.
### Describe the workflow you want to enable
Transformers and Estimators accept list of lists of numbers as valid for inputs like `X`.
Yet, when it comes to access to some basic attributes of the datasets (like the shape and ... | 24,745 | [
-0.024941284209489822,
0.02475859597325325,
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-0.008406749926507473,
0.011211163364350796,
-0.0032062912359833717,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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0.04173... |
https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24737 | [
"RFC"
] | Mandatory random seeds
### Describe the workflow you want to enable
So far, in methods such as `train_test_split`, it is not mandatory to set a random seed. In the default case, where the seed is `None`, the seed is determined randomly.
Thus, I would like to propose making mandatory setting the random seeds of **a... | 24,737 | [
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https://github.com/scikit-learn/scikit-learn/issues/24736 | [
"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=47895&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 24, 2022)
- test_function_docstring[sklearn.preprocessing._data.quantile_tr... | 24,736 | [
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https://github.com/scikit-learn/scikit-learn/issues/24736 | [
"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=47895&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Oct 24, 2022)
- test_function_docstring[sklearn.preprocessing._data.quantile_tr... | 24,736 | [
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https://github.com/scikit-learn/scikit-learn/issues/24735 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47895&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Oct 24, 2022)
- test_function_docstring[sklearn.preprocessing._data.quantile_transf... | 24,735 | [
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https://github.com/scikit-learn/scikit-learn/issues/24735 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47895&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Oct 24, 2022)
- test_function_docstring[sklearn.preprocessing._data.quantile_transf... | 24,735 | [
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https://github.com/scikit-learn/scikit-learn/issues/24735 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47895&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Oct 24, 2022)
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0.028885839506983757,
0.05462852120399475,
0.008121570572257042,
0.06904... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24732 | [
"Bug",
"New Feature",
"module:naive_bayes"
] | Improvement for Gaussian NB by rethinking the variance smoothing
### Describe the workflow you want to enable
## Problem background
In [sklearn.naive_bayes.GaussianNB](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html?highlight=gaussian+nb), a genarative probability model is g... | 24,732 | [
0.011567640118300915,
0.05099620297551155,
0.0202682763338089,
-0.002458063652738929,
0.014436517842113972,
-0.02896246500313282,
-0.04681757092475891,
0.00980378221720457,
-0.02074235863983631,
-0.03094162978231907,
0.05636415258049965,
-0.00446340162307024,
-0.00429568300023675,
0.064384... |
https://github.com/scikit-learn/scikit-learn/issues/24728 | [
"Bug",
"module:ensemble"
] | How observations with sample_weight of zero influence the fit of HistGradientBoostingRegressor
### Describe the bug
Hello,
I am trying to exclude some training observations by giving them a weight of zero through the "sample_weights" argument.
As I understand it, observations with a weight of 0 do not influen... | 24,728 | [
0.00843218807131052,
-0.032622866332530975,
0.03138634189963341,
-0.009518884122371674,
0.021576181054115295,
-0.020086266100406647,
0.014607692137360573,
0.005770714022219181,
-0.007047794759273529,
0.018627505749464035,
0.05653076991438866,
0.013011176139116287,
0.018093930557370186,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24728 | [
"Bug",
"module:ensemble"
] | How observations with sample_weight of zero influence the fit of HistGradientBoostingRegressor
### Describe the bug
Hello,
I am trying to exclude some training observations by giving them a weight of zero through the "sample_weights" argument.
As I understand it, observations with a weight of 0 do not influen... | 24,728 | [
0.00843218807131052,
-0.032622866332530975,
0.03138634189963341,
-0.009518884122371674,
0.021576181054115295,
-0.020086266100406647,
0.014607692137360573,
0.005770714022219181,
-0.007047794759273529,
0.018627505749464035,
0.05653076991438866,
0.013011176139116287,
0.018093930557370186,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24728 | [
"Bug",
"module:ensemble"
] | How observations with sample_weight of zero influence the fit of HistGradientBoostingRegressor
### Describe the bug
Hello,
I am trying to exclude some training observations by giving them a weight of zero through the "sample_weights" argument.
As I understand it, observations with a weight of 0 do not influen... | 24,728 | [
0.00843218807131052,
-0.032622866332530975,
0.03138634189963341,
-0.009518884122371674,
0.021576181054115295,
-0.020086266100406647,
0.014607692137360573,
0.005770714022219181,
-0.007047794759273529,
0.018627505749464035,
0.05653076991438866,
0.013011176139116287,
0.018093930557370186,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24725 | [
"Documentation"
] | `See also` section for `pairwise_distances` and `paired_distances` does not link to referenced documentation.
### Describe the issue linked to the documentation
The current documentation of [pairwise_distances](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise_distances.html) and [paired_distanc... | 24,725 | [
0.029006462544202805,
-0.014182857237756252,
-0.004108852241188288,
0.02896505407989025,
0.023440806195139885,
0.04828779771924019,
0.10161842405796051,
0.0294402576982975,
0.033100906759500504,
-0.047655630856752396,
-0.04903234541416168,
0.0573115348815918,
0.02005014754831791,
-0.054495... |
https://github.com/scikit-learn/scikit-learn/issues/24725 | [
"Documentation"
] | `See also` section for `pairwise_distances` and `paired_distances` does not link to referenced documentation.
### Describe the issue linked to the documentation
The current documentation of [pairwise_distances](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise_distances.html) and [paired_distanc... | 24,725 | [
0.02937512844800949,
-0.009480038657784462,
0.011701292358338833,
0.003782248590141535,
0.034209128469228745,
0.047035008668899536,
0.12447594106197357,
0.03544658422470093,
0.06056540831923485,
-0.03742005303502083,
-0.052175696939229965,
0.04404621571302414,
0.02007848210632801,
-0.05833... |
https://github.com/scikit-learn/scikit-learn/issues/24725 | [
"Documentation"
] | `See also` section for `pairwise_distances` and `paired_distances` does not link to referenced documentation.
### Describe the issue linked to the documentation
The current documentation of [pairwise_distances](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise_distances.html) and [paired_distanc... | 24,725 | [
0.017989423125982285,
0.007791320793330669,
0.012902747839689255,
-0.001046996796503663,
0.027981923893094063,
0.0437830351293087,
0.1020192801952362,
0.030297042801976204,
0.054310113191604614,
-0.04820137470960617,
-0.052720583975315094,
0.05833227187395096,
0.016449404880404472,
-0.0464... |
https://github.com/scikit-learn/scikit-learn/issues/24725 | [
"Documentation"
] | `See also` section for `pairwise_distances` and `paired_distances` does not link to referenced documentation.
### Describe the issue linked to the documentation
The current documentation of [pairwise_distances](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise_distances.html) and [paired_distanc... | 24,725 | [
0.022840918973088264,
0.015508020296692848,
0.02132600173354149,
-0.003813953371718526,
0.02918034791946411,
0.036008674651384354,
0.10593952238559723,
0.03305211290717125,
0.05864474177360535,
-0.04772516340017319,
-0.06086830049753189,
0.06047056242823601,
0.01029911357909441,
-0.0266457... |
https://github.com/scikit-learn/scikit-learn/issues/24725 | [
"Documentation"
] | `See also` section for `pairwise_distances` and `paired_distances` does not link to referenced documentation.
### Describe the issue linked to the documentation
The current documentation of [pairwise_distances](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise_distances.html) and [paired_distanc... | 24,725 | [
0.03203490376472473,
0.005375445354729891,
0.007543411571532488,
-0.001517060212790966,
0.02220277115702629,
0.040339916944503784,
0.10500789433717728,
0.02758668176829815,
0.04761989042162895,
-0.05141257867217064,
-0.056532252579927444,
0.0502391941845417,
0.03359485790133476,
-0.0461132... |
https://github.com/scikit-learn/scikit-learn/issues/24724 | [
"Needs Decision",
"RFC"
] | verbose_feature_names_out default value
### Describe the workflow you want to enable
So I obviously love the example in https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py
But it's a bit of a bummer that the pipeline needs so many pa... | 24,724 | [
-0.016084210947155952,
-0.013705947436392307,
-0.0019080148776993155,
-0.059380583465099335,
0.0026103113777935505,
0.009446697309613228,
0.05865360051393509,
-0.006727494299411774,
0.009952072985470295,
0.034414712339639664,
0.051367249339818954,
-0.0012183275539427996,
-0.03826901316642761... |
https://github.com/scikit-learn/scikit-learn/issues/24724 | [
"Needs Decision",
"RFC"
] | verbose_feature_names_out default value
### Describe the workflow you want to enable
So I obviously love the example in https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py
But it's a bit of a bummer that the pipeline needs so many pa... | 24,724 | [
-0.02368876151740551,
-0.03296714648604393,
-0.008350620977580547,
-0.05307244136929512,
-0.014292364940047264,
0.021693723276257515,
0.06124401465058327,
-0.010115011595189571,
0.005992901045829058,
0.03329974412918091,
0.05227438360452652,
-0.004804358351975679,
-0.030576802790164948,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24724 | [
"Needs Decision",
"RFC"
] | verbose_feature_names_out default value
### Describe the workflow you want to enable
So I obviously love the example in https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py
But it's a bit of a bummer that the pipeline needs so many pa... | 24,724 | [
-0.013579079881310463,
-0.010813898406922817,
-0.0010766505729407072,
-0.0540415421128273,
0.0002050661132670939,
0.021820107474923134,
0.04354109242558479,
0.002826179377734661,
0.018060551956295967,
0.03036646358668804,
0.0436430349946022,
0.009927316568791866,
-0.04598496854305267,
0.14... |
https://github.com/scikit-learn/scikit-learn/issues/24724 | [
"Needs Decision",
"RFC"
] | verbose_feature_names_out default value
### Describe the workflow you want to enable
So I obviously love the example in https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py
But it's a bit of a bummer that the pipeline needs so many pa... | 24,724 | [
-0.012049013748764992,
0.017084380611777306,
0.00968171190470457,
-0.044982392340898514,
0.02400495670735836,
0.018841128796339035,
0.07081188261508942,
-0.005596152041107416,
-0.003459581872448325,
0.042551662772893906,
0.04130551218986511,
-0.008215075358748436,
-0.036131601780653,
0.122... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24713 | [
"Bug",
"module:neural_network"
] | AttributeError: 'MLPRegressor' object has no attribute '_best_coefs'
The following parameters were working fine with another dataset. When I switched to a new dataset, for some reason an `AttributeError` is occurring. Any ideas?
```python
model = MLPRegressor(hidden_layer_sizes=(10), activation='tanh', batch_size=... | 24,713 | [
-0.0025158743374049664,
-0.010616534389555454,
0.0376998670399189,
-0.0034131486900150776,
0.09872997552156448,
0.010672753676772118,
0.0548105463385582,
0.03507951274514198,
0.005940317176282406,
-0.007894407026469707,
-0.03091234341263771,
0.017338203266263008,
-0.03187645226716995,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24712 | [
"New Feature",
"Needs Triage"
] | KNN for dim > 2
### Describe the workflow you want to enable
Applying Knn to data with dim > 2:
File "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_graph.py", line 120, in kneighbors_graph
).fit(X)
File "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_unsupervised.py", line 166, in fi... | 24,712 | [
-0.000893306452780962,
0.02646518312394619,
0.022645626217126846,
0.006778305396437645,
0.018919946625828743,
-0.034858353435993195,
0.05635878071188927,
0.01814255118370056,
0.08398666232824326,
0.009372672997415066,
-0.014161630533635616,
0.08056866377592087,
-0.015155102126300335,
0.028... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24708 | [
"Documentation"
] | Create a classification report using a confusion matrix as input
### Describe the workflow you want to enable
I had some results stored in the form of a confusion matrix for some experiments. However, I wanted to use `classification_report` to easily compute all the metrics.
### Describe your proposed solution
... | 24,708 | [
0.021610653027892113,
0.04857692867517471,
0.033417947590351105,
-0.03103078342974186,
0.06482995301485062,
0.03689146041870117,
0.023985031992197037,
0.016198137775063515,
-0.026849981397390366,
-0.0550888292491436,
-0.0057623302564024925,
0.014084449037909508,
0.03897327557206154,
0.0127... |
https://github.com/scikit-learn/scikit-learn/issues/24702 | [
"Documentation",
"module:feature_extraction"
] | TfidfVectorizer binary parameter
### Describe the issue linked to the documentation
The `TfidfVectorizer` parameter `binary` is described as:
> **binary bool, default=False**
> If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is ... | 24,702 | [
-0.03017870895564556,
-0.027287954464554787,
-0.0017895449418574572,
0.018077928572893143,
0.03210926055908203,
0.005292000249028206,
0.04518871754407883,
0.04270965978503227,
-0.09757325798273087,
-0.04646988585591316,
0.02537301927804947,
-0.022796226665377617,
0.0004948252462781966,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24702 | [
"Documentation",
"module:feature_extraction"
] | TfidfVectorizer binary parameter
### Describe the issue linked to the documentation
The `TfidfVectorizer` parameter `binary` is described as:
> **binary bool, default=False**
> If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is ... | 24,702 | [
-0.02223174087703228,
-0.023792551830410957,
0.008894984610378742,
0.00351538322865963,
0.03603010252118111,
0.0015077125281095505,
0.06463167816400528,
0.039903219789266586,
-0.11588811874389648,
-0.050795622169971466,
0.0239091906696558,
-0.00468489108607173,
0.0034813538659363985,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/24702 | [
"Documentation",
"module:feature_extraction"
] | TfidfVectorizer binary parameter
### Describe the issue linked to the documentation
The `TfidfVectorizer` parameter `binary` is described as:
> **binary bool, default=False**
> If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is ... | 24,702 | [
-0.022306842729449272,
-0.032664064317941666,
0.008394453674554825,
0.00797115359455347,
0.046172771602869034,
-0.0015211531426757574,
0.05601605772972107,
0.045670345425605774,
-0.11201691627502441,
-0.04961399361491203,
0.016429318115115166,
-0.002396287163719535,
-0.002766416873782873,
... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24700 | [
"workflow"
] | Introduction of `Waiting for Second Reviewer` tag
## Motivation
PRs commonly fall into a state where they have one approval from an initial reviewer, however no attention from any secondary reviewers. Arguably the most effective mechanism of dissolving PR backlog is exactly to target these sorts of PRs, since the ini... | 24,700 | [
0.02666400372982025,
0.007455708459019661,
-0.0249367356300354,
-0.06555234640836716,
-0.029638120904564857,
-0.005544150713831186,
-0.023674895986914635,
0.0008865321869961917,
-0.03146966174244881,
0.0033950365614145994,
0.029192982241511345,
-0.04412801191210747,
0.004284055903553963,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24696 | [
"Bug",
"Needs Triage"
] | ImportError: cannot import name 'SplineTransformer' from 'sklearn.preprocessing' ,which only appears on Linux system, and it can be imported on Windows.
### Describe the bug
Hello, I try to use `SplineTransformer` to make a pipeline, but I fail to import it as I code like `from sklearn.preprocessing import SplineT... | 24,696 | [
0.02006680890917778,
0.036494553089141846,
-0.014481594786047935,
-0.015333432704210281,
0.03304876387119293,
-0.012163432314991951,
0.03850436955690384,
-0.03221695497632027,
0.026699373498558998,
0.022285301238298416,
0.05349799618124962,
0.025005346164107323,
0.025009511038661003,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/24696 | [
"Bug",
"Needs Triage"
] | ImportError: cannot import name 'SplineTransformer' from 'sklearn.preprocessing' ,which only appears on Linux system, and it can be imported on Windows.
### Describe the bug
Hello, I try to use `SplineTransformer` to make a pipeline, but I fail to import it as I code like `from sklearn.preprocessing import SplineT... | 24,696 | [
0.02006680890917778,
0.036494553089141846,
-0.014481594786047935,
-0.015333432704210281,
0.03304876387119293,
-0.012163432314991951,
0.03850436955690384,
-0.03221695497632027,
0.026699373498558998,
0.022285301238298416,
0.05349799618124962,
0.025005346164107323,
0.025009511038661003,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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0.052737023681402206,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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0.052737023681402206,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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0.008241593837738037,
0.027524644508957863,
0.052737023681402206,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24695 | [
"Documentation",
"Needs Triage"
] | Does this line in `Classifier comparison' produce a test data leakage?
### Describe the issue linked to the documentation
I feel there's a line here:
```
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_... | 24,695 | [
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0.052737023681402206,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24694 | [
"Build / CI"
] | CI "no OpenMP" build environment actually has OpenMP
This avoided catching a regression where an unprotected `cimport openmp` was introduced.
As a side-comment: Pyodide build needs to be built without OpenMP.
From https://github.com/scikit-learn/scikit-learn/pull/24682#issuecomment-1281939439, there is OpenMP in... | 24,694 | [
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https://github.com/scikit-learn/scikit-learn/issues/24694 | [
"Build / CI"
] | CI "no OpenMP" build environment actually has OpenMP
This avoided catching a regression where an unprotected `cimport openmp` was introduced.
As a side-comment: Pyodide build needs to be built without OpenMP.
From https://github.com/scikit-learn/scikit-learn/pull/24682#issuecomment-1281939439, there is OpenMP in... | 24,694 | [
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https://github.com/scikit-learn/scikit-learn/issues/24694 | [
"Build / CI"
] | CI "no OpenMP" build environment actually has OpenMP
This avoided catching a regression where an unprotected `cimport openmp` was introduced.
As a side-comment: Pyodide build needs to be built without OpenMP.
From https://github.com/scikit-learn/scikit-learn/pull/24682#issuecomment-1281939439, there is OpenMP in... | 24,694 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
See... | 24,687 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
Sin... | 24,687 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
Thi... | 24,687 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
It ... | 24,687 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
I c... | 24,687 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
I c... | 24,687 | [
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0.093... |
https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
Not... | 24,687 | [
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0.091... |
https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
I w... | 24,687 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
"I ... | 24,687 | [
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0.07... |
https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
## ... | 24,687 | [
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https://github.com/scikit-learn/scikit-learn/issues/24687 | [
"Build / CI"
] | ⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️
**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=47750&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Oct 18, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
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https://github.com/scikit-learn/scikit-learn/issues/24686 | [
"New Feature",
"module:cluster"
] | Path to HDBSCAN Inclusion
## Introduction
The HDBSCAN estimator implementation from [`scikit-learn-contrib/hdbscan`](https://github.com/scikit-learn-contrib/hdbscan) has been adopted, modified and refactored to conform to scikit-learn API and is now merged into the [`hdbscan`](https://github.com/scikit-learn/scikit-l... | 24,686 | [
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0.020939737558364868,
0.... |
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