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/25757 | [
"New Feature"
] | Allow `norm=None` in `preprocessing.Normalizer`
### Describe the workflow you want to enable
I would like to be able to set `norm=None` in `preprocessing.Normalizer`, so that I can eaily tune `norm` as part of a pipeline. Currently, `norm` can only be one of `{‘l1’, ‘l2’, ‘max’}`.
### Describe your proposed solutio... | 25,757 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25757 | [
"New Feature"
] | Allow `norm=None` in `preprocessing.Normalizer`
### Describe the workflow you want to enable
I would like to be able to set `norm=None` in `preprocessing.Normalizer`, so that I can eaily tune `norm` as part of a pipeline. Currently, `norm` can only be one of `{‘l1’, ‘l2’, ‘max’}`.
### Describe your proposed solutio... | 25,757 | [
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https://github.com/scikit-learn/scikit-learn/issues/25755 | [
"Documentation",
"Needs Investigation"
] | Figure is shown incorrectly
### Describe the issue linked to the documentation
Second part of the [figure](https://scikit-learn.org/stable/modules/gaussian_process.html#dot-product-kernel) doesn't show sampled functions
### Suggest a potential alternative/fix
_No response_
COMMENT:
This is not an issue with the fi... | 25,755 | [
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https://github.com/scikit-learn/scikit-learn/issues/25755 | [
"Documentation",
"Needs Investigation"
] | Figure is shown incorrectly
### Describe the issue linked to the documentation
Second part of the [figure](https://scikit-learn.org/stable/modules/gaussian_process.html#dot-product-kernel) doesn't show sampled functions
### Suggest a potential alternative/fix
_No response_
COMMENT:
My understanding of Gaussian Pro... | 25,755 | [
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https://github.com/scikit-learn/scikit-learn/issues/25751 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4320199634)** (Mar 03, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/4329176818) on Mar 04, 2023 | 25,751 | [
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https://github.com/scikit-learn/scikit-learn/issues/25750 | [
"Bug",
"Needs Investigation",
"Numerical Stability"
] | Differences in scalar vs vectorized predictions with `GaussianProcessRegressor`
### Describe the bug
I would expect that calling `GaussianProcessRegressor.predict(X)` with a single X matrix, or with repeated scalar evaluations rows of X should would give nearly the same result, but they don't.
An example is give... | 25,750 | [
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https://github.com/scikit-learn/scikit-learn/issues/25750 | [
"Bug",
"Needs Investigation",
"Numerical Stability"
] | Differences in scalar vs vectorized predictions with `GaussianProcessRegressor`
### Describe the bug
I would expect that calling `GaussianProcessRegressor.predict(X)` with a single X matrix, or with repeated scalar evaluations rows of X should would give nearly the same result, but they don't.
An example is give... | 25,750 | [
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0.06523447483778,
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https://github.com/scikit-learn/scikit-learn/issues/25750 | [
"Bug",
"Needs Investigation",
"Numerical Stability"
] | Differences in scalar vs vectorized predictions with `GaussianProcessRegressor`
### Describe the bug
I would expect that calling `GaussianProcessRegressor.predict(X)` with a single X matrix, or with repeated scalar evaluations rows of X should would give nearly the same result, but they don't.
An example is give... | 25,750 | [
0.019161786884069443,
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0.06523447483778,
-0.0022663578... |
https://github.com/scikit-learn/scikit-learn/issues/25750 | [
"Bug",
"Needs Investigation",
"Numerical Stability"
] | Differences in scalar vs vectorized predictions with `GaussianProcessRegressor`
### Describe the bug
I would expect that calling `GaussianProcessRegressor.predict(X)` with a single X matrix, or with repeated scalar evaluations rows of X should would give nearly the same result, but they don't.
An example is give... | 25,750 | [
0.019161786884069443,
0.02389472723007202,
0.021601324900984764,
0.04066887125372887,
0.05302601307630539,
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0.01726651005446911,
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0.04656285047531128,
0.02883397601544857,
-0.0023060888051986694,
0.06523447483778,
-0.0022663578... |
https://github.com/scikit-learn/scikit-learn/issues/25740 | [
"New Feature"
] | Verbose for Gaussian Process regression
### Describe the workflow you want to enable
I would like to monitor the progress of my fit function for a Gaussian Process.
### Describe your proposed solution
if verbose >= 1 print the parameters "theta" and the log marginal likelihood. Maybe offer the option to return the... | 25,740 | [
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0.095696... |
https://github.com/scikit-learn/scikit-learn/issues/25740 | [
"New Feature"
] | Verbose for Gaussian Process regression
### Describe the workflow you want to enable
I would like to monitor the progress of my fit function for a Gaussian Process.
### Describe your proposed solution
if verbose >= 1 print the parameters "theta" and the log marginal likelihood. Maybe offer the option to return the... | 25,740 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/25740 | [
"New Feature"
] | Verbose for Gaussian Process regression
### Describe the workflow you want to enable
I would like to monitor the progress of my fit function for a Gaussian Process.
### Describe your proposed solution
if verbose >= 1 print the parameters "theta" and the log marginal likelihood. Maybe offer the option to return the... | 25,740 | [
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https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
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0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
-0.028566433116793633,
0.06682096421718597,
0.021875401958823204,
-0.04900840297341347,
0.06218745931982994,
0.005719470791518688,
0.11799197643995285,
-0.04082430526614189,
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-0.027447499334812164,
0.034463852643966675,
-0.004617523867636919,
0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
-0.028566433116793633,
0.06682096421718597,
0.021875401958823204,
-0.04900840297341347,
0.06218745931982994,
0.005719470791518688,
0.11799197643995285,
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-0.027447499334812164,
0.034463852643966675,
-0.004617523867636919,
0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
-0.028566433116793633,
0.06682096421718597,
0.021875401958823204,
-0.04900840297341347,
0.06218745931982994,
0.005719470791518688,
0.11799197643995285,
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0.034463852643966675,
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0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
-0.028566433116793633,
0.06682096421718597,
0.021875401958823204,
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0.034463852643966675,
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0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25730 | [
"Bug",
"module:pipeline"
] | FeatureUnion not working when aggregating data and pandas transform output selected
### Describe the bug
I would like to use `pandas` transform output and use a custom transformer in a feature union which aggregates data. When I'm using this combination I got an error. When I use default `numpy` output it works fine.... | 25,730 | [
-0.028566433116793633,
0.06682096421718597,
0.021875401958823204,
-0.04900840297341347,
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0.05949116498231888,
0.044... |
https://github.com/scikit-learn/scikit-learn/issues/25729 | [
"Bug",
"Needs Triage"
] | Issue with check_transformer_general
### Describe the bug
`check_transformer_general` does not work well when sklearn is configured with set_output as pandas.
To reproduce it, using sklearn version at least 1.2.0:
```python
from sklearn.utils.estimator_checks import check_transformer_general
from sklearn.p... | 25,729 | [
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0.04325968399643898,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/25727 | [
"Bug",
"Needs Triage"
] | wrong display
### Describe the bug
why is it not displaying properly?
### Steps/Code to Reproduce
from sklearn.metrics import classification_report
classification_report(y_test,y_pred, target_names=['normal','Toxicity'])
### Expected Results
No error
### Actual Results
![image](https://user-images.githubuser... | 25,727 | [
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0.03470778837800026,
0.0287389... |
https://github.com/scikit-learn/scikit-learn/issues/25727 | [
"Bug",
"Needs Triage"
] | wrong display
### Describe the bug
why is it not displaying properly?
### Steps/Code to Reproduce
from sklearn.metrics import classification_report
classification_report(y_test,y_pred, target_names=['normal','Toxicity'])
### Expected Results
No error
### Actual Results
![image](https://user-images.githubuser... | 25,727 | [
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0.0399... |
https://github.com/scikit-learn/scikit-learn/issues/25725 | [
"Bug",
"Needs Triage"
] | Doubled prefix operators "not" and "~" should not be used python:S2761
### Describe the bug
Only found one instance but should be worth improving since is in tutorial sections.
Calling the not or ~ prefix operator twice might be redundant: the second invocation undoes the first. Such mistakes are typically caus... | 25,725 | [
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https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
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0.02971666306257248,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
-0.007371710147708654,
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0.02971666306257248,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
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0.020342938601970673,
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0.028708837926387787,
0.02971666306257248,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
-0.007371710147708654,
-0.09125293046236038,
0.020342938601970673,
0.019029024988412857,
0.05431241914629936,
-0.027580877766013145,
0.013549153693020344,
0.009706229902803898,
-0.06924273073673248,
0.006762006785720587,
-0.028651036322116852,
0.028708837926387787,
0.02971666306257248,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25723 | [
"Needs Triage"
] | sklearn gives different trees for scaled and unscaled data
I am not very convinced. I used the same model with exact same hyperparameters and setting the same random state for a synthetically generated dataset for three scenarios. Firstly, made the tree with unscaled data, then using maxmin scaling and then scaling e... | 25,723 | [
-0.007371710147708654,
-0.09125293046236038,
0.020342938601970673,
0.019029024988412857,
0.05431241914629936,
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0.013549153693020344,
0.009706229902803898,
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0.006762006785720587,
-0.028651036322116852,
0.028708837926387787,
0.02971666306257248,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25716 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Is the check of strict convergence in KMeans too expensive for the benefits ?
### Describe the bug
In `KMeans` scikit-learn defines [`strict_convergence`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/cluster/_kmeans.py#L701) as the event of producing the same label assignments at two successive it... | 25,716 | [
-0.03138313069939613,
0.01311810314655304,
0.009217539802193642,
-0.033224452286958694,
-0.014257003553211689,
0.00913555920124054,
-0.020405005663633347,
0.03391009569168091,
0.015947755426168442,
0.04186839982867241,
0.07266835123300552,
-0.0014427502173930407,
-0.011315843090415001,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25716 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Is the check of strict convergence in KMeans too expensive for the benefits ?
### Describe the bug
In `KMeans` scikit-learn defines [`strict_convergence`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/cluster/_kmeans.py#L701) as the event of producing the same label assignments at two successive it... | 25,716 | [
-0.03218588978052139,
0.014906528405845165,
0.008868524804711342,
-0.03448512777686119,
-0.015071229077875614,
0.00997386034578085,
-0.01948181539773941,
0.030138077214360237,
0.013749254867434502,
0.04346802830696106,
0.07611655443906784,
-0.0014312855200842023,
-0.012442597188055515,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25716 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Is the check of strict convergence in KMeans too expensive for the benefits ?
### Describe the bug
In `KMeans` scikit-learn defines [`strict_convergence`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/cluster/_kmeans.py#L701) as the event of producing the same label assignments at two successive it... | 25,716 | [
-0.03267456218600273,
0.013050153851509094,
0.009912490844726562,
-0.03329708054661751,
-0.012893973849713802,
0.010667166672647,
-0.021322034299373627,
0.03337585926055908,
0.017009109258651733,
0.04253252223134041,
0.07221876829862595,
-0.0019070873968303204,
-0.013117625378072262,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25716 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | Is the check of strict convergence in KMeans too expensive for the benefits ?
### Describe the bug
In `KMeans` scikit-learn defines [`strict_convergence`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/cluster/_kmeans.py#L701) as the event of producing the same label assignments at two successive it... | 25,716 | [
-0.032341115176677704,
0.012066238559782505,
0.009679180569946766,
-0.035697802901268005,
-0.011876106262207031,
0.007347931619733572,
-0.023454563692212105,
0.030177492648363113,
0.011207721196115017,
0.04382721707224846,
0.06959544867277145,
-0.001810212736018002,
-0.01070147380232811,
-... |
https://github.com/scikit-learn/scikit-learn/issues/25711 | [
"Bug",
"Moderate",
"help wanted"
] | SequentialFeatureSelector is not working with ColumnTransformer
### Describe the bug
Please see the code.
### Steps/Code to Reproduce
```python
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import Column... | 25,711 | [
0.003851020708680153,
0.013677298091351986,
0.016833296045660973,
-0.044847141951322556,
0.09012340754270554,
-0.0030899434350430965,
0.0914425477385521,
0.01863568089902401,
0.00035641182330437005,
0.020717374980449677,
0.021611159667372704,
-0.05953605845570564,
0.0448659248650074,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/25711 | [
"Bug",
"Moderate",
"help wanted"
] | SequentialFeatureSelector is not working with ColumnTransformer
### Describe the bug
Please see the code.
### Steps/Code to Reproduce
```python
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import Column... | 25,711 | [
0.003851020708680153,
0.013677298091351986,
0.016833296045660973,
-0.044847141951322556,
0.09012340754270554,
-0.0030899434350430965,
0.0914425477385521,
0.01863568089902401,
0.00035641182330437005,
0.020717374980449677,
0.021611159667372704,
-0.05953605845570564,
0.0448659248650074,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/25711 | [
"Bug",
"Moderate",
"help wanted"
] | SequentialFeatureSelector is not working with ColumnTransformer
### Describe the bug
Please see the code.
### Steps/Code to Reproduce
```python
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import Column... | 25,711 | [
0.003851020708680153,
0.013677298091351986,
0.016833296045660973,
-0.044847141951322556,
0.09012340754270554,
-0.0030899434350430965,
0.0914425477385521,
0.01863568089902401,
0.00035641182330437005,
0.020717374980449677,
0.021611159667372704,
-0.05953605845570564,
0.0448659248650074,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/25711 | [
"Bug",
"Moderate",
"help wanted"
] | SequentialFeatureSelector is not working with ColumnTransformer
### Describe the bug
Please see the code.
### Steps/Code to Reproduce
```python
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import Column... | 25,711 | [
0.003851020708680153,
0.013677298091351986,
0.016833296045660973,
-0.044847141951322556,
0.09012340754270554,
-0.0030899434350430965,
0.0914425477385521,
0.01863568089902401,
0.00035641182330437005,
0.020717374980449677,
0.021611159667372704,
-0.05953605845570564,
0.0448659248650074,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/25711 | [
"Bug",
"Moderate",
"help wanted"
] | SequentialFeatureSelector is not working with ColumnTransformer
### Describe the bug
Please see the code.
### Steps/Code to Reproduce
```python
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import Column... | 25,711 | [
0.003851020708680153,
0.013677298091351986,
0.016833296045660973,
-0.044847141951322556,
0.09012340754270554,
-0.0030899434350430965,
0.0914425477385521,
0.01863568089902401,
0.00035641182330437005,
0.020717374980449677,
0.021611159667372704,
-0.05953605845570564,
0.0448659248650074,
0.046... |
https://github.com/scikit-learn/scikit-learn/issues/25709 | [
"New Feature",
"Needs Triage"
] | Please add **fit_params for SequentialFeatureSelector.fit method
### Describe the workflow you want to enable
I need to use `StratifiedGroupKFold` on my data.
Unlike `cross_validate`, `SequentialFeatureSelector.fit` method has no `**fit_params` or `groups` parameter, so I can't use group split.
### Describe y... | 25,709 | [
-0.026395389810204506,
0.011826707050204277,
0.0012959683081135154,
-0.007896140217781067,
0.026581041514873505,
-0.0176224485039711,
0.0624513253569603,
0.022964203730225563,
0.016500653699040413,
-0.005964198615401983,
0.017339618876576424,
0.0192724522203207,
0.020537368953227997,
0.087... |
https://github.com/scikit-learn/scikit-learn/issues/25707 | [
"New Feature",
"API",
"Needs Decision"
] | ENH HistGradientBoosting estimators should have `.feature_importances_` attribute
### Describe the workflow you want to enable
The practicality of using the `.feature_importances_` attribute to superficially analyze global explanations of your classifier is really useful, IMHO.
Right after evaluating the model, ru... | 25,707 | [
0.0021695212926715612,
0.003461115760728717,
0.006328374147415161,
0.0022492369171231985,
0.017542125657200813,
-0.017165524885058403,
-0.008840922266244888,
-0.014091608114540577,
-0.03029201179742813,
-0.022643854841589928,
0.008851874619722366,
0.004999309778213501,
-0.009180494584143162,... |
https://github.com/scikit-learn/scikit-learn/issues/25707 | [
"New Feature",
"API",
"Needs Decision"
] | ENH HistGradientBoosting estimators should have `.feature_importances_` attribute
### Describe the workflow you want to enable
The practicality of using the `.feature_importances_` attribute to superficially analyze global explanations of your classifier is really useful, IMHO.
Right after evaluating the model, ru... | 25,707 | [
0.0021695212926715612,
0.003461115760728717,
0.006328374147415161,
0.0022492369171231985,
0.017542125657200813,
-0.017165524885058403,
-0.008840922266244888,
-0.014091608114540577,
-0.03029201179742813,
-0.022643854841589928,
0.008851874619722366,
0.004999309778213501,
-0.009180494584143162,... |
https://github.com/scikit-learn/scikit-learn/issues/25699 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/4268187511)** (Feb 25, 2023)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/4273200865) on Feb 26, 2023 | 25,699 | [
-0.03892723470926285,
0.03492284566164017,
-0.02043621800839901,
-0.013489299453794956,
0.010339013300836086,
0.012514765374362469,
0.01707587204873562,
0.03928200900554657,
-0.055045925080776215,
0.028671277686953545,
0.07853657752275467,
0.03909742832183838,
-0.013034428469836712,
0.0758... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25696 | [
"Bug"
] | CalibratedClassifierCV fails on lgbm fit_params
Hi,
I'm trying to use CalibratedClassifierCV to calibrate the probabilities from a LGBM model. The issue is that when I try CalibratedClassifierCV with eval_set, I get an error ValueError: Found input variables with inconsistent numbers of samples: [43364, 1] which is... | 25,696 | [
-0.012995311990380287,
-0.038627516478300095,
0.04040314629673958,
0.019093820825219154,
0.07156342267990112,
-0.014135945588350296,
0.02704436704516411,
0.039046548306941986,
-0.03991685062646866,
0.00017407619452569634,
0.033127475529909134,
-0.003073779633268714,
0.005298789124935865,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25693 | [
"Bug",
"module:neural_network"
] | MLPRegressor.partial_fit produces an error when early_stopping is True
### Describe the bug
WIth `sklearn = 1.2.1`, when using `early_stopping = True`, `fit` works fine, but partial fit produces the following error:
I think this is related to this change: https://github.com/scikit-learn/scikit-learn/pull/24683.
#... | 25,693 | [
-0.002107718726620078,
-0.00020587415201589465,
0.014988315291702747,
-0.02041349746286869,
0.0997062474489212,
-0.01628999039530754,
0.05010652169585228,
0.015962442383170128,
0.02787705883383751,
0.000050478749471949413,
0.05082305520772934,
0.029891302809119225,
-0.04226827248930931,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25691 | [
"Needs Triage"
] | Decision Tree Regressor giving different result for scaled and unscaled data
The decision tree regressor outputs different tree for scaled and unscaled data. This is true for the random forest regressor also. As per my understanding scaling data should not change the results for decision tree or random forest regresso... | 25,691 | [
-0.020800482481718063,
-0.09067221730947495,
0.006754114758223295,
-0.0061365049332380295,
0.04523555189371109,
0.001152319018729031,
0.01583924889564514,
0.03353240340948105,
-0.0723479688167572,
0.017296677455306053,
-0.01504591852426529,
0.010320913977921009,
0.048999786376953125,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/25691 | [
"Needs Triage"
] | Decision Tree Regressor giving different result for scaled and unscaled data
The decision tree regressor outputs different tree for scaled and unscaled data. This is true for the random forest regressor also. As per my understanding scaling data should not change the results for decision tree or random forest regresso... | 25,691 | [
-0.006822431460022926,
-0.08696909993886948,
0.00553913414478302,
-0.006355195306241512,
0.03399597853422165,
-0.008495395071804523,
0.015136021189391613,
0.03319510817527771,
-0.08550696820020676,
0.017394747585058212,
-0.02023341692984104,
0.0178139079362154,
0.04889638349413872,
0.00339... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
-0.019555913284420967,
0.0061295549385249615,
-0.042535990476608276,
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0.021640446037054062,
0.02528744377195835,
0.04752080515027046,
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0.06412868946790695,
0.058438487350940704,
0.03923654556274414,
-0.0013332136441022158,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.05834617093205452,
0.05153250694274902,
0.0437484048306942,
0.0006749916938133538,
0.04776... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.05984836... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.05399114266037941,
0.040469907224178314,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.02657490... |
https://github.com/scikit-learn/scikit-learn/issues/25686 | [
"Build / CI",
"Numerical Stability"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpa... | 25,686 | [
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0.009009423665702343,
0.02903... |
https://github.com/scikit-learn/scikit-learn/issues/25666 | [
"Bug",
"module:multioutput"
] | Multioutput regressors raise ValueError when scoring with `multioutput="raw_values"`
### Describe the bug
The goal of the `multioutput="raw_values"` parameter in the regression metrics is to be able to inspect the individual scores of a multioutput metaestimator, but the `_score` function in `_validation.py` expect... | 25,666 | [
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0.03659249469637871,
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0.010999484919011593,
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0.042892515659332275,
-0.00020715536084026098,
... |
https://github.com/scikit-learn/scikit-learn/issues/25666 | [
"Bug",
"module:multioutput"
] | Multioutput regressors raise ValueError when scoring with `multioutput="raw_values"`
### Describe the bug
The goal of the `multioutput="raw_values"` parameter in the regression metrics is to be able to inspect the individual scores of a multioutput metaestimator, but the `_score` function in `_validation.py` expect... | 25,666 | [
-0.033115118741989136,
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0.04049166664481163,
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0.09776157140731812,
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0.03659249469637871,
0.01975829154253006,
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0.010999484919011593,
-0.0020516831427812576,
0.042892515659332275,
-0.00020715536084026098,
... |
https://github.com/scikit-learn/scikit-learn/issues/25662 | [
"Bug",
"Needs Triage"
] | LinearDiscriminantAnalysis: svd solver gives different results than eigen and lsqr.
### Describe the bug
LinearDiscriminantAnalysis provides three methods for determining the discriminants: `svd` (the default), `lsqr` and `eigen`. The coefficients produced by the `lsqr` and `eigen` methods agree with each other and d... | 25,662 | [
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0.00268591963686049,
0.02893071062862873,
0.03315606713294983,
-0.024... |
https://github.com/scikit-learn/scikit-learn/issues/25655 | [
"New Feature",
"Needs Investigation"
] | Implement class to reverse item encodings
### Describe the workflow you want to enable
Hi! As a psychologist working with questionnaire data, you will find yourselves in situations where you have to reverse the values in certain variables (i.e. questionnaire items), because they have reversed logic (see [this link]... | 25,655 | [
-0.04422392696142197,
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0.02962435409426689,
0.02566143311560154,
-0.011939452961087227,
0.084... |
https://github.com/scikit-learn/scikit-learn/issues/25655 | [
"New Feature",
"Needs Investigation"
] | Implement class to reverse item encodings
### Describe the workflow you want to enable
Hi! As a psychologist working with questionnaire data, you will find yourselves in situations where you have to reverse the values in certain variables (i.e. questionnaire items), because they have reversed logic (see [this link]... | 25,655 | [
-0.04422392696142197,
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0.02041124366223812,
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0.002379381563514471,
0.02962435409426689,
0.02566143311560154,
-0.011939452961087227,
0.084... |
https://github.com/scikit-learn/scikit-learn/issues/25655 | [
"New Feature",
"Needs Investigation"
] | Implement class to reverse item encodings
### Describe the workflow you want to enable
Hi! As a psychologist working with questionnaire data, you will find yourselves in situations where you have to reverse the values in certain variables (i.e. questionnaire items), because they have reversed logic (see [this link]... | 25,655 | [
-0.04422392696142197,
0.03517474979162216,
0.0012943516485393047,
0.02041124366223812,
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0.029686544090509415,
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0.002379381563514471,
0.02962435409426689,
0.02566143311560154,
-0.011939452961087227,
0.084... |
https://github.com/scikit-learn/scikit-learn/issues/25647 | [
"Bug",
"Needs Triage"
] | sklearn.inspection.PartialDependenceDisplay fails with nulls
### Describe the bug
sklearn.inspection.PartialDependenceDisplay fails with nulls in the dimension of the partial dependence
### Steps/Code to Reproduce
```python
#This is a slimmed down version of the example in the sklear documentation
#https://... | 25,647 | [
-0.008513241074979305,
0.03449510410428047,
0.028567366302013397,
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0.07455074787139893,
0.027704063802957535,
0.00852667260915041,
0.04048... |
https://github.com/scikit-learn/scikit-learn/issues/25642 | [
"New Feature",
"module:impute",
"Needs Decision - Include Feature"
] | SimpleImputer with the rule strategies median-1/median+1
### Describe the workflow you want to enable
I have run into an issue with `SimpleImputer`. Given a feature of, say, integer type, it is completely reasonable to impute the median to missing values. However, when the overall number of records is even, there i... | 25,642 | [
-0.016786957159638405,
0.04228115826845169,
-0.029368320479989052,
-0.042777691036462784,
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-0.018622197210788727,
0.022340692579746246,
0.04948407784104347,
-0.033232182264328,
-0.03435768932104111,
0.06768472492694855,
-0.05155257508158684,
-0.01120277214795351,
0.014... |
https://github.com/scikit-learn/scikit-learn/issues/25642 | [
"New Feature",
"module:impute",
"Needs Decision - Include Feature"
] | SimpleImputer with the rule strategies median-1/median+1
### Describe the workflow you want to enable
I have run into an issue with `SimpleImputer`. Given a feature of, say, integer type, it is completely reasonable to impute the median to missing values. However, when the overall number of records is even, there i... | 25,642 | [
-0.016786957159638405,
0.04228115826845169,
-0.029368320479989052,
-0.042777691036462784,
-0.01943376287817955,
-0.018622197210788727,
0.022340692579746246,
0.04948407784104347,
-0.033232182264328,
-0.03435768932104111,
0.06768472492694855,
-0.05155257508158684,
-0.01120277214795351,
0.014... |
https://github.com/scikit-learn/scikit-learn/issues/25641 | [
"Bug",
"Needs Triage"
] | Importing RandomForestRegressor produces AttributeError: module 'sklearn.metrics._dist_metrics' has no attribute 'DistanceMetric32'
### Describe the bug
Importing RandomForestRegressor produces AttributeError: module 'sklearn.metrics._dist_metrics' has no attribute 'DistanceMetric32'
### Steps/Code to Reproduce
... | 25,641 | [
0.01316988654434681,
-0.02815934084355831,
0.005807235836982727,
-0.007644299883395433,
0.054345760494470596,
0.007005118764936924,
0.015561466105282307,
0.02939310111105442,
0.038980621844530106,
-0.009841623716056347,
0.011423005722463131,
0.007641398347914219,
-0.0035321314353495836,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25637 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in LabelBinarizer
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's LabelBinarizer. Because the dtypes become object dtype when converted to numpy arrays we get `ValueError: Unknown labe... | 25,637 | [
-0.027598083019256592,
-0.018602635711431503,
0.04211258515715599,
-0.003829917637631297,
0.09283918887376785,
0.03226877376437187,
0.07306946814060211,
0.03540761023759842,
-0.03864438459277153,
-0.04037749767303467,
0.009108861908316612,
0.0484892763197422,
-0.0019941392820328474,
0.0390... |
https://github.com/scikit-learn/scikit-learn/issues/25637 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in LabelBinarizer
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's LabelBinarizer. Because the dtypes become object dtype when converted to numpy arrays we get `ValueError: Unknown labe... | 25,637 | [
-0.027598083019256592,
-0.018602635711431503,
0.04211258515715599,
-0.003829917637631297,
0.09283918887376785,
0.03226877376437187,
0.07306946814060211,
0.03540761023759842,
-0.03864438459277153,
-0.04037749767303467,
0.009108861908316612,
0.0484892763197422,
-0.0019941392820328474,
0.0390... |
https://github.com/scikit-learn/scikit-learn/issues/25637 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in LabelBinarizer
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's LabelBinarizer. Because the dtypes become object dtype when converted to numpy arrays we get `ValueError: Unknown labe... | 25,637 | [
-0.027598083019256592,
-0.018602635711431503,
0.04211258515715599,
-0.003829917637631297,
0.09283918887376785,
0.03226877376437187,
0.07306946814060211,
0.03540761023759842,
-0.03864438459277153,
-0.04037749767303467,
0.009108861908316612,
0.0484892763197422,
-0.0019941392820328474,
0.0390... |
https://github.com/scikit-learn/scikit-learn/issues/25637 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in LabelBinarizer
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's LabelBinarizer. Because the dtypes become object dtype when converted to numpy arrays we get `ValueError: Unknown labe... | 25,637 | [
-0.027598083019256592,
-0.018602635711431503,
0.04211258515715599,
-0.003829917637631297,
0.09283918887376785,
0.03226877376437187,
0.07306946814060211,
0.03540761023759842,
-0.03864438459277153,
-0.04037749767303467,
0.009108861908316612,
0.0484892763197422,
-0.0019941392820328474,
0.0390... |
https://github.com/scikit-learn/scikit-learn/issues/25635 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in `confusion_matrix`
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's `confusion_matrix` function. Because the dtypes become object dtype when converted to numpy arrays we get `ValueEr... | 25,635 | [
-0.017536120489239693,
0.00187654048204422,
0.04172457009553909,
0.0025980817154049873,
0.08919023722410202,
0.03632142022252083,
0.06464392691850662,
0.02931635081768036,
-0.01779072731733322,
-0.03795118257403374,
0.013872373849153519,
0.013087376952171326,
-0.020619835704565048,
0.02211... |
https://github.com/scikit-learn/scikit-learn/issues/25634 | [
"New Feature",
"Pandas compatibility"
] | Support nullable pandas dtypes in `unique_labels`
### Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's `unique_labels` function. Because the dtypes become `object` dtype when converted to numpy arrays we get `ValueError:... | 25,634 | [
-0.025013653561472893,
0.017269685864448547,
0.04409753903746605,
-0.00550057552754879,
0.09023743122816086,
0.017768098041415215,
0.07392308115959167,
0.02500973828136921,
-0.025741346180438995,
-0.05016482621431351,
0.008306466974318027,
0.019602041691541672,
-0.03170858323574066,
0.0450... |
https://github.com/scikit-learn/scikit-learn/issues/25632 | [
"Bug",
"Needs Triage"
] | "sklearn.utils.estimator_checks.check_transformer_data_not_an_array" prompts error
### Describe the bug
the function sklearn.utils.estimator_checks.check_transformer_data_not_an_array is trying to apply X.tolist(), where X is a pd.DataFrame. This object has no method called "tolist" hence it prompts the following err... | 25,632 | [
-0.005210819188505411,
0.020116619765758514,
0.020472124218940735,
-0.010604080744087696,
0.11736006289720535,
0.044884540140628815,
0.09219129383563995,
0.05525748059153557,
0.057231444865465164,
-0.0020534831564873457,
-0.010066620074212551,
0.07223375886678696,
0.01583884470164776,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25632 | [
"Bug",
"Needs Triage"
] | "sklearn.utils.estimator_checks.check_transformer_data_not_an_array" prompts error
### Describe the bug
the function sklearn.utils.estimator_checks.check_transformer_data_not_an_array is trying to apply X.tolist(), where X is a pd.DataFrame. This object has no method called "tolist" hence it prompts the following err... | 25,632 | [
-0.005210819188505411,
0.020116619765758514,
0.020472124218940735,
-0.010604080744087696,
0.11736006289720535,
0.044884540140628815,
0.09219129383563995,
0.05525748059153557,
0.057231444865465164,
-0.0020534831564873457,
-0.010066620074212551,
0.07223375886678696,
0.01583884470164776,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25632 | [
"Bug",
"Needs Triage"
] | "sklearn.utils.estimator_checks.check_transformer_data_not_an_array" prompts error
### Describe the bug
the function sklearn.utils.estimator_checks.check_transformer_data_not_an_array is trying to apply X.tolist(), where X is a pd.DataFrame. This object has no method called "tolist" hence it prompts the following err... | 25,632 | [
-0.005210819188505411,
0.020116619765758514,
0.020472124218940735,
-0.010604080744087696,
0.11736006289720535,
0.044884540140628815,
0.09219129383563995,
0.05525748059153557,
0.057231444865465164,
-0.0020534831564873457,
-0.010066620074212551,
0.07223375886678696,
0.01583884470164776,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25632 | [
"Bug",
"Needs Triage"
] | "sklearn.utils.estimator_checks.check_transformer_data_not_an_array" prompts error
### Describe the bug
the function sklearn.utils.estimator_checks.check_transformer_data_not_an_array is trying to apply X.tolist(), where X is a pd.DataFrame. This object has no method called "tolist" hence it prompts the following err... | 25,632 | [
-0.005210819188505411,
0.020116619765758514,
0.020472124218940735,
-0.010604080744087696,
0.11736006289720535,
0.044884540140628815,
0.09219129383563995,
0.05525748059153557,
0.057231444865465164,
-0.0020534831564873457,
-0.010066620074212551,
0.07223375886678696,
0.01583884470164776,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25631 | [
"Bug",
"module:svm",
"Needs Investigation"
] | Inflated results on random-data with SVM
### Describe the bug
When trying to train/evaluate a support vector machine in scikit-learn, I am experiencing some unexpected behaviour and I am wondering whether I am doing something wrong or that this is a possible bug.
In a very specific subset of circumstances, namel... | 25,631 | [
0.023472266271710396,
-0.07637916505336761,
0.03164806589484215,
0.04484990984201431,
0.08486850559711456,
-0.021007420495152473,
-0.030559858307242393,
-0.01541803777217865,
-0.0027667752001434565,
0.02238265424966812,
0.019880399107933044,
-0.003951715305447578,
0.018234319984912872,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25630 | [
"New Feature",
"Needs Decision - Include Feature"
] | Implement class that removes features with NaNs in sklearn.feature_selection module
### Describe the workflow you want to enable
Currently, there seems to be no way in Sklearn to remove features with NaNs (only to impute missing values). As the number of NaNs increases, imputation becomes less trustworthy, so I imp... | 25,630 | [
-0.0006139230681583285,
0.0464879535138607,
0.025295525789260864,
-0.043888773769140244,
0.050203703343868256,
-0.003718140535056591,
0.047751534730196,
-0.002169806743040681,
0.039528507739305496,
0.020914308726787567,
0.033183950930833817,
0.04543078690767288,
-0.03622322902083397,
0.096... |
https://github.com/scikit-learn/scikit-learn/issues/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25628 | [
"Question"
] | TypeError: '<=' not supported between instances of 'str' and 'int' when using fit_predict
I am trying to utilize silhouette analysis on KMeans clustering in order to determine how to choose the optimal number of clusters in a given dataset.
I tried the example code provided on [scikit-learn](https://scikit-learn.org/... | 25,628 | [
-0.007447059266269207,
-0.028247155249118805,
-0.015306809917092323,
-0.012160184793174267,
0.11612791568040848,
-0.01875433884561062,
0.04277486354112625,
0.04608345404267311,
0.029468141496181488,
-0.003980596549808979,
0.0017726827645674348,
0.021027512848377228,
-0.01253578532487154,
0... |
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