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/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32115 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | [ENH] Adding KModes and KPrototypes clustering algorithms
### Describe the workflow you want to enable
Currently, scikit-learn users working with datasets that contain categorical features (e.g., `country`, `profession`, `product_type`) face a significant hurdle. The standard practice is to use one-hot encoding befor... | 32,115 | [
-0.002644790569320321,
0.007892781868577003,
-0.03988632932305336,
-0.03859274834394455,
0.04534715414047241,
0.010089837945997715,
0.05962573364377022,
0.035222046077251434,
0.038704439997673035,
-0.01854337565600872,
-0.019890259951353073,
0.0757167860865593,
-0.01291684340685606,
0.0967... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32112 | [
"API",
"RFC",
"module:compose",
"module:pipeline"
] | RFC Deprecate FeatureUnion and make_union
Unless I'm missing something, to me `FeatureUnion` is just a `ColumnTransformer` where all transformers are applied to all features. So it's just a special case of `ColumnTransformer`.
```py
import pandas as pd
from sklearn.pipeline import FeatureUnion
from sklearn.compose im... | 32,112 | [
-0.03953064605593681,
0.010664774104952812,
0.02041156403720379,
-0.044310566037893295,
0.03566005825996399,
0.02238260954618454,
0.12110774219036102,
-0.06382039189338684,
-0.03418393060564995,
-0.06214458867907524,
0.05879839137196541,
-0.06543407589197159,
0.07285360991954803,
0.0973159... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
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0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
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0.026560744270682335,
0.0020718590822070837,
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-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
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-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32110 | [
"Performance",
"Needs Benchmarks",
"module:neural_network"
] | Optimize Performance of SGDOptimizer and AdamOptimizer with Vectorized Operations
### Describe the workflow you want to enable
I aim to enable a more efficient training workflow for Multilayer Perceptrons (MLPs) in scikit-learn by optimizing the performance of the `SGDOptimizer` and `AdamOptimizer` classes. Currently... | 32,110 | [
-0.02212171070277691,
0.08861380815505981,
0.007127601187676191,
0.0033120361622422934,
0.023288700729608536,
0.0033005941659212112,
0.026560744270682335,
0.0020718590822070837,
-0.043306875973939896,
-0.01742580346763134,
0.029109491035342216,
0.033581409603357315,
-0.0005771241849288344,
... |
https://github.com/scikit-learn/scikit-learn/issues/32109 | [
"Enhancement",
"API",
"Needs Decision",
"module:covariance",
"module:linear_model"
] | Add inner max_iter or a smart automatic setting to Lasso inside graphical lasso
`GraphicalLasso` and `GraphicalLassoCV` expose `enet_tol`. They should also expose `enet_max_iter`.
Currently, the `max_iter` of the *outer iteration* is also used for this inner iteration. This is unfortunate, e.g., if you set a small num... | 32,109 | [
0.013022075407207012,
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0.07613084465265274,
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https://github.com/scikit-learn/scikit-learn/issues/32109 | [
"Enhancement",
"API",
"Needs Decision",
"module:covariance",
"module:linear_model"
] | Add inner max_iter or a smart automatic setting to Lasso inside graphical lasso
`GraphicalLasso` and `GraphicalLassoCV` expose `enet_tol`. They should also expose `enet_max_iter`.
Currently, the `max_iter` of the *outer iteration* is also used for this inner iteration. This is unfortunate, e.g., if you set a small num... | 32,109 | [
0.01868695393204689,
0.010942047461867332,
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0.029772749170660973,
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0.005671223159879446,
0.004066356457769871,
0.07370683550834656,
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https://github.com/scikit-learn/scikit-learn/issues/32109 | [
"Enhancement",
"API",
"Needs Decision",
"module:covariance",
"module:linear_model"
] | Add inner max_iter or a smart automatic setting to Lasso inside graphical lasso
`GraphicalLasso` and `GraphicalLassoCV` expose `enet_tol`. They should also expose `enet_max_iter`.
Currently, the `max_iter` of the *outer iteration* is also used for this inner iteration. This is unfortunate, e.g., if you set a small num... | 32,109 | [
0.009895272552967072,
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0.0399286113679409,
0.05818149074912071,
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0.038992397487163544,
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0.07103519886732101,
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https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
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0.003828560234978795,
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0.09486739337444305,
0.02404318004846573,
0.09589295089244843,
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0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
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0.003828560234978795,
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0.09486739337444305,
0.02404318004846573,
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-0.016804829239845276,
0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
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0.003828560234978795,
-0.03474713861942291,
0.09486739337444305,
0.02404318004846573,
0.09589295089244843,
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-0.016804829239845276,
0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
-0.007143034134060144,
0.003828560234978795,
-0.03474713861942291,
0.09486739337444305,
0.02404318004846573,
0.09589295089244843,
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-0.016804829239845276,
0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
-0.007143034134060144,
0.003828560234978795,
-0.03474713861942291,
0.09486739337444305,
0.02404318004846573,
0.09589295089244843,
-0.03823378309607506,
-0.03979162499308586,
-0.016804829239845276,
0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32104 | [
"Bug"
] | FeatureUnion with polars output can error due to duplicate column names
### Describe the bug
FeatureUnion concatenates outputs of its transformers _before_ the `set_output` wrapper renames columns based on `get_feature_names_out` (adding the transformer name prefix). This works with pandas but not polars which does n... | 32,104 | [
0.007460850290954113,
-0.007143034134060144,
0.003828560234978795,
-0.03474713861942291,
0.09486739337444305,
0.02404318004846573,
0.09589295089244843,
-0.03823378309607506,
-0.03979162499308586,
-0.016804829239845276,
0.011178703978657722,
-0.004770207684487104,
0.06914632767438889,
0.038... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
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-0.03540924936532974,
0.049356743693351746,
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-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
-0.049443840980529785,
-0.03540924936532974,
0.049356743693351746,
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-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
-0.049443840980529785,
-0.03540924936532974,
0.049356743693351746,
-0.04012754559516907,
-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
-0.049443840980529785,
-0.03540924936532974,
0.049356743693351746,
-0.04012754559516907,
-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
-0.049443840980529785,
-0.03540924936532974,
0.049356743693351746,
-0.04012754559516907,
-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32099 | [
"Bug"
] | DecisionTreeRegressor with absolute error criterion: non-optimal split
### Describe the bug
While working on fixing the issue https://github.com/scikit-learn/scikit-learn/issues/9626, I noticed that in some cases, the current implementation of `DecisionTreeRegressor(criterion="absolute_error")` doesn't not find the o... | 32,099 | [
0.02307530678808689,
-0.009125303477048874,
0.03514564782381058,
0.030484870076179504,
0.03819938004016876,
-0.049443840980529785,
-0.03540924936532974,
0.049356743693351746,
-0.04012754559516907,
-0.008748003281652927,
0.045336686074733734,
0.02786901593208313,
0.01451114285737276,
-0.047... |
https://github.com/scikit-learn/scikit-learn/issues/32095 | [
"Bug"
] | Using `fetch_20newsgroups` with multiple pytest workers leads to race
### Describe the bug
When using `pytest-xdist` with several workers to run a test suite that uses `fetch_20newsgroups` as a fixture (`scope="session"`) the dataset shape is sometimes wrong. For example I just had a run where `X.shape=(5902, 68435) ... | 32,095 | [
-0.03226177766919136,
0.009651966392993927,
-0.025506244972348213,
0.04402146115899086,
0.021488917991518974,
-0.019744614139199257,
0.07274297624826431,
0.03969898447394371,
0.006251863203942776,
0.022681325674057007,
-0.02253224328160286,
-0.05876830965280533,
-0.05444001033902168,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/32095 | [
"Bug"
] | Using `fetch_20newsgroups` with multiple pytest workers leads to race
### Describe the bug
When using `pytest-xdist` with several workers to run a test suite that uses `fetch_20newsgroups` as a fixture (`scope="session"`) the dataset shape is sometimes wrong. For example I just had a run where `X.shape=(5902, 68435) ... | 32,095 | [
-0.03226177766919136,
0.009651966392993927,
-0.025506244972348213,
0.04402146115899086,
0.021488917991518974,
-0.019744614139199257,
0.07274297624826431,
0.03969898447394371,
0.006251863203942776,
0.022681325674057007,
-0.02253224328160286,
-0.05876830965280533,
-0.05444001033902168,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/32095 | [
"Bug"
] | Using `fetch_20newsgroups` with multiple pytest workers leads to race
### Describe the bug
When using `pytest-xdist` with several workers to run a test suite that uses `fetch_20newsgroups` as a fixture (`scope="session"`) the dataset shape is sometimes wrong. For example I just had a run where `X.shape=(5902, 68435) ... | 32,095 | [
-0.03226177766919136,
0.009651966392993927,
-0.025506244972348213,
0.04402146115899086,
0.021488917991518974,
-0.019744614139199257,
0.07274297624826431,
0.03969898447394371,
0.006251863203942776,
0.022681325674057007,
-0.02253224328160286,
-0.05876830965280533,
-0.05444001033902168,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/32090 | [
"Bug",
"Documentation"
] | Unpickling ColumnTransformer fitted in 1.6.1 fails in 1.7.1 with AttributeError: _RemainderColsList
### Describe the bug
**Summary**
A `ColumnTransformer` pickled with **scikit-learn 1.6.1** cannot be unpickled with **1.7.1** (and other versions > 1.6.1). The unpickling fails before any method call with:
```bash
A... | 32,090 | [
-0.007168160751461983,
0.06074659153819084,
0.028645819053053856,
-0.04453305900096893,
0.03655834496021271,
0.01291393768042326,
0.03676914796233177,
0.05333925783634186,
0.02892390824854374,
-0.01404623780399561,
0.04456203058362007,
0.07291070371866226,
0.0038338927552103996,
-0.0049332... |
https://github.com/scikit-learn/scikit-learn/issues/32090 | [
"Bug",
"Documentation"
] | Unpickling ColumnTransformer fitted in 1.6.1 fails in 1.7.1 with AttributeError: _RemainderColsList
### Describe the bug
**Summary**
A `ColumnTransformer` pickled with **scikit-learn 1.6.1** cannot be unpickled with **1.7.1** (and other versions > 1.6.1). The unpickling fails before any method call with:
```bash
A... | 32,090 | [
-0.007168160751461983,
0.06074659153819084,
0.028645819053053856,
-0.04453305900096893,
0.03655834496021271,
0.01291393768042326,
0.03676914796233177,
0.05333925783634186,
0.02892390824854374,
-0.01404623780399561,
0.04456203058362007,
0.07291070371866226,
0.0038338927552103996,
-0.0049332... |
https://github.com/scikit-learn/scikit-learn/issues/32090 | [
"Bug",
"Documentation"
] | Unpickling ColumnTransformer fitted in 1.6.1 fails in 1.7.1 with AttributeError: _RemainderColsList
### Describe the bug
**Summary**
A `ColumnTransformer` pickled with **scikit-learn 1.6.1** cannot be unpickled with **1.7.1** (and other versions > 1.6.1). The unpickling fails before any method call with:
```bash
A... | 32,090 | [
-0.007168160751461983,
0.06074659153819084,
0.028645819053053856,
-0.04453305900096893,
0.03655834496021271,
0.01291393768042326,
0.03676914796233177,
0.05333925783634186,
0.02892390824854374,
-0.01404623780399561,
0.04456203058362007,
0.07291070371866226,
0.0038338927552103996,
-0.0049332... |
https://github.com/scikit-learn/scikit-learn/issues/32090 | [
"Bug",
"Documentation"
] | Unpickling ColumnTransformer fitted in 1.6.1 fails in 1.7.1 with AttributeError: _RemainderColsList
### Describe the bug
**Summary**
A `ColumnTransformer` pickled with **scikit-learn 1.6.1** cannot be unpickled with **1.7.1** (and other versions > 1.6.1). The unpickling fails before any method call with:
```bash
A... | 32,090 | [
-0.007168160751461983,
0.06074659153819084,
0.028645819053053856,
-0.04453305900096893,
0.03655834496021271,
0.01291393768042326,
0.03676914796233177,
0.05333925783634186,
0.02892390824854374,
-0.01404623780399561,
0.04456203058362007,
0.07291070371866226,
0.0038338927552103996,
-0.0049332... |
https://github.com/scikit-learn/scikit-learn/issues/32087 | [
"Bug"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Sep 27, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=80471&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Sep 27, 2025)
- test_get_met... | 32,087 | [
-0.029064752161502838,
0.0177699513733387,
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0.034355442970991135,
0.029710521921515465,
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0.000514839543029666,
0.03963829204440117,
0.08605864644050598,
-0.04138211905956268,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32087 | [
"Bug"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Sep 27, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=80471&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Sep 27, 2025)
- test_get_met... | 32,087 | [
-0.023173082619905472,
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0.005401242058724165,
0.020531749352812767,
0.024726159870624542,
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0.020480887964367867,
0.05531525984406471,
0.045644935220479965,
-0.008710503578186035,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/32087 | [
"Bug"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Sep 27, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=80471&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Sep 27, 2025)
- test_get_met... | 32,087 | [
-0.007673963904380798,
0.023904645815491676,
0.01626516319811344,
-0.006210506893694401,
0.02647142857313156,
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0.03166986629366875,
0.015285675413906574,
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0.017017535865306854,
0.02166627161204815,
0.02562594972550869,
0.00020125506853219122,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/32087 | [
"Bug"
] | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Sep 27, 2025) ⚠️
**CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=80471&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Sep 27, 2025)
- test_get_met... | 32,087 | [
-0.026232445612549782,
0.033087123185396194,
-0.006316306069493294,
-0.018469132483005524,
0.04200487211346626,
0.0021788040176033974,
0.022461390122771263,
0.008817738853394985,
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0.01944226212799549,
0.0526357963681221,
0.046421345323324203,
-0.00999369379132986,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32086 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Sep 03, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=79590&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Sep 03, 2025)
- Test Collection Failure
COMMENT:
##... | 32,086 | [
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0.03761511668562889,
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0.02897864207625389,
0.044234499335289,
0.03733774647116661,
-0.004480408970266581,
0.093315355... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.03033754974603653,
0.005520842503756285,
0.015353771857917309,
0.042034655809402466,
0.05748867988586426,
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0.0048210229724645615,
0.05089453607797623,
0.05256330221891403,
-0.016080332919955254,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.03510705381631851,
-0.02798326313495636,
0.012419774197041988,
0.039805538952350616,
0.028982674703001976,
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0.013847876340150833,
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0.010255907662212849,
0.07959269732236862,
0.04028505086898804,
0.00790734775364399,
-0.041... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.03158234804868698,
-0.013130907900631428,
0.006699475459754467,
0.03896350413560867,
0.04616399481892586,
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0.018256941810250282,
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0.005290254019200802,
0.07266899943351746,
0.043966054916381836,
0.008231340907514095,
-0.026... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.03191453590989113,
-0.013099245727062225,
0.007980788126587868,
0.03746643662452698,
0.03878235071897507,
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0.0074256546795368195,
0.006217343267053366,
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0.0025676731020212173,
0.0809154361486435,
0.03983086720108986,
-0.0010654565412551165,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.02227669768035412,
-0.006271542981266975,
0.005545496940612793,
0.029911812394857407,
0.029577745124697685,
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0.03189240023493767,
-0.005605717655271292,
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0.00685490109026432,
0.07447206228971481,
0.029724104329943657,
0.010249782353639603,
-0.021... |
https://github.com/scikit-learn/scikit-learn/issues/32083 | [
"Documentation"
] | 1.1.8 LARS Lasso at Mathematical Formulation
### Describe the issue linked to the documentation
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the l1 norm of the parameter vector.
* not a curve
* "curve" is not computed at every point
* infinitely man... | 32,083 | [
0.03169969469308853,
-0.013590811751782894,
0.009815430268645287,
0.038082074373960495,
0.04444112256169319,
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0.0009036282426677644,
0.005999458953738213,
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0.0024165587965399027,
0.07515744864940643,
0.04266851767897606,
-0.002747330814599991,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.023015527054667473,
0.057162728160619736,
-0.008981183171272278,
0.006178760901093483,
0.06124243885278702,
0.03246740624308586,
0.09749672561883926,
0.024796180427074432,
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0.004325774032622576,
0.03245052322745323,
0.009453240782022476,
0.0550... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.02678360417485237,
0.056621797382831573,
0.007126220501959324,
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0.06619561463594437,
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0.09434427320957184,
0.023774638772010803,
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0.010824077762663364,
0.042969901114702225,
0.008378514088690281,
0.04541... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.020605526864528656,
0.07765352725982666,
-0.011540669947862625,
0.007807682268321514,
0.054335203021764755,
0.024478325620293617,
0.0905325636267662,
0.029284603893756866,
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0.014520478434860706,
0.03086203895509243,
0.003757466096431017,
0.0511... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.01940569281578064,
0.05805953964591026,
-0.004972338210791349,
0.005884076002985239,
0.0577743835747242,
0.034870922565460205,
0.10264391452074051,
0.022048501297831535,
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-0.0058203586377203465,
0.010922606103122234,
0.03317312151193619,
0.0136283989995718,
0.059160... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.025876879692077637,
0.05873991176486015,
-0.011314533650875092,
0.006921570748090744,
0.058306820690631866,
0.03283232823014259,
0.0966440886259079,
0.02762649394571781,
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-0.001968067605048418,
0.006027686409652233,
0.03267122432589531,
0.008572902530431747,
0.0582... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.023131348192691803,
0.05685785412788391,
-0.009612900204956532,
0.013408849015831947,
0.06814968585968018,
0.011239239946007729,
0.09067784994840622,
0.02917104959487915,
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0.0010233481880277395,
0.013572492636740208,
0.0296915415674448,
0.021440181881189346,
0.06004... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.018724242225289345,
0.05122259631752968,
0.002062880666926503,
0.013911201618611813,
0.06497127562761307,
0.01311760675162077,
0.09250258654356003,
0.029149970039725304,
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-0.020068923011422157,
0.010222462937235832,
0.03539712727069855,
0.009271061047911644,
0.05128... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.024569017812609673,
0.06425244361162186,
-0.016701452434062958,
0.00247298926115036,
0.062120221555233,
0.03256845474243164,
0.09825709462165833,
0.021194368600845337,
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-0.001349343452602625,
0.008856560103595257,
0.035232994705438614,
0.004337315913289785,
0.064207... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.017606046050786972,
0.04379219189286232,
0.007787194103002548,
0.009147921577095985,
0.06721038371324539,
0.01951509527862072,
0.09950533509254456,
0.02301267348229885,
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-0.025120463222265244,
0.023285990580916405,
0.04041729122400284,
0.005480202846229076,
0.041652... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.02141788974404335,
0.06248360127210617,
-0.009935279376804829,
0.011386513710021973,
0.04306361824274063,
0.017070263624191284,
0.101566843688488,
0.036792680621147156,
-0.05819198861718178,
-0.01003605592995882,
0.0066196732223033905,
0.025740332901477814,
0.001746813184581697,
0.05876... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.026958785951137543,
0.0403296984732151,
-0.004354425240308046,
0.019158704206347466,
0.06904060393571854,
0.022644080221652985,
0.07425708323717117,
0.02582549676299095,
-0.04408257454633713,
0.00048580317525193095,
0.023309126496315002,
0.027497578412294388,
0.002656553639099002,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/32076 | [
"Enhancement",
"help wanted"
] | ```TargetEncoder``` should take ```groups``` as an argument
### Describe the workflow you want to enable
The current implementation of TargetEncoder uses ```KFold```-cross-validation to avoid data leakage. In cases of longitudinal or clustered data, it is desirable to ensure that rows belonging to the same group or c... | 32,076 | [
-0.029985155910253525,
0.05277726426720619,
-0.008549774065613747,
0.024401411414146423,
0.04262833669781685,
0.015742309391498566,
0.0777515321969986,
0.03960254043340683,
-0.041971709579229355,
-0.018936853855848312,
0.013238268904387951,
0.015385851263999939,
-0.002044420689344406,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/32075 | [
"RFC"
] | RFC new fitted attributes for LogisticRegressionCV
Contributes to #11865.
### Fitted Attributes
After the removal of `multi_class` and any OvR-logic in `LogisticRegressionCV` in #32073, there are a few fitted attributes that have now (or always had) a strange data format (I neglect l1_ratios in the following for ease... | 32,075 | [
-0.021472329273819923,
0.005483897868543863,
0.03476102650165558,
0.035262834280729294,
0.034927815198898315,
0.001962082227692008,
0.04402868077158928,
0.02460920251905918,
0.00012071750097675249,
0.0019259527325630188,
0.06843859702348709,
-0.020451048389077187,
0.006694653537124395,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32075 | [
"RFC"
] | RFC new fitted attributes for LogisticRegressionCV
Contributes to #11865.
### Fitted Attributes
After the removal of `multi_class` and any OvR-logic in `LogisticRegressionCV` in #32073, there are a few fitted attributes that have now (or always had) a strange data format (I neglect l1_ratios in the following for ease... | 32,075 | [
-0.021472329273819923,
0.005483897868543863,
0.03476102650165558,
0.035262834280729294,
0.034927815198898315,
0.001962082227692008,
0.04402868077158928,
0.02460920251905918,
0.00012071750097675249,
0.0019259527325630188,
0.06843859702348709,
-0.020451048389077187,
0.006694653537124395,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32072 | [
"Bug",
"Needs Triage"
] | LogisticRegressionCV intercept is wrong
### Describe the bug
The intercept calculated by `LogisticRegressionCV` is wrong.
A bit related to #11865.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV... | 32,072 | [
-0.019673891365528107,
-0.03898872062563896,
0.030184520408511162,
0.0505676232278347,
0.0645250678062439,
-0.03888344764709473,
0.05959514155983925,
0.004777921363711357,
0.05658828839659691,
0.004617850761860609,
0.05303945764899254,
0.023670295253396034,
0.004836810752749443,
0.00337829... |
https://github.com/scikit-learn/scikit-learn/issues/32067 | [
"New Feature",
"Needs Triage"
] | Enhance the warning message for metadata default value change
### Describe the workflow you want to enable
Currently the warning raised for [Deprecation / Default Value Change](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_metadata_routing.html#deprecation-default-value-change)
is quite generic
```... | 32,067 | [
0.011897331103682518,
0.07386486232280731,
0.006679686717689037,
-0.03645943105220795,
0.029811706393957138,
-0.012764332816004753,
-0.016362879425287247,
0.0012411943171173334,
-0.03756891191005707,
-0.0016165621345862746,
0.06919803470373154,
0.08828993141651154,
-0.07569922506809235,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03980743885040283,
0.014909641817212105,
0.01319982297718525,
-0.0029553542844951153,
0.04128642752766609,
-0.022911231964826584,
0.012380784377455711,
0.0047640735283494,
-0.01127196941524744,
0.0353073887526989,
0.06279248744249344,
-0.004043178167194128,
0.036020677536726,
0.060460533... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.029090452939271927,
0.008494109846651554,
0.011595473624765873,
-0.012745124287903309,
0.041370000690221786,
-0.03011353872716427,
0.015269712544977665,
0.005656234920024872,
-0.006173161789774895,
0.04233092814683914,
0.057191457599401474,
0.00813324935734272,
0.032313600182533264,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03550126031041145,
0.008967271074652672,
0.011152499355375767,
-0.011315830051898956,
0.03884380683302879,
-0.029412241652607918,
0.014762445352971554,
0.00016907128156162798,
-0.011764206923544407,
0.043426357209682465,
0.061909791082143784,
-0.0021797039080411196,
0.028043601661920547,
... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03359846770763397,
0.012677454389631748,
0.012589598074555397,
-0.014489908702671528,
0.04324396699666977,
-0.025708017870783806,
0.01621934399008751,
0.004556868225336075,
-0.0031753985676914454,
0.042248450219631195,
0.05146501958370209,
-0.0014111336786299944,
0.032986462116241455,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.036401208490133286,
0.020935246720910072,
0.015026908367872238,
-0.01617790386080742,
0.0345153734087944,
-0.035208191722631454,
0.019022805616259575,
0.005517550278455019,
-0.013308417052030563,
0.03193063661456108,
0.06702687591314316,
0.007419877219945192,
0.03707139194011688,
0.06239... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03548409044742584,
0.01069752499461174,
0.013157744891941547,
-0.013252886943519115,
0.04212625324726105,
-0.02996625192463398,
0.01431951578706503,
-0.0006719189113937318,
-0.010712530463933945,
0.04353148490190506,
0.06272045522928238,
-0.004300883039832115,
0.02906888723373413,
0.0678... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03660954535007477,
0.008167745545506477,
0.009253955446183681,
-0.016895346343517303,
0.03816128522157669,
-0.028974836692214012,
0.014219284988939762,
0.0007506607216782868,
-0.004087465349584818,
0.039745889604091644,
0.06488046050071716,
-0.0013314718380570412,
0.02669570967555046,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.037981826812028885,
0.009036369621753693,
0.010888893157243729,
-0.007599161006510258,
0.037944354116916656,
-0.028447862714529037,
0.012979199178516865,
0.003723220666870475,
-0.010478928685188293,
0.04264683276414871,
0.06368070095777512,
-0.0014132513897493482,
0.030414650216698647,
0... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.03500261530280113,
0.01302518043667078,
0.02015637792646885,
-0.00840115174651146,
0.045239146798849106,
-0.030982518568634987,
0.015240716747939587,
0.0038777897134423256,
-0.01168619841337204,
0.04298482462763786,
0.059522390365600586,
0.003249489003792405,
0.029472481459379196,
0.0636... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.0346161425113678,
0.01361869927495718,
0.017052335664629936,
-0.012587475590407848,
0.05203785002231598,
-0.029118236154317856,
0.0035023754462599754,
0.012343122623860836,
-0.0058879554271698,
0.03976364806294441,
0.06566336005926132,
-0.0007915050373412669,
0.02309761941432953,
0.06481... |
https://github.com/scikit-learn/scikit-learn/issues/32062 | [
"Bug",
"Needs Investigation"
] | Regressor Prediction Makes a Negative Y Offset
### Describe the bug
Hi, I've found a strange situation where regressor prediction makes a negative Y offset. See an orange line on my picture below.
Here is my py file and json data:
[test_scikit.zip](https://github.com/user-attachments/files/22069020/test_scikit.zip)
... | 32,062 | [
0.019258135929703712,
0.018150759860873222,
0.011656149290502071,
-0.019480779767036438,
0.04174063727259636,
-0.03203483670949936,
0.006842430215328932,
0.004459861200302839,
-0.030113253742456436,
0.031240321695804596,
0.039880696684122086,
0.003214009338989854,
0.017729351297020912,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32049 | [
"Documentation",
"module:metrics"
] | The dcg_score and ndcg_score documentation are hard to understand
### Describe the issue linked to the documentation
The documentation for the `dcg_score` and `ndcg_score` leave much to be desired.
I believe this is also a by-product of competing definitions of the discount cumulative gains (DCG) and normalised DCG ... | 32,049 | [
-0.04699716344475746,
0.002138422569260001,
0.023413674905896187,
-0.03684462234377861,
-0.02469068020582199,
0.022812185809016228,
0.031141076236963272,
-0.019623201340436935,
-0.02054448612034321,
-0.007152851205319166,
0.007036249153316021,
-0.002207226352766156,
0.03298875316977501,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/32048 | [
"New Feature",
"Needs Decision - Include Feature"
] | Leiden Clustering
### Describe the workflow you want to enable
The "Leiden" Clustering algorithm is considered one of the most powerful clustering algorithms, often outperforming competitors by a wide margin.
The algorithm fulfils the inclusion criteria: its now 6 years old, has some 5200 citations.
Currently, it ... | 32,048 | [
0.01928676664829254,
-0.013882909901440144,
0.0021179344039410353,
0.0008894777856767178,
-0.058821145445108414,
-0.009982354007661343,
0.028018126264214516,
-0.005016196519136429,
0.08755432069301605,
0.00603290181607008,
0.006168076768517494,
0.04741568863391876,
0.009649282321333885,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/32048 | [
"New Feature",
"Needs Decision - Include Feature"
] | Leiden Clustering
### Describe the workflow you want to enable
The "Leiden" Clustering algorithm is considered one of the most powerful clustering algorithms, often outperforming competitors by a wide margin.
The algorithm fulfils the inclusion criteria: its now 6 years old, has some 5200 citations.
Currently, it ... | 32,048 | [
-0.006734014023095369,
-0.011187546886503696,
0.017639953643083572,
0.008225614205002785,
-0.06637171655893326,
-0.019540326669812202,
0.008508565835654736,
-0.013978532515466213,
0.11487225443124771,
0.016373105347156525,
0.0026240397710353136,
0.03117801807820797,
0.022879673168063164,
0... |
https://github.com/scikit-learn/scikit-learn/issues/32048 | [
"New Feature",
"Needs Decision - Include Feature"
] | Leiden Clustering
### Describe the workflow you want to enable
The "Leiden" Clustering algorithm is considered one of the most powerful clustering algorithms, often outperforming competitors by a wide margin.
The algorithm fulfils the inclusion criteria: its now 6 years old, has some 5200 citations.
Currently, it ... | 32,048 | [
0.009460609406232834,
-0.004669016692787409,
0.004888480994850397,
0.002124734688550234,
-0.0457889623939991,
-0.014334841631352901,
-0.007607019506394863,
-0.006304034031927586,
0.11439630389213562,
0.015709610655903816,
-0.013502093032002449,
0.04037381708621979,
0.00559943076223135,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/32048 | [
"New Feature",
"Needs Decision - Include Feature"
] | Leiden Clustering
### Describe the workflow you want to enable
The "Leiden" Clustering algorithm is considered one of the most powerful clustering algorithms, often outperforming competitors by a wide margin.
The algorithm fulfils the inclusion criteria: its now 6 years old, has some 5200 citations.
Currently, it ... | 32,048 | [
0.006873069331049919,
0.023132039234042168,
0.012588120065629482,
-0.016493776813149452,
-0.03944472596049309,
-0.012805819511413574,
0.03000156581401825,
-0.03197799250483513,
0.09534776210784912,
0.00961955264210701,
-0.02783719077706337,
0.052077047526836395,
0.014281006529927254,
-0.00... |
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