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/25413 | [
"API",
"Needs Decision",
"module:tree"
] | [Refactor Request Tree] Make sorting and splitting utility functions cimportable by including them in the pxd files
Hi,
I was wondering if it is possible to define the relevant splitter utility functions in the `.pxd` file so that it is cimportable from 3rd party applications?
## Motivation
3rd party applicatio... | 25,413 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/25413 | [
"API",
"Needs Decision",
"module:tree"
] | [Refactor Request Tree] Make sorting and splitting utility functions cimportable by including them in the pxd files
Hi,
I was wondering if it is possible to define the relevant splitter utility functions in the `.pxd` file so that it is cimportable from 3rd party applications?
## Motivation
3rd party applicatio... | 25,413 | [
0.006656566634774208,
0.06828641146421432,
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0.029631366953253746,
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0.03371697664260864,
-0.05620845407247543,
... |
https://github.com/scikit-learn/scikit-learn/issues/25412 | [
"New Feature",
"module:preprocessing"
] | InteractionTransformer
### Describe the workflow you want to enable
The latest 1.2 release is full of great features, e.g., full column name support. This brings me to one of my most desired features regarding building strong and realistic linear models: Interactions!
It is currently very hard to add interaction... | 25,412 | [
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0.09086649119853973,
0.014983463101089,
0.008598385378718376,
0.029925702139735222,
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0.01726399175822735,
0.12636... |
https://github.com/scikit-learn/scikit-learn/issues/25412 | [
"New Feature",
"module:preprocessing"
] | InteractionTransformer
### Describe the workflow you want to enable
The latest 1.2 release is full of great features, e.g., full column name support. This brings me to one of my most desired features regarding building strong and realistic linear models: Interactions!
It is currently very hard to add interaction... | 25,412 | [
-0.017321091145277023,
0.09086649119853973,
0.014983463101089,
0.008598385378718376,
0.029925702139735222,
-0.0180569626390934,
0.06434442847967148,
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0.0027082012966275215,
-0.014641436748206615,
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-0.022771546617150307,
0.01726399175822735,
0.12636... |
https://github.com/scikit-learn/scikit-learn/issues/25412 | [
"New Feature",
"module:preprocessing"
] | InteractionTransformer
### Describe the workflow you want to enable
The latest 1.2 release is full of great features, e.g., full column name support. This brings me to one of my most desired features regarding building strong and realistic linear models: Interactions!
It is currently very hard to add interaction... | 25,412 | [
-0.017321091145277023,
0.09086649119853973,
0.014983463101089,
0.008598385378718376,
0.029925702139735222,
-0.0180569626390934,
0.06434442847967148,
-0.01715530827641487,
0.0027082012966275215,
-0.014641436748206615,
-0.01989511400461197,
-0.022771546617150307,
0.01726399175822735,
0.12636... |
https://github.com/scikit-learn/scikit-learn/issues/25409 | [
"Documentation",
"module:feature_extraction"
] | correct and reasonable new example to replace the old one
https://github.com/scikit-learn/scikit-learn/blob/98cf537f5c538fdbc9d27b851cf03ce7611b8a48/sklearn/feature_extraction/image.py#L496
The problem of the old example is that it did not consider the "n_samples" dimension of the function `from sklearn.feature_ext... | 25,409 | [
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0.011592945083975792,
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0.005905414931476116,
0.04207312688231468,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25405 | [
"New Feature",
"Needs Triage"
] | Feature request: for GridSearchCV and RandomizedSearchCV, add a kwarg for a preprocessing step after CV splits have been made
### Describe the workflow you want to enable
See solution
### Describe your proposed solution
Add a new kwarg to cross-validators such as GridSearchCV and RandomizedSearchCV that allows you ... | 25,405 | [
-0.051065973937511444,
-0.023574795573949814,
0.017155908048152924,
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0.025401046499609947,
0.04081004858016968,
-0.015814365819096565,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25403 | [
"Bug",
"Needs Triage"
] | test_spectral_embedding_two_components[float32-lobpcg] fails with scipy 1.10
### Describe the bug
On openSUSE Tumbleweed, when updating scipy to 1.10.0, a test starts to fail when packaging scikit-learn.
### Steps/Code to Reproduce
```
docker pull opensuse/tumbleweed
docker run -it --name tumbleweed-sklearn opens... | 25,403 | [
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0.0005260431207716465,
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0.018489636480808258,
0.07237230986356735,
0.003813603427261114,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25403 | [
"Bug",
"Needs Triage"
] | test_spectral_embedding_two_components[float32-lobpcg] fails with scipy 1.10
### Describe the bug
On openSUSE Tumbleweed, when updating scipy to 1.10.0, a test starts to fail when packaging scikit-learn.
### Steps/Code to Reproduce
```
docker pull opensuse/tumbleweed
docker run -it --name tumbleweed-sklearn opens... | 25,403 | [
0.0046795387752354145,
-0.034292954951524734,
0.0005260431207716465,
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0.027480348944664,
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0.03746318444609642,
0.029688380658626556,
-0.0168343186378479,
0.018489636480808258,
0.07237230986356735,
0.003813603427261114,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
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0.0658051073551178,
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0.021720416843891144,
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0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
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0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
0.03454058617353439,
0.0038302235770970583,
0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
0.03454058617353439,
0.0038302235770970583,
0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
0.03454058617353439,
0.0038302235770970583,
0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
0.03454058617353439,
0.0038302235770970583,
0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25401 | [
"Bug"
] | PartialDependence categorical not working with missings
### Describe the bug
Since version 1.2, the partial dependence plot can deal with categorical features - fantastic! We encountered a small but annoying issue when there are missing values in the categoricals:
- Missing values in numeric features are handled... | 25,401 | [
0.017284531146287918,
0.06716616451740265,
0.02043675072491169,
-0.019040411338210106,
0.0658051073551178,
0.0185200497508049,
0.021720416843891144,
0.03454058617353439,
0.0038302235770970583,
0.0028918120078742504,
0.03295935317873955,
-0.00699749356135726,
0.0352097786962986,
0.039118699... |
https://github.com/scikit-learn/scikit-learn/issues/25400 | [
"New Feature",
"Needs Triage"
] | OrdinalEncoder with option to mention the start index
### Describe the workflow you want to enable
Hi,
Can OrdinalEncoder be provided with an argument to decide what index to start encoding on
### Describe your proposed solution
For example
```
import numpy as np
from sklearn.preprocessing import OrdinalEn... | 25,400 | [
-0.027199488133192062,
0.1120242178440094,
0.0205878596752882,
-0.01846039853990078,
0.029077982529997826,
0.023649411275982857,
-0.0004919321509078145,
0.018194982782006264,
-0.06004020944237709,
-0.035037629306316376,
0.08596694469451904,
0.08182673901319504,
-0.02699285000562668,
0.0478... |
https://github.com/scikit-learn/scikit-learn/issues/25400 | [
"New Feature",
"Needs Triage"
] | OrdinalEncoder with option to mention the start index
### Describe the workflow you want to enable
Hi,
Can OrdinalEncoder be provided with an argument to decide what index to start encoding on
### Describe your proposed solution
For example
```
import numpy as np
from sklearn.preprocessing import OrdinalEn... | 25,400 | [
-0.03724541887640953,
0.10016467422246933,
0.02065751887857914,
-0.0023750620894134045,
0.04140559583902359,
0.024581151083111763,
0.01829349435865879,
0.027663547545671463,
-0.04753715172410011,
-0.02843041718006134,
0.08199337124824524,
0.08113212883472443,
-0.01544248964637518,
0.038089... |
https://github.com/scikit-learn/scikit-learn/issues/25400 | [
"New Feature",
"Needs Triage"
] | OrdinalEncoder with option to mention the start index
### Describe the workflow you want to enable
Hi,
Can OrdinalEncoder be provided with an argument to decide what index to start encoding on
### Describe your proposed solution
For example
```
import numpy as np
from sklearn.preprocessing import OrdinalEn... | 25,400 | [
-0.027398649603128433,
0.10972075909376144,
0.019626423716545105,
-0.016834264621138573,
0.025352638214826584,
0.018881293013691902,
0.0005995170213282108,
0.018430562689900398,
-0.060058947652578354,
-0.0348319485783577,
0.08115053921937943,
0.08300326019525528,
-0.024318663403391838,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25400 | [
"New Feature",
"Needs Triage"
] | OrdinalEncoder with option to mention the start index
### Describe the workflow you want to enable
Hi,
Can OrdinalEncoder be provided with an argument to decide what index to start encoding on
### Describe your proposed solution
For example
```
import numpy as np
from sklearn.preprocessing import OrdinalEn... | 25,400 | [
-0.0284804068505764,
0.11026522517204285,
0.01891372911632061,
-0.01726357266306877,
0.025410547852516174,
0.019589204341173172,
0.0017491719918325543,
0.01826157607138157,
-0.05918164178729057,
-0.035987578332424164,
0.08287150412797928,
0.08352085202932358,
-0.025709619745612144,
0.04752... |
https://github.com/scikit-learn/scikit-learn/issues/25399 | [
"Bug",
"Needs Triage"
] | RandomForestClassifier allows float max_samples greater than 1 without raising exception
### Describe the bug
When using the RandomForestClassifier from scikit-learn, the model allows passing float values greater than 1 for the max_samples parameter without raising an exception, which is unexpected. I expected the co... | 25,399 | [
0.006880727130919695,
-0.09125716984272003,
0.03870706632733345,
-0.015525467693805695,
0.06755392253398895,
-0.02078232914209366,
0.0027215606532990932,
0.013760710135102272,
0.03767189383506775,
-0.003963848575949669,
0.06600473076105118,
-0.0032879651989787817,
-0.02449042722582817,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25397 | [
"Bug",
"Needs Reproducible Code"
] | error: 'i' format requires -2147483648 <= number <= 2147483647
### Describe the bug
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import ut... | 25,397 | [
-0.025839025154709816,
0.004372808150947094,
0.011901693418622017,
-0.03291749209165573,
0.07354824244976044,
-0.017883779481053352,
0.02123897522687912,
0.04175267741084099,
0.00838875025510788,
-0.02054813876748085,
0.03319970890879631,
-0.03182723745703697,
0.028395919129252434,
0.05613... |
https://github.com/scikit-learn/scikit-learn/issues/25397 | [
"Bug",
"Needs Reproducible Code"
] | error: 'i' format requires -2147483648 <= number <= 2147483647
### Describe the bug
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import ut... | 25,397 | [
-0.025839025154709816,
0.004372808150947094,
0.011901693418622017,
-0.03291749209165573,
0.07354824244976044,
-0.017883779481053352,
0.02123897522687912,
0.04175267741084099,
0.00838875025510788,
-0.02054813876748085,
0.03319970890879631,
-0.03182723745703697,
0.028395919129252434,
0.05613... |
https://github.com/scikit-learn/scikit-learn/issues/25397 | [
"Bug",
"Needs Reproducible Code"
] | error: 'i' format requires -2147483648 <= number <= 2147483647
### Describe the bug
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import ut... | 25,397 | [
-0.025839025154709816,
0.004372808150947094,
0.011901693418622017,
-0.03291749209165573,
0.07354824244976044,
-0.017883779481053352,
0.02123897522687912,
0.04175267741084099,
0.00838875025510788,
-0.02054813876748085,
0.03319970890879631,
-0.03182723745703697,
0.028395919129252434,
0.05613... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
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0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25395 | [
"Documentation"
] | Initializing new random instances for each estimator instead of passing around the same one
### Describe the issue linked to the documentation
After reading the detailed and helpful section on ["Controlling randomness"](https://scikit-learn.org/stable/common_pitfalls.html#controlling-randomness), my understanding i... | 25,395 | [
-0.02420436590909958,
0.06623981893062592,
0.03894906863570213,
-0.0353429839015007,
-0.01703611947596073,
-0.012800917960703373,
0.08326617628335953,
-0.010000573471188545,
0.02809724397957325,
-0.029951894655823708,
0.05808991566300392,
0.0012582623166963458,
0.010074599646031857,
-0.039... |
https://github.com/scikit-learn/scikit-learn/issues/25389 | [
"Needs Triage"
] | ValueError: Number of features of the input must be equal to or greater than that of the fitted transformer. Transformer n_features is 5 and input n_features is 4.
i am making an machine learning model for a house price prediction project and when i click on predict price it shows
ValueError: Number of features of th... | 25,389 | [
-0.016518210992217064,
-0.02340948022902012,
0.012522883713245392,
-0.010392693802714348,
0.05883204564452171,
0.0002177747228415683,
0.06572144478559494,
-0.0017455720808357,
0.05872730910778046,
0.01222203578799963,
0.0073943487368524075,
0.013483426533639431,
0.01721334084868431,
0.1041... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25380 | [
"Bug",
"help wanted",
"Hard"
] | SVC and OneClassSVM fails to fit or have wrong fitted attributes with null sample weights
### Describe the bug
*SVC().fit(X, y, w)* fails when the targets *y* are multiclass and the sample_weights *w* zero out one of the classes.
* Dense *X* produces incorrect arrays for *support_*, *n_support_*, and *dual_coef_* at... | 25,380 | [
0.014064259827136993,
-0.017025195062160492,
0.056028325110673904,
0.04545212164521217,
0.12477084994316101,
-0.01643848419189453,
0.005769773852080107,
0.03743160888552666,
-0.0015495012048631907,
0.027768515050411224,
0.07598657160997391,
0.010954630561172962,
0.03650228679180145,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25365 | [
"Bug",
"Pandas compatibility"
] | sklearn.set_config(transform_output="pandas") breaks TSNE embeddings
### Describe the bug
TSNE doesn't work when the [global config is changed to pandas.](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html#global-configuration)
I tracked down this bug in the sklearn codebas... | 25,365 | [
-0.019655689597129822,
-0.018555212765932083,
0.011237770318984985,
0.04370666667819023,
0.07186837494373322,
0.03598174825310707,
0.07083269208669662,
-0.006518892478197813,
0.0082060182467103,
-0.036055147647857666,
-0.007719734683632851,
0.06594361364841461,
-0.0003980481706093997,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25364 | [
"Bug",
"module:neighbors"
] | Specifying 'cosine' as metric in KDTree throws error
### Describe the bug
I am trying to implement the `KDTree Algorithm` with `cosine` as a distance metric. I first started with [scipy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html), but it didn't support `cosine` ... | 25,364 | [
-0.006185722071677446,
-0.023828038945794106,
0.0018848819890990853,
-0.010918493382632732,
0.01888272352516651,
0.0008045375579968095,
0.041820771992206573,
0.0204290933907032,
-0.009370526298880577,
-0.05001269280910492,
0.01210723165422678,
0.017080504447221756,
0.01960580423474312,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25351 | [
"Documentation"
] | Missing visualization tool - sklearn-evaluation
### Describe the issue linked to the documentation
[This issue](https://github.com/scikit-learn/scikit-learn/pull/17112) was closed a while ago, removing some unmaintained tools. One of them was a tool to allow out-of-the-box visualizations.
My suggestion is to add... | 25,351 | [
-0.021676382049918175,
0.04406432807445526,
-0.010747065767645836,
-0.0392657034099102,
0.013410208746790886,
0.019068196415901184,
0.04257462918758392,
0.01832684502005577,
-0.0014605688629671931,
-0.006731190253049135,
-0.008342177607119083,
0.0972379669547081,
0.0029015038162469864,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25351 | [
"Documentation"
] | Missing visualization tool - sklearn-evaluation
### Describe the issue linked to the documentation
[This issue](https://github.com/scikit-learn/scikit-learn/pull/17112) was closed a while ago, removing some unmaintained tools. One of them was a tool to allow out-of-the-box visualizations.
My suggestion is to add... | 25,351 | [
-0.02509942278265953,
0.03793348744511604,
-0.01006996724754572,
-0.037146542221307755,
0.01886368729174137,
0.01323356106877327,
0.030295561999082565,
0.015363950282335281,
0.006665021646767855,
-0.006295479368418455,
-0.004015112761408091,
0.0946546196937561,
-0.00479417247697711,
0.0891... |
https://github.com/scikit-learn/scikit-learn/issues/25351 | [
"Documentation"
] | Missing visualization tool - sklearn-evaluation
### Describe the issue linked to the documentation
[This issue](https://github.com/scikit-learn/scikit-learn/pull/17112) was closed a while ago, removing some unmaintained tools. One of them was a tool to allow out-of-the-box visualizations.
My suggestion is to add... | 25,351 | [
-0.021259402856230736,
0.037958722561597824,
-0.010750330053269863,
-0.036037057638168335,
0.018763605505228043,
0.01380655262619257,
0.041174523532390594,
0.016334492713212967,
0.008618641644716263,
-0.00392059376463294,
-0.004855209495872259,
0.09730507433414459,
-0.00948451366275549,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25351 | [
"Documentation"
] | Missing visualization tool - sklearn-evaluation
### Describe the issue linked to the documentation
[This issue](https://github.com/scikit-learn/scikit-learn/pull/17112) was closed a while ago, removing some unmaintained tools. One of them was a tool to allow out-of-the-box visualizations.
My suggestion is to add... | 25,351 | [
-0.027187542989850044,
0.033262889832258224,
-0.005605545826256275,
-0.038459617644548416,
0.01844284124672413,
0.018175069242715836,
0.04261099174618721,
0.01898246631026268,
0.020401760935783386,
-0.007459355518221855,
-0.0163248423486948,
0.09891416132450104,
-0.006974603980779648,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/25351 | [
"Documentation"
] | Missing visualization tool - sklearn-evaluation
### Describe the issue linked to the documentation
[This issue](https://github.com/scikit-learn/scikit-learn/pull/17112) was closed a while ago, removing some unmaintained tools. One of them was a tool to allow out-of-the-box visualizations.
My suggestion is to add... | 25,351 | [
-0.025057608261704445,
0.03451327979564667,
-0.010000975802540779,
-0.03746234253048897,
0.019864508882164955,
0.01607341319322586,
0.03394044190645218,
0.01579580269753933,
0.011355023831129074,
-0.006127950735390186,
-0.008328489027917385,
0.09895266592502594,
-0.006614407990127802,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/25343 | [
"Documentation",
"Needs Triage"
] | Typo in contributing docs
### Describe the issue linked to the documentation
In a previous PR when `master` was changed to `main`, it also changed the word `master in` following line:
https://github.com/scikit-learn/scikit-learn/blame/e1ec3f99a3a91823d5923b8e894b8b8792206aab/doc/developers/contributing.rst#L1415... | 25,343 | [
0.07987572997808456,
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0.0017630131915211678,
-0.020007027313113213,
0.05317705124616623,
0.03485223650932312,
-0.037227995693683624,
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-0.011206432245671749,
0.055339887738227844,
0.005113699473440647,
0.03954795375466347,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25336 | [
"Bug",
"Needs Triage"
] | sklearn.neighbors.KernelDensity bandwith estimation with "scott" or "silverman" is showing TypeError
### Describe the bug
I am not able to use bandwith estimation techniques "scott" and "silverman" in KernelDensity estimation in sklearn.neighbors as shown in the documentation. It is throwing TypeError. It works only... | 25,336 | [
0.007285506464540958,
0.00006001084693707526,
0.009476774372160435,
-0.0020551569759845734,
0.08337678015232086,
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0.014390408992767334,
0.028845902532339096,
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-0.0022967569530010223,
0.0019774767570197582,
0.04226170480251312,
0.02494429610669613,
... |
https://github.com/scikit-learn/scikit-learn/issues/25333 | [
"Bug",
"module:ensemble",
"module:tree"
] | Read only buffer in cross_val_score with sparse matrix.
### Describe the bug
When calling `cross_val_score` with a sparse data matrix `X` and a `RandomForestClassifier` with `n_jobs=-1`, there is a weird interaction with joblib and memmapping that makes the buffer from `X` read-only, breaking the cython code for the ... | 25,333 | [
-0.033028364181518555,
-0.046833280473947525,
0.021393654868006706,
0.04808712378144264,
0.0616082064807415,
-0.026531988754868507,
-0.019372381269931793,
0.010236373171210289,
0.009249725379049778,
0.004778592847287655,
-0.024750450626015663,
0.012856424786150455,
0.035705577582120895,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25333 | [
"Bug",
"module:ensemble",
"module:tree"
] | Read only buffer in cross_val_score with sparse matrix.
### Describe the bug
When calling `cross_val_score` with a sparse data matrix `X` and a `RandomForestClassifier` with `n_jobs=-1`, there is a weird interaction with joblib and memmapping that makes the buffer from `X` read-only, breaking the cython code for the ... | 25,333 | [
-0.033028364181518555,
-0.046833280473947525,
0.021393654868006706,
0.04808712378144264,
0.0616082064807415,
-0.026531988754868507,
-0.019372381269931793,
0.010236373171210289,
0.009249725379049778,
0.004778592847287655,
-0.024750450626015663,
0.012856424786150455,
0.035705577582120895,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25333 | [
"Bug",
"module:ensemble",
"module:tree"
] | Read only buffer in cross_val_score with sparse matrix.
### Describe the bug
When calling `cross_val_score` with a sparse data matrix `X` and a `RandomForestClassifier` with `n_jobs=-1`, there is a weird interaction with joblib and memmapping that makes the buffer from `X` read-only, breaking the cython code for the ... | 25,333 | [
-0.033028364181518555,
-0.046833280473947525,
0.021393654868006706,
0.04808712378144264,
0.0616082064807415,
-0.026531988754868507,
-0.019372381269931793,
0.010236373171210289,
0.009249725379049778,
0.004778592847287655,
-0.024750450626015663,
0.012856424786150455,
0.035705577582120895,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25328 | [
"Bug",
"Needs Triage"
] | Gridsearch return nan as score value
### Describe the bug
I'm trying to train random forest regressor and Greadsearchcv in JupyterLab. Whenever I tried to tune the model, it return NaN as the scoring result.
> In Google Colab, the score value was returned.
When I set `error_score='raise'`, I have the following... | 25,328 | [
-0.011040681041777134,
-0.017841774970293045,
0.04872605577111244,
0.007125906180590391,
0.09865105897188187,
-0.016746751964092255,
-0.018287617713212967,
0.03732117637991905,
0.00990472361445427,
-0.003399995621293783,
-0.027746567502617836,
0.03177657723426819,
0.017497409135103226,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25322 | [
"Build / CI"
] | Increase minimum Cython version to 0.29.33
Require minimum Cython version >= 0.29.33 as from this version on Cython supports `const` fused types with memory views, see release notes https://cython.readthedocs.io/en/latest/src/changes.html#id30 and the related issue #10624.
COMMENT:
I am highly favorable of requiring ... | 25,322 | [
-0.05027831345796585,
0.034896161407232285,
-0.03400704637169838,
-0.04442622885107994,
0.014348367229104042,
0.003865933045744896,
-0.013181998394429684,
0.015822673216462135,
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-0.05615358427166939,
0.048079393804073334,
0.003289399202913046,
0.005551905836910009,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25322 | [
"Build / CI"
] | Increase minimum Cython version to 0.29.33
Require minimum Cython version >= 0.29.33 as from this version on Cython supports `const` fused types with memory views, see release notes https://cython.readthedocs.io/en/latest/src/changes.html#id30 and the related issue #10624.
COMMENT:
I am +1 on setting the minimum vers... | 25,322 | [
-0.026533493772149086,
0.03562188148498535,
-0.026207827031612396,
-0.029313456267118454,
0.036046821624040604,
0.027600035071372986,
0.023172549903392792,
0.025548655539751053,
0.03242184966802597,
-0.0453731007874012,
0.05450250953435898,
0.04348790645599365,
-0.019675809890031815,
0.025... |
https://github.com/scikit-learn/scikit-learn/issues/25322 | [
"Build / CI"
] | Increase minimum Cython version to 0.29.33
Require minimum Cython version >= 0.29.33 as from this version on Cython supports `const` fused types with memory views, see release notes https://cython.readthedocs.io/en/latest/src/changes.html#id30 and the related issue #10624.
COMMENT:
https://github.com/scikit-learn/sci... | 25,322 | [
-0.019499031826853752,
-0.0015544375637546182,
-0.008686017245054245,
-0.01027852576225996,
0.0658285841345787,
0.010004435665905476,
-0.002706293947994709,
0.021084768697619438,
0.01814231649041176,
-0.056952014565467834,
0.03742349147796631,
0.017841756343841553,
-0.007183781825006008,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25319 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values
### Describe the bug
[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/skl... | 25,319 | [
-0.010815514251589775,
0.015040501952171326,
0.02940959669649601,
-0.01306899730116129,
0.03250414878129959,
0.0006423505838029087,
0.05849962309002876,
0.019059184938669205,
-0.007673292420804501,
-0.019343983381986618,
0.000468868063762784,
-0.02691369131207466,
-0.023524517193436623,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25311 | [
"Bug"
] | Inconsistency between liac-arff and pandas parser in fetch_openml
From https://github.com/fairlearn/fairlearn/pull/1166, we have an inconsistency between liac-arff and pandas parser.
From the ARFF specs, the leading whitespaces are ignored if not between quotes. The pandas `read_csv` will include this space by defa... | 25,311 | [
0.0757492333650589,
0.026458321139216423,
-0.00032988839666359127,
0.03373879939317703,
0.10321693122386932,
0.018470803275704384,
0.019996868446469307,
0.0023412362206727266,
-0.008486972190439701,
-0.030054597184062004,
0.0013536661863327026,
0.03492886945605278,
0.011330382898449898,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/25310 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/3889482380)** (Jan 11, 2023)
COMMENT:
Apparently, it should be linked with https://github.com/scikit-learn/scikit-learn/pull/25062 where we start to raise an error if the `ConvergenceWar... | 25,310 | [
-0.08532726019620895,
0.02778490073978901,
-0.027408620342612267,
-0.027455322444438934,
0.009605593979358673,
0.025041082873940468,
-0.011875350959599018,
0.03616510331630707,
-0.008240984752774239,
0.04992487281560898,
0.10478108376264572,
0.01865733042359352,
-0.006463303696364164,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/25301 | [
"Bug",
"Needs Triage"
] | Bisecting Kmeans predict and transform.argmin do not give same results
### Describe the bug
The bisecting KMeans algorithm does not give the same result when using `.predict(X)` and `.transform(X).argmin(1)`.
I'm not sure what the desired output would be, and whether they should be equal. This, however, does go... | 25,301 | [
0.019649440422654152,
-0.05445015802979469,
-0.017899343743920326,
0.022339612245559692,
0.03506452590227127,
-0.046803709119558334,
0.02261938340961933,
0.030080199241638184,
-0.0226620864123106,
-0.0023414595052599907,
0.014078160747885704,
0.02754565328359604,
0.022602321580052376,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25301 | [
"Bug",
"Needs Triage"
] | Bisecting Kmeans predict and transform.argmin do not give same results
### Describe the bug
The bisecting KMeans algorithm does not give the same result when using `.predict(X)` and `.transform(X).argmin(1)`.
I'm not sure what the desired output would be, and whether they should be equal. This, however, does go... | 25,301 | [
0.019649440422654152,
-0.05445015802979469,
-0.017899343743920326,
0.022339612245559692,
0.03506452590227127,
-0.046803709119558334,
0.02261938340961933,
0.030080199241638184,
-0.0226620864123106,
-0.0023414595052599907,
0.014078160747885704,
0.02754565328359604,
0.022602321580052376,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25298 | [
"Bug",
"Needs Triage"
] | RMSE using mean_squared_error does not return the correct value
### Describe the bug
The RMSE value obtained from mean_squared_error function with the squared parameter set to false return a different value compared to manually root the MSE obtained by the same function
### Steps/Code to Reproduce
```shell
f... | 25,298 | [
0.028012119233608246,
-0.07207286357879639,
0.0474233515560627,
0.016471771523356438,
0.06927522271871567,
-0.0012256293557584286,
0.011223996989428997,
0.024744780734181404,
0.009830765426158905,
-0.03001374378800392,
-0.01613580621778965,
-0.0042311158031225204,
0.03738223388791084,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25298 | [
"Bug",
"Needs Triage"
] | RMSE using mean_squared_error does not return the correct value
### Describe the bug
The RMSE value obtained from mean_squared_error function with the squared parameter set to false return a different value compared to manually root the MSE obtained by the same function
### Steps/Code to Reproduce
```shell
f... | 25,298 | [
0.028012119233608246,
-0.07207286357879639,
0.0474233515560627,
0.016471771523356438,
0.06927522271871567,
-0.0012256293557584286,
0.011223996989428997,
0.024744780734181404,
0.009830765426158905,
-0.03001374378800392,
-0.01613580621778965,
-0.0042311158031225204,
0.03738223388791084,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25298 | [
"Bug",
"Needs Triage"
] | RMSE using mean_squared_error does not return the correct value
### Describe the bug
The RMSE value obtained from mean_squared_error function with the squared parameter set to false return a different value compared to manually root the MSE obtained by the same function
### Steps/Code to Reproduce
```shell
f... | 25,298 | [
0.028012119233608246,
-0.07207286357879639,
0.0474233515560627,
0.016471771523356438,
0.06927522271871567,
-0.0012256293557584286,
0.011223996989428997,
0.024744780734181404,
0.009830765426158905,
-0.03001374378800392,
-0.01613580621778965,
-0.0042311158031225204,
0.03738223388791084,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25293 | [
"Bug"
] | _SetOutputMixin changes default order of inheritance
### Describe the bug
Inheriting from `TransformerMixin` now implicitly adds the wrapped `transform` method of superclass to subclasses and as a consequence can change the order in which multiple inheritance is resolved.
This is caused by the `_SetOutputMixin.__in... | 25,293 | [
0.0267962459474802,
0.008790520019829273,
0.01631767302751541,
0.043767135590314865,
0.044837016612291336,
0.01368087437003851,
0.03765394911170006,
0.0022590963635593653,
-0.07488293200731277,
-0.04922957718372345,
0.013356519863009453,
0.0211578868329525,
0.015792854130268097,
-0.0238809... |
https://github.com/scikit-learn/scikit-learn/issues/25293 | [
"Bug"
] | _SetOutputMixin changes default order of inheritance
### Describe the bug
Inheriting from `TransformerMixin` now implicitly adds the wrapped `transform` method of superclass to subclasses and as a consequence can change the order in which multiple inheritance is resolved.
This is caused by the `_SetOutputMixin.__in... | 25,293 | [
0.0267962459474802,
0.008790520019829273,
0.01631767302751541,
0.043767135590314865,
0.044837016612291336,
0.01368087437003851,
0.03765394911170006,
0.0022590963635593653,
-0.07488293200731277,
-0.04922957718372345,
0.013356519863009453,
0.0211578868329525,
0.015792854130268097,
-0.0238809... |
https://github.com/scikit-learn/scikit-learn/issues/25292 | [
"Bug",
"module:preprocessing"
] | get_feature_names_out not working on periodic SplineTransformers
### Describe the bug
When using a SplineTransformer with argument `extrapolation="periodic"` there seems to be a disagreement with the number of columns of the transformed features and the names returned by `get_feature_names_out`
### Steps/Code to Rep... | 25,292 | [
0.040510863065719604,
0.02001006156206131,
0.00031175572075881064,
0.009111432358622551,
0.027666345238685608,
-0.004350819159299135,
0.08351621776819229,
-0.009852861054241657,
-0.03243650868535042,
0.04231514409184456,
0.06607069075107574,
0.011633451096713543,
0.0581304170191288,
0.0155... |
https://github.com/scikit-learn/scikit-learn/issues/25292 | [
"Bug",
"module:preprocessing"
] | get_feature_names_out not working on periodic SplineTransformers
### Describe the bug
When using a SplineTransformer with argument `extrapolation="periodic"` there seems to be a disagreement with the number of columns of the transformed features and the names returned by `get_feature_names_out`
### Steps/Code to Rep... | 25,292 | [
0.040510863065719604,
0.02001006156206131,
0.00031175572075881064,
0.009111432358622551,
0.027666345238685608,
-0.004350819159299135,
0.08351621776819229,
-0.009852861054241657,
-0.03243650868535042,
0.04231514409184456,
0.06607069075107574,
0.011633451096713543,
0.0581304170191288,
0.0155... |
https://github.com/scikit-learn/scikit-learn/issues/25287 | [
"Documentation"
] | `transform_output` set in `config_context` not preserved in the Transformer object?
### Describe the bug
This is related to: https://github.com/scikit-learn/scikit-learn/pull/23734 (btw I love this enhancement!), when `config_context` is used the Transformers created within the context do not register/memoize the t... | 25,287 | [
-0.06728053838014603,
-0.017253465950489044,
0.03479259833693504,
0.00005711133053409867,
0.04807646945118904,
-0.013568524271249771,
0.09494656324386597,
-0.0041529289446771145,
-0.05196221545338631,
0.006628004368394613,
-0.015826940536499023,
0.045640066266059875,
0.010715550743043423,
... |
https://github.com/scikit-learn/scikit-learn/issues/25287 | [
"Documentation"
] | `transform_output` set in `config_context` not preserved in the Transformer object?
### Describe the bug
This is related to: https://github.com/scikit-learn/scikit-learn/pull/23734 (btw I love this enhancement!), when `config_context` is used the Transformers created within the context do not register/memoize the t... | 25,287 | [
-0.06728053838014603,
-0.017253465950489044,
0.03479259833693504,
0.00005711133053409867,
0.04807646945118904,
-0.013568524271249771,
0.09494656324386597,
-0.0041529289446771145,
-0.05196221545338631,
0.006628004368394613,
-0.015826940536499023,
0.045640066266059875,
0.010715550743043423,
... |
https://github.com/scikit-learn/scikit-learn/issues/25287 | [
"Documentation"
] | `transform_output` set in `config_context` not preserved in the Transformer object?
### Describe the bug
This is related to: https://github.com/scikit-learn/scikit-learn/pull/23734 (btw I love this enhancement!), when `config_context` is used the Transformers created within the context do not register/memoize the t... | 25,287 | [
-0.06728053838014603,
-0.017253465950489044,
0.03479259833693504,
0.00005711133053409867,
0.04807646945118904,
-0.013568524271249771,
0.09494656324386597,
-0.0041529289446771145,
-0.05196221545338631,
0.006628004368394613,
-0.015826940536499023,
0.045640066266059875,
0.010715550743043423,
... |
https://github.com/scikit-learn/scikit-learn/issues/25287 | [
"Documentation"
] | `transform_output` set in `config_context` not preserved in the Transformer object?
### Describe the bug
This is related to: https://github.com/scikit-learn/scikit-learn/pull/23734 (btw I love this enhancement!), when `config_context` is used the Transformers created within the context do not register/memoize the t... | 25,287 | [
-0.06728053838014603,
-0.017253465950489044,
0.03479259833693504,
0.00005711133053409867,
0.04807646945118904,
-0.013568524271249771,
0.09494656324386597,
-0.0041529289446771145,
-0.05196221545338631,
0.006628004368394613,
-0.015826940536499023,
0.045640066266059875,
0.010715550743043423,
... |
https://github.com/scikit-learn/scikit-learn/issues/25287 | [
"Documentation"
] | `transform_output` set in `config_context` not preserved in the Transformer object?
### Describe the bug
This is related to: https://github.com/scikit-learn/scikit-learn/pull/23734 (btw I love this enhancement!), when `config_context` is used the Transformers created within the context do not register/memoize the t... | 25,287 | [
-0.06728053838014603,
-0.017253465950489044,
0.03479259833693504,
0.00005711133053409867,
0.04807646945118904,
-0.013568524271249771,
0.09494656324386597,
-0.0041529289446771145,
-0.05196221545338631,
0.006628004368394613,
-0.015826940536499023,
0.045640066266059875,
0.010715550743043423,
... |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 | [
0.005759113002568483,
0.0642445832490921,
0.022947151213884354,
-0.05373499542474747,
0.025978364050388336,
0.0063086338341236115,
0.044346537441015244,
0.0449284203350544,
0.09699950367212296,
-0.011559265665709972,
0.08395181596279144,
0.08265455067157745,
-0.006625216919928789,
0.070664... |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 | [
0.0034222882241010666,
0.05361369624733925,
0.02084525115787983,
-0.030995067209005356,
0.010016806423664093,
-0.008903825655579567,
0.028381522744894028,
0.0313599668443203,
0.08600218594074249,
-0.005475285928696394,
0.05963767692446709,
0.09389529377222061,
0.007211090996861458,
0.05088... |
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