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/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25950 | [
"Bug",
"Needs Triage"
] | Lasso loss is (mathematically) invariant wrt simultaneous rotation of X and y, but the solver outcome is not
### Describe the bug
For any given set of coefficients and intercept, the Lasso loss is invariant with respect to replacing X and y with U@X and U@y, where U is any orthonormal matrix (proof see below). Howeve... | 25,950 | [
0.010071882978081703,
0.010290028527379036,
0.03734659031033516,
0.0022443803027272224,
0.06073809042572975,
0.020263275131583214,
0.00812505092471838,
0.02081388421356678,
-0.013913219794631004,
0.015021377243101597,
-0.0026477037463337183,
0.06389746069908142,
0.054209232330322266,
-0.04... |
https://github.com/scikit-learn/scikit-learn/issues/25946 | [
"Bug",
"Needs Triage"
] | Output redirection is broken
### Describe the bug
I'm trying to redirect output from a script that uses Scikit-Learn to a file, as follows:
`python myscript.py > mylog.log 2>&1`
This should redirect both stdout and stderr to _mylog.log_. Most of my code uses logging statements which, as expected, are written to... | 25,946 | [
0.029033837839961052,
-0.00220193387940526,
0.0207353588193655,
-0.04919305816292763,
0.089673712849617,
-0.007333251181989908,
0.021665120497345924,
0.047176312655210495,
0.03212379664182663,
-0.06932328641414642,
-0.008796017616987228,
0.027784079313278198,
-0.028748173266649246,
0.05705... |
https://github.com/scikit-learn/scikit-learn/issues/25946 | [
"Bug",
"Needs Triage"
] | Output redirection is broken
### Describe the bug
I'm trying to redirect output from a script that uses Scikit-Learn to a file, as follows:
`python myscript.py > mylog.log 2>&1`
This should redirect both stdout and stderr to _mylog.log_. Most of my code uses logging statements which, as expected, are written to... | 25,946 | [
0.029033837839961052,
-0.00220193387940526,
0.0207353588193655,
-0.04919305816292763,
0.089673712849617,
-0.007333251181989908,
0.021665120497345924,
0.047176312655210495,
0.03212379664182663,
-0.06932328641414642,
-0.008796017616987228,
0.027784079313278198,
-0.028748173266649246,
0.05705... |
https://github.com/scikit-learn/scikit-learn/issues/25946 | [
"Bug",
"Needs Triage"
] | Output redirection is broken
### Describe the bug
I'm trying to redirect output from a script that uses Scikit-Learn to a file, as follows:
`python myscript.py > mylog.log 2>&1`
This should redirect both stdout and stderr to _mylog.log_. Most of my code uses logging statements which, as expected, are written to... | 25,946 | [
0.029033837839961052,
-0.00220193387940526,
0.0207353588193655,
-0.04919305816292763,
0.089673712849617,
-0.007333251181989908,
0.021665120497345924,
0.047176312655210495,
0.03212379664182663,
-0.06932328641414642,
-0.008796017616987228,
0.027784079313278198,
-0.028748173266649246,
0.05705... |
https://github.com/scikit-learn/scikit-learn/issues/25946 | [
"Bug",
"Needs Triage"
] | Output redirection is broken
### Describe the bug
I'm trying to redirect output from a script that uses Scikit-Learn to a file, as follows:
`python myscript.py > mylog.log 2>&1`
This should redirect both stdout and stderr to _mylog.log_. Most of my code uses logging statements which, as expected, are written to... | 25,946 | [
0.029033837839961052,
-0.00220193387940526,
0.0207353588193655,
-0.04919305816292763,
0.089673712849617,
-0.007333251181989908,
0.021665120497345924,
0.047176312655210495,
0.03212379664182663,
-0.06932328641414642,
-0.008796017616987228,
0.027784079313278198,
-0.028748173266649246,
0.05705... |
https://github.com/scikit-learn/scikit-learn/issues/25944 | [
"Bug"
] | Polynomial features min degree and max degree not working properly
### Describe the bug
I'm trying to use the PolynomialFeatures to generate 2nd order terms and exclude linear ones. According to the documentation, this should look like this:
`poly = PolynomialFeatures(degree=(2,2),interaction_only=True)`
But ... | 25,944 | [
-0.0021005815360695124,
-0.024626804515719414,
0.02772074192762375,
0.05058734863996506,
0.08707188814878464,
-0.03637781739234924,
0.05818430706858635,
0.005910536739975214,
0.09500020742416382,
-0.01918892003595829,
0.05523138493299484,
0.030357567593455315,
0.02312564291059971,
0.038209... |
https://github.com/scikit-learn/scikit-learn/issues/25944 | [
"Bug"
] | Polynomial features min degree and max degree not working properly
### Describe the bug
I'm trying to use the PolynomialFeatures to generate 2nd order terms and exclude linear ones. According to the documentation, this should look like this:
`poly = PolynomialFeatures(degree=(2,2),interaction_only=True)`
But ... | 25,944 | [
-0.0021005815360695124,
-0.024626804515719414,
0.02772074192762375,
0.05058734863996506,
0.08707188814878464,
-0.03637781739234924,
0.05818430706858635,
0.005910536739975214,
0.09500020742416382,
-0.01918892003595829,
0.05523138493299484,
0.030357567593455315,
0.02312564291059971,
0.038209... |
https://github.com/scikit-learn/scikit-learn/issues/25937 | [
"Needs Triage"
] | Unstable sklearn/svm/tests/test_bounds.py::test_newrand_set_seed
On my local laptop I often observe a random failure for `test_newrand_set_seed`.
I am running macos m1 with Python 3.11 from conda-forge. scikit-learn has been built with the clang compilers from conda-forge.
Here is a typical run with 10 repetitio... | 25,937 | [
-0.030376136302947998,
-0.026769524440169334,
-0.013700168579816818,
-0.020556705072522163,
0.07914097607135773,
-0.02120557427406311,
-0.014223463833332062,
0.05357622355222702,
-0.04950695112347603,
-0.0183509960770607,
0.06709936261177063,
0.048747725784778595,
-0.037660811096429825,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25935 | [
"Bug"
] | BaggingClassifier throws ValueError: WRITEBACKIFCOPY base is read-only
### Describe the bug
When I use the bagging-classifier in conjunction with LinearSVC it throws `ValueError: WRITEBACKIFCOPY base is read-only` when `n_jobs!=1`.
Changing `n_jobs` to 1 removes the error
### Steps/Code to Reproduce
The i... | 25,935 | [
-0.00918820034712553,
0.008624842390418053,
0.0249063428491354,
0.04210781306028366,
0.038633204996585846,
-0.011294079013168812,
0.022044675424695015,
0.05011015757918358,
-0.017908237874507904,
-0.0062413825653493404,
0.026856135576963425,
0.042740847915410995,
-0.04025810956954956,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/25933 | [
"Bug"
] | Potentially unsafe cast in sklearn.utils.multiclass.type_of_target
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25835
<div type='discussions-op-text'>
<sup>Originally posted by **thomasryck** March 13, 2023</sup>
Hi
I was using the method type_of_target from the multiclass file.
... | 25,933 | [
-0.037450384348630905,
0.031882934272289276,
0.00628760876134038,
0.04097074642777443,
0.06458954513072968,
0.0059434506110847,
0.024963397532701492,
0.01786094531416893,
-0.04900941252708435,
-0.04912266880273819,
-0.0039077517576515675,
0.016210950911045074,
-0.01835482195019722,
-0.0144... |
https://github.com/scikit-learn/scikit-learn/issues/25933 | [
"Bug"
] | Potentially unsafe cast in sklearn.utils.multiclass.type_of_target
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25835
<div type='discussions-op-text'>
<sup>Originally posted by **thomasryck** March 13, 2023</sup>
Hi
I was using the method type_of_target from the multiclass file.
... | 25,933 | [
-0.04450836405158043,
0.019431641325354576,
0.0009268139256164432,
0.04552082717418671,
0.06677035242319107,
-0.0002461760595906526,
0.033889833837747574,
0.02605561539530754,
-0.058325063437223434,
-0.04706037789583206,
0.0007139520603232086,
0.0123050007969141,
-0.021755078807473183,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/25933 | [
"Bug"
] | Potentially unsafe cast in sklearn.utils.multiclass.type_of_target
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25835
<div type='discussions-op-text'>
<sup>Originally posted by **thomasryck** March 13, 2023</sup>
Hi
I was using the method type_of_target from the multiclass file.
... | 25,933 | [
-0.032704658806324005,
0.02690352126955986,
0.005778134800493717,
0.032252971082925797,
0.06800714135169983,
0.000865419686306268,
0.028249502182006836,
0.02734738029539585,
-0.05988343432545662,
-0.0447908379137516,
-0.004144174046814442,
0.01316354051232338,
-0.026989441365003586,
-0.025... |
https://github.com/scikit-learn/scikit-learn/issues/25933 | [
"Bug"
] | Potentially unsafe cast in sklearn.utils.multiclass.type_of_target
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25835
<div type='discussions-op-text'>
<sup>Originally posted by **thomasryck** March 13, 2023</sup>
Hi
I was using the method type_of_target from the multiclass file.
... | 25,933 | [
-0.03632379323244095,
0.012094140984117985,
0.003739232663065195,
0.03630712255835533,
0.06796234101057053,
0.002660807454958558,
0.03320310264825821,
0.027613062411546707,
-0.06034038960933685,
-0.0430292934179306,
0.006635064259171486,
0.018939556553959846,
-0.019905289635062218,
-0.0135... |
https://github.com/scikit-learn/scikit-learn/issues/25933 | [
"Bug"
] | Potentially unsafe cast in sklearn.utils.multiclass.type_of_target
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25835
<div type='discussions-op-text'>
<sup>Originally posted by **thomasryck** March 13, 2023</sup>
Hi
I was using the method type_of_target from the multiclass file.
... | 25,933 | [
-0.03035895526409149,
0.026372915133833885,
0.011521929875016212,
0.03488417714834213,
0.06490763276815414,
0.005823326297104359,
0.040483660995960236,
0.021977011114358902,
-0.05155913159251213,
-0.04860512539744377,
0.0017106943996623158,
0.013495421968400478,
-0.012096396647393703,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25932 | [
"Bug",
"Needs Info"
] | Random forest classifier probability is negative
### Describe the bug
Random forest classifier probability is negative value
### Steps/Code to Reproduce
```py
import numpy as np
clf = RandomForestClassifier(n_estimators=500, max_depth=50, random_state=0, min_samples_split = 10, min_samples_leaf = 10)
clf... | 25,932 | [
-0.012864070013165474,
-0.0658799484372139,
0.028387773782014847,
-0.009055000729858875,
0.07160171121358871,
-0.04980260506272316,
-0.04328232258558273,
-0.009246725589036942,
-0.0037258132360875607,
-0.021233411505818367,
0.02510656975209713,
-0.01160860899835825,
0.02729300782084465,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25932 | [
"Bug",
"Needs Info"
] | Random forest classifier probability is negative
### Describe the bug
Random forest classifier probability is negative value
### Steps/Code to Reproduce
```py
import numpy as np
clf = RandomForestClassifier(n_estimators=500, max_depth=50, random_state=0, min_samples_split = 10, min_samples_leaf = 10)
clf... | 25,932 | [
-0.012864070013165474,
-0.0658799484372139,
0.028387773782014847,
-0.009055000729858875,
0.07160171121358871,
-0.04980260506272316,
-0.04328232258558273,
-0.009246725589036942,
-0.0037258132360875607,
-0.021233411505818367,
0.02510656975209713,
-0.01160860899835825,
0.02729300782084465,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25932 | [
"Bug",
"Needs Info"
] | Random forest classifier probability is negative
### Describe the bug
Random forest classifier probability is negative value
### Steps/Code to Reproduce
```py
import numpy as np
clf = RandomForestClassifier(n_estimators=500, max_depth=50, random_state=0, min_samples_split = 10, min_samples_leaf = 10)
clf... | 25,932 | [
-0.012864070013165474,
-0.0658799484372139,
0.028387773782014847,
-0.009055000729858875,
0.07160171121358871,
-0.04980260506272316,
-0.04328232258558273,
-0.009246725589036942,
-0.0037258132360875607,
-0.021233411505818367,
0.02510656975209713,
-0.01160860899835825,
0.02729300782084465,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25932 | [
"Bug",
"Needs Info"
] | Random forest classifier probability is negative
### Describe the bug
Random forest classifier probability is negative value
### Steps/Code to Reproduce
```py
import numpy as np
clf = RandomForestClassifier(n_estimators=500, max_depth=50, random_state=0, min_samples_split = 10, min_samples_leaf = 10)
clf... | 25,932 | [
-0.012864070013165474,
-0.0658799484372139,
0.028387773782014847,
-0.009055000729858875,
0.07160171121358871,
-0.04980260506272316,
-0.04328232258558273,
-0.009246725589036942,
-0.0037258132360875607,
-0.021233411505818367,
0.02510656975209713,
-0.01160860899835825,
0.02729300782084465,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25929 | [
"Enhancement"
] | Visual improvements for ROC and precision-recall plots
### Describe the workflow you want to enable
Hello,
I would like to suggest the following improvements:
- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top... | 25,929 | [
-0.04340018332004547,
-0.005339865107089281,
-0.030178997665643692,
0.01671769842505455,
0.012057124637067318,
-0.019486404955387115,
0.025634611025452614,
0.04707639664411545,
-0.021579846739768982,
0.010748066939413548,
-0.02207319065928459,
0.05941232666373253,
-0.017879992723464966,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25923 | [
"Bug"
] | average_precision_score() and roc_auc_score() require a full list of arguments
### Describe the bug
These two functions, namely average_precision_score() and roc_auc_score() require a full list of arguments. Otherwise, it reports errors!
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.metrics ... | 25,923 | [
0.02569718472659588,
-0.03102211281657219,
0.01852594129741192,
-0.014727473258972168,
0.10354693233966827,
0.02658744715154171,
0.03571977838873863,
-0.016638001427054405,
0.02393491193652153,
-0.025522038340568542,
0.017129864543676376,
0.00946484599262476,
0.04164399951696396,
0.0054486... |
https://github.com/scikit-learn/scikit-learn/issues/25923 | [
"Bug"
] | average_precision_score() and roc_auc_score() require a full list of arguments
### Describe the bug
These two functions, namely average_precision_score() and roc_auc_score() require a full list of arguments. Otherwise, it reports errors!
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.metrics ... | 25,923 | [
0.02569718472659588,
-0.03102211281657219,
0.01852594129741192,
-0.014727473258972168,
0.10354693233966827,
0.02658744715154171,
0.03571977838873863,
-0.016638001427054405,
0.02393491193652153,
-0.025522038340568542,
0.017129864543676376,
0.00946484599262476,
0.04164399951696396,
0.0054486... |
https://github.com/scikit-learn/scikit-learn/issues/25922 | [
"New Feature",
"Needs Decision - Include Feature"
] | Make complex numbers check optional in `BaseEstimator._validate_and_set_fit_params`
### Describe the workflow you want to enable
I recently extended my library [PySR](https://github.com/MilesCranmer/PySR) to allow for complex `X` and `y` arrays. However, the function `BaseEstimator._validate_and_set_fit_params` has... | 25,922 | [
-0.03670469671487808,
0.06559967994689941,
0.03760092332959175,
-0.03897392377257347,
0.09533125907182693,
0.013861490413546562,
0.05099492892622948,
0.011881424114108086,
-0.004871272947639227,
0.005153769627213478,
0.022011689841747284,
0.003242124803364277,
-0.022194430232048035,
0.0003... |
https://github.com/scikit-learn/scikit-learn/issues/25922 | [
"New Feature",
"Needs Decision - Include Feature"
] | Make complex numbers check optional in `BaseEstimator._validate_and_set_fit_params`
### Describe the workflow you want to enable
I recently extended my library [PySR](https://github.com/MilesCranmer/PySR) to allow for complex `X` and `y` arrays. However, the function `BaseEstimator._validate_and_set_fit_params` has... | 25,922 | [
-0.03670469671487808,
0.06559967994689941,
0.03760092332959175,
-0.03897392377257347,
0.09533125907182693,
0.013861490413546562,
0.05099492892622948,
0.011881424114108086,
-0.004871272947639227,
0.005153769627213478,
0.022011689841747284,
0.003242124803364277,
-0.022194430232048035,
0.0003... |
https://github.com/scikit-learn/scikit-learn/issues/25922 | [
"New Feature",
"Needs Decision - Include Feature"
] | Make complex numbers check optional in `BaseEstimator._validate_and_set_fit_params`
### Describe the workflow you want to enable
I recently extended my library [PySR](https://github.com/MilesCranmer/PySR) to allow for complex `X` and `y` arrays. However, the function `BaseEstimator._validate_and_set_fit_params` has... | 25,922 | [
-0.03670469671487808,
0.06559967994689941,
0.03760092332959175,
-0.03897392377257347,
0.09533125907182693,
0.013861490413546562,
0.05099492892622948,
0.011881424114108086,
-0.004871272947639227,
0.005153769627213478,
0.022011689841747284,
0.003242124803364277,
-0.022194430232048035,
0.0003... |
https://github.com/scikit-learn/scikit-learn/issues/25922 | [
"New Feature",
"Needs Decision - Include Feature"
] | Make complex numbers check optional in `BaseEstimator._validate_and_set_fit_params`
### Describe the workflow you want to enable
I recently extended my library [PySR](https://github.com/MilesCranmer/PySR) to allow for complex `X` and `y` arrays. However, the function `BaseEstimator._validate_and_set_fit_params` has... | 25,922 | [
-0.03670469671487808,
0.06559967994689941,
0.03760092332959175,
-0.03897392377257347,
0.09533125907182693,
0.013861490413546562,
0.05099492892622948,
0.011881424114108086,
-0.004871272947639227,
0.005153769627213478,
0.022011689841747284,
0.003242124803364277,
-0.022194430232048035,
0.0003... |
https://github.com/scikit-learn/scikit-learn/issues/25922 | [
"New Feature",
"Needs Decision - Include Feature"
] | Make complex numbers check optional in `BaseEstimator._validate_and_set_fit_params`
### Describe the workflow you want to enable
I recently extended my library [PySR](https://github.com/MilesCranmer/PySR) to allow for complex `X` and `y` arrays. However, the function `BaseEstimator._validate_and_set_fit_params` has... | 25,922 | [
-0.03670469671487808,
0.06559967994689941,
0.03760092332959175,
-0.03897392377257347,
0.09533125907182693,
0.013861490413546562,
0.05099492892622948,
0.011881424114108086,
-0.004871272947639227,
0.005153769627213478,
0.022011689841747284,
0.003242124803364277,
-0.022194430232048035,
0.0003... |
https://github.com/scikit-learn/scikit-learn/issues/25908 | [
"Needs Triage"
] | make_classification: variances of informative, redundant and useless features differ significantly
In this part of the code for `make_classification`, the informative and redundant features are generated; here is the informative part:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440c... | 25,908 | [
0.0016570803709328175,
0.03404662385582924,
0.006323433946818113,
0.032684583216905594,
0.04450378566980362,
0.042849455028772354,
0.029540564864873886,
-0.008343718014657497,
-0.046203821897506714,
0.011798840947449207,
0.019705092534422874,
0.07616271823644638,
0.08593503385782242,
0.018... |
https://github.com/scikit-learn/scikit-learn/issues/25908 | [
"Needs Triage"
] | make_classification: variances of informative, redundant and useless features differ significantly
In this part of the code for `make_classification`, the informative and redundant features are generated; here is the informative part:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440c... | 25,908 | [
0.0016570803709328175,
0.03404662385582924,
0.006323433946818113,
0.032684583216905594,
0.04450378566980362,
0.042849455028772354,
0.029540564864873886,
-0.008343718014657497,
-0.046203821897506714,
0.011798840947449207,
0.019705092534422874,
0.07616271823644638,
0.08593503385782242,
0.018... |
https://github.com/scikit-learn/scikit-learn/issues/25908 | [
"Needs Triage"
] | make_classification: variances of informative, redundant and useless features differ significantly
In this part of the code for `make_classification`, the informative and redundant features are generated; here is the informative part:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440c... | 25,908 | [
0.0016570803709328175,
0.03404662385582924,
0.006323433946818113,
0.032684583216905594,
0.04450378566980362,
0.042849455028772354,
0.029540564864873886,
-0.008343718014657497,
-0.046203821897506714,
0.011798840947449207,
0.019705092534422874,
0.07616271823644638,
0.08593503385782242,
0.018... |
https://github.com/scikit-learn/scikit-learn/issues/25908 | [
"Needs Triage"
] | make_classification: variances of informative, redundant and useless features differ significantly
In this part of the code for `make_classification`, the informative and redundant features are generated; here is the informative part:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440c... | 25,908 | [
0.0016570803709328175,
0.03404662385582924,
0.006323433946818113,
0.032684583216905594,
0.04450378566980362,
0.042849455028772354,
0.029540564864873886,
-0.008343718014657497,
-0.046203821897506714,
0.011798840947449207,
0.019705092534422874,
0.07616271823644638,
0.08593503385782242,
0.018... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.005343723576515913,
0.04240700602531433,
0.03684725984930992,
-0.03583727031946182,
0.0276692733168602,
-0.002495619934052229,
0.0071788509376347065,
0.011770006269216537,
-0.0027593893464654684,
-0.0041532013565301895,
0.07725375890731812,
0.04132777452468872,
-0.025539344176650047,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.011971392668783665,
0.04492352157831192,
0.04541081562638283,
-0.016610227525234222,
0.04646177589893341,
-0.020445911213755608,
-0.025214768946170807,
0.01458633504807949,
-0.005463874898850918,
-0.010218216106295586,
0.06092994287610054,
0.04167809709906578,
-0.022155512124300003,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
-0.0005341694923117757,
0.046166885644197464,
0.03575065732002258,
-0.03288803994655609,
0.035298459231853485,
-0.00040658083162270486,
0.005435256287455559,
0.01766938529908657,
-0.0035764481872320175,
-0.0020478663500398397,
0.07373296469449997,
0.04173204302787781,
-0.02490929886698723,
... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
-0.005988561548292637,
0.029451612383127213,
0.042449358850717545,
-0.028834953904151917,
0.037983350455760956,
-0.006251384504139423,
-0.0008048599702306092,
0.010515348985791206,
0.026934459805488586,
-0.004667190369218588,
0.059571318328380585,
0.03596443682909012,
-0.011410647071897984,
... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.012693808414041996,
0.04778540879487991,
0.04158155620098114,
-0.03523804247379303,
0.024345261976122856,
-0.004878563340753317,
-0.0037984184455126524,
0.005151662509888411,
-0.007866467349231243,
-0.0019249608740210533,
0.06117061898112297,
0.04865546151995659,
-0.0325610488653183,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.008700184524059296,
0.04960457235574722,
0.03395666927099228,
-0.04508807882666588,
0.02617371641099453,
-0.0002682403428480029,
0.012605151161551476,
0.01787777617573738,
-0.006664498709142208,
-0.006197960115969181,
0.08258718252182007,
0.04147165268659592,
-0.02567748911678791,
0.0175... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.016277272254228592,
0.05437195301055908,
0.040595363825559616,
-0.03220142796635628,
0.02042579837143421,
-0.0031843718606978655,
0.0009683044045232236,
0.006916996091604233,
-0.006977589800953865,
-0.0035461662337183952,
0.06813352555036545,
0.050938915461301804,
-0.03066522814333439,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.02272217720746994,
0.06604202836751938,
0.0510023757815361,
-0.03763524442911148,
0.006318778730928898,
-0.006552956532686949,
0.018802456557750702,
0.013962572440505028,
0.002075235592201352,
-0.0058373999781906605,
0.07410035282373428,
0.04580047354102135,
-0.04729551821947098,
0.00453... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.02358655445277691,
0.07866359502077103,
0.049988023936748505,
-0.040321722626686096,
0.015132688917219639,
-0.012521485798060894,
0.01236653234809637,
0.02258704975247383,
0.008386358618736267,
-0.00988307036459446,
0.07674884796142578,
0.040302880108356476,
-0.04479977861046791,
0.01287... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
-0.0022939504124224186,
0.044570036232471466,
0.04319973662495613,
-0.016766391694545746,
0.037863150238990784,
-0.009145619347691536,
0.009597200900316238,
0.011147445067763329,
-0.0008146842592395842,
-0.01832052692770958,
0.0766516700387001,
0.05938871577382088,
-0.037413835525512695,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
-0.00020477402722463012,
0.03825119882822037,
0.04300893098115921,
-0.0174880288541317,
0.03179226443171501,
-0.011856197379529476,
0.02196813002228737,
0.007141795475035906,
0.01045612059533596,
-0.01310053188353777,
0.05883832648396492,
0.052250154316425323,
-0.03355478122830391,
0.02934... |
https://github.com/scikit-learn/scikit-learn/issues/25906 | [
"Bug",
"module:linear_model"
] | Use sample_weight when validating LogisticRegressionCV
Metadata Routing https://github.com/scikit-learn/scikit-learn/pull/24027 will add much needed support for taking into account sample_weight when cross-validating. However, the current implementation of LogisticRegressionCV doesn't seem to be taking advantage of th... | 25,906 | [
0.0100339874625206,
0.05395166948437691,
0.046535592526197433,
-0.022072216495871544,
0.04822400584816933,
-0.012482010759413242,
-0.005191111005842686,
0.005158125422894955,
-0.005895392503589392,
-0.016862457618117332,
0.047714438289403915,
0.08059025555849075,
-0.021975906565785408,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/25903 | [
"Needs Triage"
] | make_classification: repeated features are not chosen uniformly at random
I have just noticed that this piece of code in `make_classification` which selects features to repeat does not do so uniformly at random:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440ca9eb239/sklearn/dataset... | 25,903 | [
0.01059131883084774,
0.03369458392262459,
-0.011820041574537754,
0.005531173199415207,
-0.018507899716496468,
-0.0006752488552592695,
0.04691476374864578,
-0.015006957575678825,
-0.0067361462861299515,
-0.05565729737281799,
0.11146378517150879,
0.004859026055783033,
0.008218473754823208,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25903 | [
"Needs Triage"
] | make_classification: repeated features are not chosen uniformly at random
I have just noticed that this piece of code in `make_classification` which selects features to repeat does not do so uniformly at random:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440ca9eb239/sklearn/dataset... | 25,903 | [
0.012553784064948559,
0.040720753371715546,
-0.016893295571208,
-0.0011817221529781818,
-0.015579002909362316,
-0.008292926475405693,
0.035720739513635635,
-0.01714865118265152,
-0.015418020077049732,
-0.05961895361542702,
0.10861838608980179,
0.016547545790672302,
0.023135265335440636,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25903 | [
"Needs Triage"
] | make_classification: repeated features are not chosen uniformly at random
I have just noticed that this piece of code in `make_classification` which selects features to repeat does not do so uniformly at random:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440ca9eb239/sklearn/dataset... | 25,903 | [
0.006971081253141165,
0.017376437783241272,
-0.009071220643818378,
0.005810215603560209,
-0.013950218446552753,
-0.009371371939778328,
0.036443889141082764,
-0.007672121282666922,
0.007970019243657589,
-0.05057549104094505,
0.10929399728775024,
0.015328352339565754,
0.017137939110398293,
0... |
https://github.com/scikit-learn/scikit-learn/issues/25903 | [
"Needs Triage"
] | make_classification: repeated features are not chosen uniformly at random
I have just noticed that this piece of code in `make_classification` which selects features to repeat does not do so uniformly at random:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440ca9eb239/sklearn/dataset... | 25,903 | [
0.019453028216958046,
0.041644565761089325,
-0.009201224893331528,
-0.0067858826369047165,
-0.007812761701643467,
-0.0014842117670923471,
0.034387972205877304,
-0.013049743138253689,
-0.005693515297025442,
-0.05875933915376663,
0.10970121622085571,
0.020759806036949158,
0.006040680222213268,... |
https://github.com/scikit-learn/scikit-learn/issues/25903 | [
"Needs Triage"
] | make_classification: repeated features are not chosen uniformly at random
I have just noticed that this piece of code in `make_classification` which selects features to repeat does not do so uniformly at random:
https://github.com/scikit-learn/scikit-learn/blob/9aaed498795f68e5956ea762fef9c440ca9eb239/sklearn/dataset... | 25,903 | [
0.015011349692940712,
0.02177085168659687,
-0.011697212234139442,
0.0023886063136160374,
-0.0072909146547317505,
-0.0030910021159797907,
0.033575158566236496,
-0.007995767518877983,
-0.00980379618704319,
-0.0523362010717392,
0.10723761469125748,
0.017000196501612663,
0.0175003744661808,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
https://github.com/scikit-learn/scikit-learn/issues/25896 | [
"New Feature",
"RFC"
] | Support other dataframes like polars and pyarrow not just pandas
### Describe the workflow you want to enable
Currently, scikit-learn nowhere claims to support [pyarrow](https://arrow.apache.org/docs/python/) or [polars](https://www.pola.rs/). And indeed,
```python
import numpy as np
from sklearn.datasets import... | 25,896 | [
-0.024049928411841393,
0.06095851957798004,
0.035001643002033234,
-0.02311539463698864,
0.05334573984146118,
0.03988242894411087,
0.08239445090293884,
-0.0035029833670705557,
0.004978629294782877,
0.004015618935227394,
-0.033846985548734665,
0.06425856053829193,
0.0311697106808424,
0.06895... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.