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/23254 | [
"Documentation"
] | RandomizedSearchCV verbose parameter description is not describing the verbosity levels.
### Describe the issue linked to the documentation
In the website of the RandomizedSearchCV the `verbose` parameter is not discussing the verbosity levels:
"verbose : int
Controls the verbosity: the higher, the more messages."
... | 23,254 | [
0.0030584181658923626,
-0.04023411497473717,
0.004303402733057737,
0.015950240194797516,
0.04720884561538696,
-0.014154311269521713,
-0.028531409800052643,
0.04108942672610283,
0.031453292816877365,
0.00422378396615386,
0.03756633773446083,
0.05884246528148651,
-0.004448555875569582,
0.028... |
https://github.com/scikit-learn/scikit-learn/issues/23253 | [
"New Feature",
"Needs Triage"
] | Global config for n_jobs
### Describe the workflow you want to enable
As the title states, global config for n_jobs.
### Describe your proposed solution
I'd like a global config for n_jobs, where instead of me searching the documentation for every single method, I can maybe supply `N_JOBS=x` and that value gets pas... | 23,253 | [
-0.03449225798249245,
0.10397995263338089,
-0.028172168880701065,
0.007160486187785864,
-0.02682359889149666,
0.015356894582509995,
0.08856851607561111,
-0.01875995472073555,
-0.016808941960334778,
0.024898121133446693,
0.025985363870859146,
0.0312764048576355,
-0.03146649897098541,
-0.013... |
https://github.com/scikit-learn/scikit-learn/issues/23253 | [
"New Feature",
"Needs Triage"
] | Global config for n_jobs
### Describe the workflow you want to enable
As the title states, global config for n_jobs.
### Describe your proposed solution
I'd like a global config for n_jobs, where instead of me searching the documentation for every single method, I can maybe supply `N_JOBS=x` and that value gets pas... | 23,253 | [
-0.0011764089576900005,
0.06045662984251976,
-0.019629428163170815,
0.006501434370875359,
-0.015673154965043068,
0.025210460647940636,
0.08450857549905777,
-0.024009259417653084,
-0.0035128090530633926,
0.014583353884518147,
0.016454994678497314,
0.02935115247964859,
-0.033776599913835526,
... |
https://github.com/scikit-learn/scikit-learn/issues/23253 | [
"New Feature",
"Needs Triage"
] | Global config for n_jobs
### Describe the workflow you want to enable
As the title states, global config for n_jobs.
### Describe your proposed solution
I'd like a global config for n_jobs, where instead of me searching the documentation for every single method, I can maybe supply `N_JOBS=x` and that value gets pas... | 23,253 | [
-0.0393683984875679,
0.0877658948302269,
-0.03150919824838638,
0.006347975227981806,
-0.028703903779387474,
0.015629097819328308,
0.07786522060632706,
-0.020977836102247238,
-0.018760990351438522,
0.029247751459479332,
0.04057874530553818,
0.03548489138484001,
-0.05281616747379303,
-0.0030... |
https://github.com/scikit-learn/scikit-learn/issues/23247 | [
"Bug",
"Needs Triage"
] | Update from sckit-learn 0.24.2 to scikit-learn-1.0.2-py38hae1ba45_ gets Linking Error
### Describe the bug
INFO conda.core.link:_execute_actions(771): ===> LINKING PACKAGE: anaconda::scikit-learn-1.0.2-py38hae1ba45_1 <===
prefix=/Users/davidlaxer/anaconda3/envs/ai
source=/Users/davidlaxer/anaconda3/pkgs/scikit-... | 23,247 | [
0.05026533827185631,
-0.006309757009148598,
-0.023489711806178093,
-0.03883782774209976,
0.049961552023887634,
0.04092840105295181,
0.016933167353272438,
0.033683519810438156,
0.004532238002866507,
-0.028454219922423363,
-0.006337838713079691,
0.06370608508586884,
0.022730154916644096,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23245 | [
"Question"
] | pyinstaller exe error No module named 'sklearn.utils._typedefs'
### Describe the bug
in my code
from sklearn.ensemble import RandomForestClassifier
When I use pyinstaller to convert my .py file to exe it work on scikit-learn version 0.24.2 but not 1.0.2
The command I use is pyinstaller --hidden-import="sklea... | 23,245 | [
0.023472236469388008,
-0.007796217687427998,
0.012022419832646847,
-0.015188513323664665,
0.06477557867765427,
0.009360254742205143,
0.06745152920484543,
-0.004884867928922176,
0.03326348960399628,
-0.03691612929105759,
-0.008733496069908142,
0.036332737654447556,
-0.016164477914571762,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23245 | [
"Question"
] | pyinstaller exe error No module named 'sklearn.utils._typedefs'
### Describe the bug
in my code
from sklearn.ensemble import RandomForestClassifier
When I use pyinstaller to convert my .py file to exe it work on scikit-learn version 0.24.2 but not 1.0.2
The command I use is pyinstaller --hidden-import="sklea... | 23,245 | [
0.023472236469388008,
-0.007796217687427998,
0.012022419832646847,
-0.015188513323664665,
0.06477557867765427,
0.009360254742205143,
0.06745152920484543,
-0.004884867928922176,
0.03326348960399628,
-0.03691612929105759,
-0.008733496069908142,
0.036332737654447556,
-0.016164477914571762,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23245 | [
"Question"
] | pyinstaller exe error No module named 'sklearn.utils._typedefs'
### Describe the bug
in my code
from sklearn.ensemble import RandomForestClassifier
When I use pyinstaller to convert my .py file to exe it work on scikit-learn version 0.24.2 but not 1.0.2
The command I use is pyinstaller --hidden-import="sklea... | 23,245 | [
0.023472236469388008,
-0.007796217687427998,
0.012022419832646847,
-0.015188513323664665,
0.06477557867765427,
0.009360254742205143,
0.06745152920484543,
-0.004884867928922176,
0.03326348960399628,
-0.03691612929105759,
-0.008733496069908142,
0.036332737654447556,
-0.016164477914571762,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/23243 | [
"Documentation"
] | Improve SVR vs KRR example further
Follow up of https://github.com/scikit-learn/scikit-learn/pull/22804, example code is https://github.com/scikit-learn/scikit-learn/blob/main/examples/miscellaneous/plot_kernel_ridge_regression.py
- once the grid-search is done use `svr.best_estimator_` and `kr.best_estimator_` as ... | 23,243 | [
0.03494816645979881,
0.020898917689919472,
0.003963793162256479,
0.01714165136218071,
0.009443233720958233,
-0.03302090987563133,
0.010905508883297443,
0.03432090952992439,
-0.03017471358180046,
0.003053084248676896,
0.015402313321828842,
0.056909531354904175,
0.013632021844387054,
0.05196... |
https://github.com/scikit-learn/scikit-learn/issues/23243 | [
"Documentation"
] | Improve SVR vs KRR example further
Follow up of https://github.com/scikit-learn/scikit-learn/pull/22804, example code is https://github.com/scikit-learn/scikit-learn/blob/main/examples/miscellaneous/plot_kernel_ridge_regression.py
- once the grid-search is done use `svr.best_estimator_` and `kr.best_estimator_` as ... | 23,243 | [
0.024601802229881287,
0.018932940438389778,
0.008291881531476974,
0.016521934419870377,
0.020960114896297455,
-0.035317499190568924,
0.01413035113364458,
0.041083380579948425,
-0.017087552696466446,
0.015874678269028664,
0.01466873474419117,
0.07172898948192596,
0.003887169761583209,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/23243 | [
"Documentation"
] | Improve SVR vs KRR example further
Follow up of https://github.com/scikit-learn/scikit-learn/pull/22804, example code is https://github.com/scikit-learn/scikit-learn/blob/main/examples/miscellaneous/plot_kernel_ridge_regression.py
- once the grid-search is done use `svr.best_estimator_` and `kr.best_estimator_` as ... | 23,243 | [
0.028094738721847534,
0.02161519043147564,
0.009951555170118809,
0.014689135365188122,
0.018543822690844536,
-0.03456678241491318,
0.013943847268819809,
0.04306226223707199,
-0.018685711547732353,
0.018100611865520477,
0.013533273711800575,
0.07102811336517334,
0.002692368347197771,
0.0566... |
https://github.com/scikit-learn/scikit-learn/issues/23243 | [
"Documentation"
] | Improve SVR vs KRR example further
Follow up of https://github.com/scikit-learn/scikit-learn/pull/22804, example code is https://github.com/scikit-learn/scikit-learn/blob/main/examples/miscellaneous/plot_kernel_ridge_regression.py
- once the grid-search is done use `svr.best_estimator_` and `kr.best_estimator_` as ... | 23,243 | [
0.03119214065372944,
0.020112408325076103,
0.012453055009245872,
0.019315995275974274,
0.023213448002934456,
-0.03595232963562012,
0.0051247975789010525,
0.04393448680639267,
-0.0182753074914217,
0.023209193721413612,
0.009381345473229885,
0.06936684995889664,
0.009068593382835388,
0.04785... |
https://github.com/scikit-learn/scikit-learn/issues/23243 | [
"Documentation"
] | Improve SVR vs KRR example further
Follow up of https://github.com/scikit-learn/scikit-learn/pull/22804, example code is https://github.com/scikit-learn/scikit-learn/blob/main/examples/miscellaneous/plot_kernel_ridge_regression.py
- once the grid-search is done use `svr.best_estimator_` and `kr.best_estimator_` as ... | 23,243 | [
0.02769792638719082,
0.016840441152453423,
0.01306774653494358,
0.017554903402924538,
0.02546512708067894,
-0.03985679894685745,
0.0006209338316693902,
0.041209280490875244,
-0.01810535416007042,
0.022486500442028046,
0.009565945714712143,
0.07188842445611954,
0.008292064070701599,
0.05154... |
https://github.com/scikit-learn/scikit-learn/issues/23231 | [
"Easy",
"Documentation"
] | Scan examples to see where HistGradientBoostingRegressor should replace GradientBoostingRegressor
### Describe the issue linked to the documentation
People still use GradientBoostingRegressor where they should probably be using HistGradientBoostingRegressor (and similar for Classifier).
We need to avoid situations... | 23,231 | [
0.015614272095263004,
0.046435534954071045,
-0.006603363901376724,
-0.021075710654258728,
0.00043959595495834947,
-0.03844660520553589,
0.003086737124249339,
0.005330161191523075,
-0.04550226405262947,
-0.0019583606626838446,
0.06611563265323639,
-0.024163782596588135,
0.00460470374673605,
... |
https://github.com/scikit-learn/scikit-learn/issues/23231 | [
"Easy",
"Documentation"
] | Scan examples to see where HistGradientBoostingRegressor should replace GradientBoostingRegressor
### Describe the issue linked to the documentation
People still use GradientBoostingRegressor where they should probably be using HistGradientBoostingRegressor (and similar for Classifier).
We need to avoid situations... | 23,231 | [
0.017949772998690605,
0.008980868384242058,
0.0001241410500369966,
-0.012253663502633572,
-0.021518796682357788,
-0.039050839841365814,
-0.014821238815784454,
0.010037009604275227,
-0.048926062881946564,
-0.009119088761508465,
0.06698019057512283,
-0.04349280893802643,
0.011001304723322392,
... |
https://github.com/scikit-learn/scikit-learn/issues/23231 | [
"Easy",
"Documentation"
] | Scan examples to see where HistGradientBoostingRegressor should replace GradientBoostingRegressor
### Describe the issue linked to the documentation
People still use GradientBoostingRegressor where they should probably be using HistGradientBoostingRegressor (and similar for Classifier).
We need to avoid situations... | 23,231 | [
0.014285956509411335,
0.017847353592514992,
-0.00392565643414855,
-0.024846654385328293,
0.005668476689606905,
-0.03597039729356766,
-0.0010848597157746553,
0.0003989612450823188,
-0.04486473649740219,
0.00008758636977290735,
0.08302736282348633,
-0.03997901827096939,
0.010151475667953491,
... |
https://github.com/scikit-learn/scikit-learn/issues/23231 | [
"Easy",
"Documentation"
] | Scan examples to see where HistGradientBoostingRegressor should replace GradientBoostingRegressor
### Describe the issue linked to the documentation
People still use GradientBoostingRegressor where they should probably be using HistGradientBoostingRegressor (and similar for Classifier).
We need to avoid situations... | 23,231 | [
0.03366557136178017,
0.034356117248535156,
-0.005462643690407276,
-0.027602337300777435,
-0.021965302526950836,
-0.027302568778395653,
0.028446003794670105,
-0.01419433020055294,
-0.05654648691415787,
-0.04128935560584068,
0.06458449363708496,
-0.034659236669540405,
0.017291201278567314,
-... |
https://github.com/scikit-learn/scikit-learn/issues/23231 | [
"Easy",
"Documentation"
] | Scan examples to see where HistGradientBoostingRegressor should replace GradientBoostingRegressor
### Describe the issue linked to the documentation
People still use GradientBoostingRegressor where they should probably be using HistGradientBoostingRegressor (and similar for Classifier).
We need to avoid situations... | 23,231 | [
0.015578662045300007,
0.017558902502059937,
-0.008700876496732235,
-0.02471526898443699,
0.0024616217706352472,
-0.03632095083594322,
0.0037644291296601295,
0.000044472009903984144,
-0.04901749640703201,
0.003482099622488022,
0.0839606523513794,
-0.03998330235481262,
0.012212412431836128,
... |
https://github.com/scikit-learn/scikit-learn/issues/23226 | [
"Bug",
"Needs Triage"
] | ColumnTransformer FunctionTransformer get_feature_names_out() does not work
### Describe the bug
When I use a FunctionTransformer in my ColumnTransformer and try to use the columntransformer.get_feature_names_out() function I get an error. Fitting/Predicting works fine as long as I dont touch this function.
Erro... | 23,226 | [
0.031088998541235924,
-0.015858151018619537,
0.0215587317943573,
-0.0026486783754080534,
0.0904594212770462,
0.024291429668664932,
0.11185101419687271,
0.03949860483407974,
0.005133716389536858,
0.0386352464556694,
-0.02424548752605915,
0.0016732790973037481,
0.01841031387448311,
0.0283440... |
https://github.com/scikit-learn/scikit-learn/issues/23226 | [
"Bug",
"Needs Triage"
] | ColumnTransformer FunctionTransformer get_feature_names_out() does not work
### Describe the bug
When I use a FunctionTransformer in my ColumnTransformer and try to use the columntransformer.get_feature_names_out() function I get an error. Fitting/Predicting works fine as long as I dont touch this function.
Erro... | 23,226 | [
0.031088998541235924,
-0.015858151018619537,
0.0215587317943573,
-0.0026486783754080534,
0.0904594212770462,
0.024291429668664932,
0.11185101419687271,
0.03949860483407974,
0.005133716389536858,
0.0386352464556694,
-0.02424548752605915,
0.0016732790973037481,
0.01841031387448311,
0.0283440... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23225 | [
"Bug",
"module:compose"
] | ColumnTransformer with category_encoders doesn't encode "integer" columns
### Describe the bug
Not sure if this really be consider a bug report fo scikit-learn as this may be issue rising due to interaction between
- `ColumnTransformer`
- `category_encoders`
- `pandas` (categorical datatype)
Anyways, I was t... | 23,225 | [
0.0005920412950217724,
0.07576294243335724,
0.03144676610827446,
-0.007200795225799084,
0.09694097936153412,
0.024213477969169617,
0.05829460173845291,
0.05056794360280037,
-0.05434051528573036,
-0.02399732917547226,
0.02205113135278225,
0.025817861780524254,
0.03500653803348541,
0.0078421... |
https://github.com/scikit-learn/scikit-learn/issues/23213 | [
"Bug"
] | precision_recall_curve() is not returning the full curve at high recall
### Describe the bug
`precision_recall_curve()` is truncating the curve once it reach maximum recall 1, that is not nice because it is removing relevant information.
Indeed, once you reach the first threshold value that gives a recall of 100%,... | 23,213 | [
0.004929034970700741,
-0.025051331147551537,
0.020244112238287926,
0.02941151149570942,
0.04841136559844017,
-0.04131700098514557,
-0.048351794481277466,
0.013862928375601768,
-0.051565151661634445,
-0.0020644902251660824,
0.04557494819164276,
0.024308763444423676,
0.003415733575820923,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23213 | [
"Bug"
] | precision_recall_curve() is not returning the full curve at high recall
### Describe the bug
`precision_recall_curve()` is truncating the curve once it reach maximum recall 1, that is not nice because it is removing relevant information.
Indeed, once you reach the first threshold value that gives a recall of 100%,... | 23,213 | [
0.004929034970700741,
-0.025051331147551537,
0.020244112238287926,
0.02941151149570942,
0.04841136559844017,
-0.04131700098514557,
-0.048351794481277466,
0.013862928375601768,
-0.051565151661634445,
-0.0020644902251660824,
0.04557494819164276,
0.024308763444423676,
0.003415733575820923,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23211 | [
"Build / CI"
] | Grouped timings for parametrized tests
Sometimes it is convenient to group parametrized tests by function name, to look at the total time taken by the group of parametrized tests, rather than individual parametrized tests. When trying multiple combinations of parameters, it is easy to generate a big number of parametr... | 23,211 | [
-0.03510702773928642,
0.056721433997154236,
-0.030833285301923752,
0.0028005640488117933,
0.03587041422724724,
-0.009431587532162666,
0.07932952791452408,
0.04666009172797203,
-0.023171376436948776,
-0.004632033407688141,
0.00023252959363162518,
0.0352480411529541,
0.02063615620136261,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23211 | [
"Build / CI"
] | Grouped timings for parametrized tests
Sometimes it is convenient to group parametrized tests by function name, to look at the total time taken by the group of parametrized tests, rather than individual parametrized tests. When trying multiple combinations of parameters, it is easy to generate a big number of parametr... | 23,211 | [
-0.03510702773928642,
0.056721433997154236,
-0.030833285301923752,
0.0028005640488117933,
0.03587041422724724,
-0.009431587532162666,
0.07932952791452408,
0.04666009172797203,
-0.023171376436948776,
-0.004632033407688141,
0.00023252959363162518,
0.0352480411529541,
0.02063615620136261,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23205 | [
"New Feature",
"Needs Decision - Include Feature"
] | Exposing submodules directly from main sklearn module instead of recursively importing each submodule separately
### Describe the workflow you want to enable
The problem that I'm facing is the same as the issue described in this [stackoverflow](https://stackoverflow.com/questions/46572475/module-sklearn-has-no-attr... | 23,205 | [
0.015005785971879959,
0.057873357087373734,
0.008926451206207275,
-0.015163913369178772,
0.05340522900223732,
0.037576623260974884,
0.12009148299694061,
0.00037138990592211485,
0.07299739867448807,
-0.018264127895236015,
-0.0744858905673027,
0.0977434441447258,
-0.01278290618211031,
0.0316... |
https://github.com/scikit-learn/scikit-learn/issues/23205 | [
"New Feature",
"Needs Decision - Include Feature"
] | Exposing submodules directly from main sklearn module instead of recursively importing each submodule separately
### Describe the workflow you want to enable
The problem that I'm facing is the same as the issue described in this [stackoverflow](https://stackoverflow.com/questions/46572475/module-sklearn-has-no-attr... | 23,205 | [
0.015005785971879959,
0.057873357087373734,
0.008926451206207275,
-0.015163913369178772,
0.05340522900223732,
0.037576623260974884,
0.12009148299694061,
0.00037138990592211485,
0.07299739867448807,
-0.018264127895236015,
-0.0744858905673027,
0.0977434441447258,
-0.01278290618211031,
0.0316... |
https://github.com/scikit-learn/scikit-learn/issues/23205 | [
"New Feature",
"Needs Decision - Include Feature"
] | Exposing submodules directly from main sklearn module instead of recursively importing each submodule separately
### Describe the workflow you want to enable
The problem that I'm facing is the same as the issue described in this [stackoverflow](https://stackoverflow.com/questions/46572475/module-sklearn-has-no-attr... | 23,205 | [
0.015005785971879959,
0.057873357087373734,
0.008926451206207275,
-0.015163913369178772,
0.05340522900223732,
0.037576623260974884,
0.12009148299694061,
0.00037138990592211485,
0.07299739867448807,
-0.018264127895236015,
-0.0744858905673027,
0.0977434441447258,
-0.01278290618211031,
0.0316... |
https://github.com/scikit-learn/scikit-learn/issues/23205 | [
"New Feature",
"Needs Decision - Include Feature"
] | Exposing submodules directly from main sklearn module instead of recursively importing each submodule separately
### Describe the workflow you want to enable
The problem that I'm facing is the same as the issue described in this [stackoverflow](https://stackoverflow.com/questions/46572475/module-sklearn-has-no-attr... | 23,205 | [
0.015005785971879959,
0.057873357087373734,
0.008926451206207275,
-0.015163913369178772,
0.05340522900223732,
0.037576623260974884,
0.12009148299694061,
0.00037138990592211485,
0.07299739867448807,
-0.018264127895236015,
-0.0744858905673027,
0.0977434441447258,
-0.01278290618211031,
0.0316... |
https://github.com/scikit-learn/scikit-learn/issues/23205 | [
"New Feature",
"Needs Decision - Include Feature"
] | Exposing submodules directly from main sklearn module instead of recursively importing each submodule separately
### Describe the workflow you want to enable
The problem that I'm facing is the same as the issue described in this [stackoverflow](https://stackoverflow.com/questions/46572475/module-sklearn-has-no-attr... | 23,205 | [
0.015005785971879959,
0.057873357087373734,
0.008926451206207275,
-0.015163913369178772,
0.05340522900223732,
0.037576623260974884,
0.12009148299694061,
0.00037138990592211485,
0.07299739867448807,
-0.018264127895236015,
-0.0744858905673027,
0.0977434441447258,
-0.01278290618211031,
0.0316... |
https://github.com/scikit-learn/scikit-learn/issues/23199 | [
"module:linear_model",
"module:discriminant_analysis"
] | Consider LSMR instead of LSQR
#### Proposition
Currently, we use the LSQR solver for (possibly sparse) linear equation / least squares problems in
- `LinearDiscriminantAnalysis`
- `LinearRegression`
- `Ridge`
Consider to change to LSMR because according to [1] and [2] LSMR might be able to terminate earlier and... | 23,199 | [
-0.03853936120867729,
0.08352937549352646,
0.03903846815228462,
-0.006980984006077051,
0.05790561065077782,
0.004740182310342789,
0.03252251446247101,
0.04532279819250107,
0.025062818080186844,
-0.03399088233709335,
0.007716207765042782,
0.036679379642009735,
-0.02634527161717415,
-0.00647... |
https://github.com/scikit-learn/scikit-learn/issues/23195 | [
"New Feature"
] | Add more robust Kmeans initializer for mixture.GaussianMixture
### Describe the workflow you want to enable
The [GaussianMixture](https://github.com/scikit-learn/scikit-learn/blob/24f3006fb2d054b8afb26382209ae33629a8dfe0/sklearn/mixture/_gaussian_mixture.py#L456 ) class has an option of initializing using `K-means`... | 23,195 | [
-0.020007934421300888,
0.027503397315740585,
0.030611351132392883,
-0.0007272639777511358,
0.08388067036867142,
0.007163087371736765,
-0.00745348958298564,
-0.008945733308792114,
-0.01938479207456112,
0.014783523976802826,
0.03654991090297699,
0.04732711240649223,
-0.027211889624595642,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23195 | [
"New Feature"
] | Add more robust Kmeans initializer for mixture.GaussianMixture
### Describe the workflow you want to enable
The [GaussianMixture](https://github.com/scikit-learn/scikit-learn/blob/24f3006fb2d054b8afb26382209ae33629a8dfe0/sklearn/mixture/_gaussian_mixture.py#L456 ) class has an option of initializing using `K-means`... | 23,195 | [
-0.020007934421300888,
0.027503397315740585,
0.030611351132392883,
-0.0007272639777511358,
0.08388067036867142,
0.007163087371736765,
-0.00745348958298564,
-0.008945733308792114,
-0.01938479207456112,
0.014783523976802826,
0.03654991090297699,
0.04732711240649223,
-0.027211889624595642,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23195 | [
"New Feature"
] | Add more robust Kmeans initializer for mixture.GaussianMixture
### Describe the workflow you want to enable
The [GaussianMixture](https://github.com/scikit-learn/scikit-learn/blob/24f3006fb2d054b8afb26382209ae33629a8dfe0/sklearn/mixture/_gaussian_mixture.py#L456 ) class has an option of initializing using `K-means`... | 23,195 | [
-0.020007934421300888,
0.027503397315740585,
0.030611351132392883,
-0.0007272639777511358,
0.08388067036867142,
0.007163087371736765,
-0.00745348958298564,
-0.008945733308792114,
-0.01938479207456112,
0.014783523976802826,
0.03654991090297699,
0.04732711240649223,
-0.027211889624595642,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23195 | [
"New Feature"
] | Add more robust Kmeans initializer for mixture.GaussianMixture
### Describe the workflow you want to enable
The [GaussianMixture](https://github.com/scikit-learn/scikit-learn/blob/24f3006fb2d054b8afb26382209ae33629a8dfe0/sklearn/mixture/_gaussian_mixture.py#L456 ) class has an option of initializing using `K-means`... | 23,195 | [
-0.020007934421300888,
0.027503397315740585,
0.030611351132392883,
-0.0007272639777511358,
0.08388067036867142,
0.007163087371736765,
-0.00745348958298564,
-0.008945733308792114,
-0.01938479207456112,
0.014783523976802826,
0.03654991090297699,
0.04732711240649223,
-0.027211889624595642,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23195 | [
"New Feature"
] | Add more robust Kmeans initializer for mixture.GaussianMixture
### Describe the workflow you want to enable
The [GaussianMixture](https://github.com/scikit-learn/scikit-learn/blob/24f3006fb2d054b8afb26382209ae33629a8dfe0/sklearn/mixture/_gaussian_mixture.py#L456 ) class has an option of initializing using `K-means`... | 23,195 | [
-0.020007934421300888,
0.027503397315740585,
0.030611351132392883,
-0.0007272639777511358,
0.08388067036867142,
0.007163087371736765,
-0.00745348958298564,
-0.008945733308792114,
-0.01938479207456112,
0.014783523976802826,
0.03654991090297699,
0.04732711240649223,
-0.027211889624595642,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23187 | [
"Question"
] | Different results depending on labels using linear_svc and adaboost
Hi all,
I'm having different results with linear_svc and adaboost depending on the labels `0/1 or 1/-1`. Below I've provided github repo with a notebook example using part of the[ CoronaHack -Chest X-Ray-Datase](https://www.kaggle.com/datasets/pra... | 23,187 | [
0.018188174813985825,
-0.09550374746322632,
0.009112978354096413,
0.04531005397439003,
0.05576383322477341,
-0.0214796531945467,
0.001375830383040011,
0.029256517067551613,
-0.016690760850906372,
0.014546671882271767,
0.025209948420524597,
-0.0005838476354256272,
0.05347885563969612,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/23187 | [
"Question"
] | Different results depending on labels using linear_svc and adaboost
Hi all,
I'm having different results with linear_svc and adaboost depending on the labels `0/1 or 1/-1`. Below I've provided github repo with a notebook example using part of the[ CoronaHack -Chest X-Ray-Datase](https://www.kaggle.com/datasets/pra... | 23,187 | [
0.018188174813985825,
-0.09550374746322632,
0.009112978354096413,
0.04531005397439003,
0.05576383322477341,
-0.0214796531945467,
0.001375830383040011,
0.029256517067551613,
-0.016690760850906372,
0.014546671882271767,
0.025209948420524597,
-0.0005838476354256272,
0.05347885563969612,
0.057... |
https://github.com/scikit-learn/scikit-learn/issues/23185 | [
"Build / CI"
] | The azure CI times out more and more often
- The 2 windows jobs timeout (60min) more often than they don't these days. When they manage to complete, they take ~55min. I recall that they used to take "only" ~40min not so long ago.
- The 2 macos jobs don't timeout but usually take ~50min while they used to take ~30mi... | 23,185 | [
-0.04790448397397995,
-0.022059034556150436,
-0.02228255197405815,
-0.056387610733509064,
-0.0040590534918010235,
0.01001951564103365,
0.003069597762078047,
0.04763485863804817,
-0.01151412446051836,
0.058918729424476624,
0.04429033771157265,
0.033913083374500275,
0.014428751543164253,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23185 | [
"Build / CI"
] | The azure CI times out more and more often
- The 2 windows jobs timeout (60min) more often than they don't these days. When they manage to complete, they take ~55min. I recall that they used to take "only" ~40min not so long ago.
- The 2 macos jobs don't timeout but usually take ~50min while they used to take ~30mi... | 23,185 | [
-0.021046770736575127,
-0.047850724309682846,
-0.01700357347726822,
-0.06425073742866516,
0.04204234108328819,
0.007548208814114332,
0.040948983281850815,
0.01303284615278244,
-0.014176661148667336,
0.05938471108675003,
0.053523268550634384,
0.04613411799073219,
-0.022847184911370277,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
0.0023819636553525925,
0.020335255190730095,
0.012074318714439869,
0.011265791952610016,
0.031029148027300835,
-0.025891754776239395,
0.009672620333731174,
0.045895010232925415,
-0.06318565458059311,
0.01800706423819065,
0.07733502984046936,
0.04871630296111107,
0.009530647657811642,
0.069... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.0003455009718891233,
0.0077798329293727875,
0.01285164151340723,
0.010837863199412823,
0.002725407248362899,
-0.011523663066327572,
0.020075077190995216,
0.062748484313488,
-0.08543553948402405,
0.025517387315630913,
0.058325089514255524,
0.04143034294247627,
0.028631778433918953,
0.043... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.004312554839998484,
0.027429990470409393,
0.023654695600271225,
0.02253809943795204,
0.023779673501849174,
-0.017938900738954544,
0.020577965304255486,
0.06628770381212234,
-0.07210664451122284,
0.008509098552167416,
0.06493727117776871,
0.051616016775369644,
-0.0009316075593233109,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.018005967140197754,
0.030882887542247772,
0.019039390608668327,
0.006225781049579382,
0.0069472468458116055,
-0.02020101621747017,
0.018259186297655106,
0.06921491026878357,
-0.08202565461397171,
0.01331663504242897,
0.05148136243224144,
0.04999995231628418,
0.02168360911309719,
0.07116... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.007007953245192766,
0.02076098509132862,
0.007488372270017862,
0.021187584847211838,
0.014680738560855389,
-0.026860861107707024,
0.024010734632611275,
0.0577363558113575,
-0.08046600222587585,
0.008551563136279583,
0.06876210868358612,
0.038100145757198334,
0.016637375578284264,
0.0710... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
0.021207962185144424,
0.05735675245523453,
0.015081049874424934,
0.01471003983169794,
0.02548893168568611,
-0.011676913127303123,
0.027742944657802582,
0.04971751198172569,
-0.10318508744239807,
0.012048535980284214,
0.046790286898612976,
0.03589508682489395,
0.029742972925305367,
0.070174... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.0050399936735630035,
0.02062929980456829,
0.01316840946674347,
0.012047000229358673,
0.018819965422153473,
-0.03215448930859566,
0.018742933869361877,
0.06159320846199989,
-0.08213534951210022,
0.013625533320009708,
0.06006615236401558,
0.04047742113471031,
0.011101538315415382,
0.07088... |
https://github.com/scikit-learn/scikit-learn/issues/23180 | [
"Bug",
"Moderate",
"help wanted",
"module:linear_model"
] | Investigate SAG/SAGA solver
#### Description
The newly introduced tight tests for `Ridge` in #22910 together with the random seed fixture in #22749 revealed some shortcomings of the sag and saga solver:
1. ~~It shows some random behavior even with fixed random seed.~~
2. The `tol` needs to be set much smaller to ... | 23,180 | [
-0.0025793106760829687,
0.02396489307284355,
0.024224048480391502,
0.02799374982714653,
0.022048888728022575,
-0.014931665733456612,
0.0248276237398386,
0.06834416836500168,
-0.07084324955940247,
0.011242554523050785,
0.06400497257709503,
0.03645097091794014,
0.004270150326192379,
0.070403... |
https://github.com/scikit-learn/scikit-learn/issues/23179 | [
"Bug",
"module:neural_network",
"Needs Investigation"
] | Wrong normalization for pseudolikelihood in Restricted Boltzmann Machine.
### Describe the bug
Pseudolikelihood of a sample under Restricted Boltzmann machine includes the number of features as a multiplier.
Thus pseudolikelihood reflects the dimensionality of visible units, apart from the sample probability.
I... | 23,179 | [
-0.010593296028673649,
-0.05784163996577263,
0.019432656466960907,
-0.0016912018181756139,
0.03651966154575348,
-0.03602898493409157,
0.03481780365109444,
-0.03864506259560585,
-0.041027430444955826,
0.04498087987303734,
0.015345689840614796,
0.007641685660928488,
0.011107298545539379,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23179 | [
"Bug",
"module:neural_network",
"Needs Investigation"
] | Wrong normalization for pseudolikelihood in Restricted Boltzmann Machine.
### Describe the bug
Pseudolikelihood of a sample under Restricted Boltzmann machine includes the number of features as a multiplier.
Thus pseudolikelihood reflects the dimensionality of visible units, apart from the sample probability.
I... | 23,179 | [
-0.010593296028673649,
-0.05784163996577263,
0.019432656466960907,
-0.0016912018181756139,
0.03651966154575348,
-0.03602898493409157,
0.03481780365109444,
-0.03864506259560585,
-0.041027430444955826,
0.04498087987303734,
0.015345689840614796,
0.007641685660928488,
0.011107298545539379,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.016327809542417526,
0.03447628393769264,
0.024773893877863884,
-0.004180677700787783,
0.06225105747580528,
-0.04264124855399132,
-0.029996253550052643,
0.026107672601938248,
-0.0006121232290752232,
-0.032361194491386414,
0.05195995420217514,
0.02810662053525448,
-0.02002432569861412,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.019753020256757736,
0.04485171288251877,
0.011240324936807156,
-0.007756407838314772,
0.03927639499306679,
-0.04234323278069496,
0.012788177467882633,
0.026822326704859734,
0.012169059365987778,
-0.01787959784269333,
0.04947438836097717,
0.015259377658367157,
-0.03443344682455063,
0.074... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.02719203382730484,
0.05742553994059563,
0.0175598356872797,
-0.011655940674245358,
0.05816448852419853,
-0.04293859750032425,
0.003621008712798357,
0.028551772236824036,
0.01810997910797596,
-0.017617767676711082,
0.0651705339550972,
0.007650549989193678,
-0.05116266384720802,
0.0674560... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.01704578660428524,
0.03873077407479286,
0.01314321719110012,
-0.01580221764743328,
0.030150894075632095,
-0.0383681021630764,
0.017900971695780754,
0.03226126730442047,
0.015349684283137321,
-0.023593124002218246,
0.055967170745134354,
0.00297431368380785,
-0.03477178141474724,
0.081828... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.006883788388222456,
0.04294934868812561,
0.027268679812550545,
-0.01935681700706482,
0.06096046790480614,
-0.036863453686237335,
0.0157766155898571,
0.007087593898177147,
0.028371747583150864,
-0.03112894296646118,
0.0391598679125309,
0.02546435222029686,
-0.021467139944434166,
0.064496... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.012802891433238983,
0.05151378735899925,
0.020287374034523964,
-0.009474127553403378,
0.02693655714392662,
-0.02507803589105606,
0.024275321513414383,
0.034566525369882584,
0.02696347050368786,
-0.01467714924365282,
0.047675520181655884,
-0.011376854963600636,
-0.021408746019005775,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23178 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | roc_auc_score must warn user about using labels instead of probabilities in binary case
### Describe the workflow you want to enable
I've seen a lot of people use `YetAnotherClassifier.predict()` instead of desired `.predict_proba()[:, 1]` and get wrong result. This problem sometimes can be hard to found and IMO it c... | 23,178 | [
-0.014647711999714375,
0.03456701338291168,
0.022229056805372238,
-0.013692070730030537,
0.025997353717684746,
-0.04201798513531685,
0.013357816264033318,
0.0312504805624485,
0.020549237728118896,
-0.028594210743904114,
0.046764153987169266,
0.002985486527904868,
-0.033482592552900314,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
On one side, I feel guilty for... | 23,177 | [
-0.01977858878672123,
0.0406518317759037,
0.000362623279215768,
-0.014832226559519768,
0.06338729709386826,
-0.005547030363231897,
0.02714444138109684,
0.05727161839604378,
-0.02196568436920643,
0.020213156938552856,
0.056316863745450974,
0.08965223282575607,
-0.008344977162778378,
0.06717... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
What I don't get is that these... | 23,177 | [
-0.012348035350441933,
0.04738682508468628,
0.001724356901831925,
-0.02551053650677204,
0.06430762261152267,
0.009456019848585129,
0.022765344008803368,
-0.0007224191795103252,
-0.04917445778846741,
0.020877253264188766,
0.07060790807008743,
0.0570312961935997,
0.009020249359309673,
0.0628... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
Should we open an issue for th... | 23,177 | [
-0.013890580274164677,
0.046595461666584015,
-0.010560417547821999,
-0.0249562356621027,
0.07170571386814117,
0.002602139487862587,
0.03432092443108559,
0.03884205222129822,
-0.05047309771180153,
-0.0012711712624877691,
0.07704073935747147,
0.09287317842245102,
-0.011712591163814068,
0.082... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
@lorentzenchr I'm not sure wha... | 23,177 | [
-0.005204421933740377,
0.049837544560432434,
0.0005869997548870742,
-0.01277356781065464,
0.06531334668397903,
0.0005528955953195691,
0.025551917031407356,
0.05578067898750305,
-0.034927722066640854,
-0.0008435561321675777,
0.05883447453379631,
0.08511573821306229,
-0.00205049617215991,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
I probably found the source of... | 23,177 | [
-0.012871728278696537,
0.018076324835419655,
-0.0000574424302612897,
0.006075584329664707,
0.05099086835980415,
-0.006169780157506466,
0.030398381873965263,
0.029039084911346436,
-0.011231438256800175,
0.03607001528143883,
0.06229037791490555,
0.08733958750963211,
-0.0018388140015304089,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23177 | [
"Blocker",
"module:linear_model",
"module:test-suite"
] | test_ridge_regression_unpenalized is unstable
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41172&view=logs&jobId=c0f56aa6-924f-525c-3d4d-cdeecf409e66&j=c0f56aa6-924f-525c-3d4d-cdeecf409e66&t=f81f9209-4a58-571a-6d88-f139846a65e1
cc/ @lorentzenchr
COMMENT:
With the randomness of the CI,... | 23,177 | [
-0.015417845919728279,
0.0297529399394989,
0.008443602360785007,
-0.00537487119436264,
0.07202174514532089,
0.008387533016502857,
0.027225034311413765,
0.03191962465643883,
-0.017211999744176865,
0.027622580528259277,
0.0811968445777893,
0.06963962316513062,
-0.008145489729940891,
0.037079... |
https://github.com/scikit-learn/scikit-learn/issues/23176 | [
"New Feature",
"Needs Triage"
] | CalibratedClassifierCV allowing for more types of input data
### Describe the workflow you want to enable
With the current implementation of **CalibratedClassifierCV**, it cannot be used it with generic datasets.
Whenever input data are of mixed type (e.g. Pandas DataFrame), the `_validate_data()` method will fail, ... | 23,176 | [
-0.028537629172205925,
0.0630088523030281,
0.06255631893873215,
-0.0035635989625006914,
0.09250032901763916,
0.009535088203847408,
0.05615759268403053,
0.04756230115890503,
0.014371536672115326,
-0.01773773320019245,
0.02223379537463188,
-0.007664013188332319,
0.009493342600762844,
0.03756... |
https://github.com/scikit-learn/scikit-learn/issues/23176 | [
"New Feature",
"Needs Triage"
] | CalibratedClassifierCV allowing for more types of input data
### Describe the workflow you want to enable
With the current implementation of **CalibratedClassifierCV**, it cannot be used it with generic datasets.
Whenever input data are of mixed type (e.g. Pandas DataFrame), the `_validate_data()` method will fail, ... | 23,176 | [
-0.035146214067935944,
0.06767503172159195,
0.055753327906131744,
-0.007099541835486889,
0.08825014531612396,
0.010808187536895275,
0.05848861485719681,
0.049493372440338135,
0.007320574019104242,
-0.02179732546210289,
0.018526749685406685,
-0.013804307207465172,
0.00881078653037548,
0.045... |
https://github.com/scikit-learn/scikit-learn/issues/23175 | [
"Bug",
"Needs Triage"
] | ndcg_score
### Describe the bug
ndcg_score doesn't work. you're a bunch of useless spaghetti coders, just go to italian restaurant if you like pasta, don't waste other people's time producing tons of govnokod.
### Steps/Code to Reproduce
import ndcg_score
### Expected Results
work
### Actual Results
no work
##... | 23,175 | [
-0.05503030866384506,
-0.012918435968458652,
-0.007767722476273775,
0.016870811581611633,
0.005247312597930431,
0.004535657819360495,
0.0012789315078407526,
-0.0002474181237630546,
0.010607720352709293,
0.0013013383140787482,
0.029665904119610786,
0.008828816004097462,
0.011413518339395523,
... |
https://github.com/scikit-learn/scikit-learn/issues/23172 | [
"Documentation",
"module:linear_model",
"module:preprocessing",
"module:test-suite"
] | Clarify and test dropping categories in linear models
#### Proposition
- [ ] Improve documentation about dropping or not dropping category levels.
- [ ] Add tests that linear models (with canonical link) satisfy for L2 penalized coefficients of one category: `sum_{levels} coef = 0`
#### Reasoning
We tell 2 diffe... | 23,172 | [
0.01948796771466732,
0.0885741338133812,
0.02034056931734085,
0.01783771999180317,
0.004264194052666426,
0.037035390734672546,
0.09422630071640015,
0.01640673540532589,
0.0037438501603901386,
-0.06277619302272797,
0.04249276965856552,
0.022952266037464142,
0.04761587083339691,
0.0257150065... |
https://github.com/scikit-learn/scikit-learn/issues/23172 | [
"Documentation",
"module:linear_model",
"module:preprocessing",
"module:test-suite"
] | Clarify and test dropping categories in linear models
#### Proposition
- [ ] Improve documentation about dropping or not dropping category levels.
- [ ] Add tests that linear models (with canonical link) satisfy for L2 penalized coefficients of one category: `sum_{levels} coef = 0`
#### Reasoning
We tell 2 diffe... | 23,172 | [
0.016681015491485596,
0.08865498006343842,
0.018016371876001358,
0.02150145173072815,
0.007132843602448702,
0.03648978844285011,
0.0929599329829216,
0.018890436738729477,
0.00041715719271451235,
-0.06797656416893005,
0.040944118052721024,
0.021230846643447876,
0.04554806277155876,
0.026557... |
https://github.com/scikit-learn/scikit-learn/issues/23172 | [
"Documentation",
"module:linear_model",
"module:preprocessing",
"module:test-suite"
] | Clarify and test dropping categories in linear models
#### Proposition
- [ ] Improve documentation about dropping or not dropping category levels.
- [ ] Add tests that linear models (with canonical link) satisfy for L2 penalized coefficients of one category: `sum_{levels} coef = 0`
#### Reasoning
We tell 2 diffe... | 23,172 | [
0.017559753730893135,
0.09050828218460083,
0.01830226741731167,
0.017970629036426544,
0.009164354763925076,
0.039704564958810806,
0.0938701257109642,
0.02040844038128853,
0.00646380428224802,
-0.06561822444200516,
0.03879554197192192,
0.025266434997320175,
0.043116915971040726,
0.033901065... |
https://github.com/scikit-learn/scikit-learn/issues/23165 | [
"Needs Triage"
] | ⚠️ CI failed on macOS.pylatest_conda_mkl_no_openmp ⚠️
**CI Failed on [macOS.pylatest_conda_mkl_no_openmp](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=41102&view=logs&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2)**
- test_neighbors_metrics[float32-wminkowski]
COMMENT:
Triggered by #23110 that now ... | 23,165 | [
-0.029076531529426575,
0.006562915630638599,
-0.03739602491259575,
-0.053490497171878815,
0.04756970703601837,
0.025808118283748627,
0.0308217853307724,
0.056479692459106445,
-0.029728448018431664,
-0.010045832023024559,
0.01828167401254177,
0.017199933528900146,
-0.013686117716133595,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23162 | [
"module:covariance",
"Needs Investigation"
] | MinCovDet estimation of covariance with strong bias?
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/23161
<div type='discussions-op-text'>
<sup>Originally posted by **hongfei0224** April 20, 2022</sup>
I was playing with MinCovDet (Minimum Covariance Determinant) in sklearn:
[https:/... | 23,162 | [
-0.011406918987631798,
-0.05543265864253044,
0.015457979403436184,
0.03569739684462547,
0.01812557689845562,
0.004086469300091267,
0.013324039056897163,
-0.008458212949335575,
0.005018469411879778,
0.06722760200500488,
0.004205291159451008,
0.04389641433954239,
0.02348068356513977,
-0.0136... |
https://github.com/scikit-learn/scikit-learn/issues/23160 | [
"Documentation"
] | Compare BIRCH and MiniBatchKMeans - unused variable
### Describe the issue linked to the documentation
In the code example of [plot_birch_vs_minibatchkmeans](https://scikit-learn.org/stable/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html) `time_ = time() - t` is defined but not used.
### Suggest a potential... | 23,160 | [
0.0002877733495552093,
-0.09842927753925323,
-0.013713853433728218,
0.013323254883289337,
-0.00811660848557949,
0.0005657269502989948,
0.06028667092323303,
0.009142166003584862,
0.0031302531715482473,
0.026429511606693268,
0.03667411208152771,
0.013704433105885983,
-0.006250111851841211,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23160 | [
"Documentation"
] | Compare BIRCH and MiniBatchKMeans - unused variable
### Describe the issue linked to the documentation
In the code example of [plot_birch_vs_minibatchkmeans](https://scikit-learn.org/stable/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html) `time_ = time() - t` is defined but not used.
### Suggest a potential... | 23,160 | [
-0.004602185450494289,
-0.10200861841440201,
-0.015943830832839012,
0.015073128044605255,
-0.009636971168220043,
-0.00003844136153929867,
0.06417343765497208,
0.004508158192038536,
0.008199043571949005,
0.02776581048965454,
0.03491271287202835,
0.015786340460181236,
-0.008066870272159576,
... |
https://github.com/scikit-learn/scikit-learn/issues/23158 | [
"Question"
] | The score evaluation with "roc_auc" is not the same in GridSearchCV() and roc_auc_score() functon
### Describe the bug
The score evaluation results in GridSearchCV () with refit=roc_auc not eqaul to the score caculated from roc_auc_score() functon.
### Steps/Code to Reproduce
import numpy as np
from matplotlib i... | 23,158 | [
0.005883523728698492,
-0.04353831335902214,
0.025582201778888702,
0.03124198690056801,
0.04225282371044159,
-0.018429094925522804,
-0.0675477385520935,
-0.0045871431939303875,
0.014723988249897957,
-0.002054212847724557,
-0.0161935705691576,
0.027572523802518845,
0.052177321165800095,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23158 | [
"Question"
] | The score evaluation with "roc_auc" is not the same in GridSearchCV() and roc_auc_score() functon
### Describe the bug
The score evaluation results in GridSearchCV () with refit=roc_auc not eqaul to the score caculated from roc_auc_score() functon.
### Steps/Code to Reproduce
import numpy as np
from matplotlib i... | 23,158 | [
0.005883523728698492,
-0.04353831335902214,
0.025582201778888702,
0.03124198690056801,
0.04225282371044159,
-0.018429094925522804,
-0.0675477385520935,
-0.0045871431939303875,
0.014723988249897957,
-0.002054212847724557,
-0.0161935705691576,
0.027572523802518845,
0.052177321165800095,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23158 | [
"Question"
] | The score evaluation with "roc_auc" is not the same in GridSearchCV() and roc_auc_score() functon
### Describe the bug
The score evaluation results in GridSearchCV () with refit=roc_auc not eqaul to the score caculated from roc_auc_score() functon.
### Steps/Code to Reproduce
import numpy as np
from matplotlib i... | 23,158 | [
0.005883523728698492,
-0.04353831335902214,
0.025582201778888702,
0.03124198690056801,
0.04225282371044159,
-0.018429094925522804,
-0.0675477385520935,
-0.0045871431939303875,
0.014723988249897957,
-0.002054212847724557,
-0.0161935705691576,
0.027572523802518845,
0.052177321165800095,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/23156 | [
"New Feature",
"Needs Triage"
] | Getting ETA for model training
### Describe the workflow you want to enable
Getting an estimate time for a model to train or an estimated time remaining before the training is completed.
### Describe your proposed solution
Understand computational resources to get an estimate on time to train. OR
Use the already e... | 23,156 | [
-0.050366632640361786,
0.07496027648448944,
0.01350756548345089,
-0.008175658993422985,
-0.009486369788646698,
-0.02026316709816456,
0.018030337989330292,
0.010723701678216457,
-0.005258822347968817,
0.0008874862687662244,
0.046460747718811035,
0.02889171987771988,
-0.060896746814250946,
0... |
https://github.com/scikit-learn/scikit-learn/issues/23141 | [
"Bug",
"module:cluster"
] | MiniBatchKMeans returns fewer clusters than requested
### Describe the bug
For some samples and requested n_clusters MiniBatchKMeans does not return a proper clustering in terms of the number of clusters and consecutive labels.
The example given below shows, that when requesting 11 clusters the result only consist... | 23,141 | [
0.06040720269083977,
-0.10427885502576828,
-0.017819367349147797,
0.06522341817617416,
0.029502779245376587,
-0.02672729454934597,
0.03119446150958538,
0.0336746871471405,
0.01872147060930729,
0.024978728964924812,
-0.0008759087650105357,
0.02007586508989334,
0.01962895318865776,
-0.020965... |
https://github.com/scikit-learn/scikit-learn/issues/23141 | [
"Bug",
"module:cluster"
] | MiniBatchKMeans returns fewer clusters than requested
### Describe the bug
For some samples and requested n_clusters MiniBatchKMeans does not return a proper clustering in terms of the number of clusters and consecutive labels.
The example given below shows, that when requesting 11 clusters the result only consist... | 23,141 | [
0.06040720269083977,
-0.10427885502576828,
-0.017819367349147797,
0.06522341817617416,
0.029502779245376587,
-0.02672729454934597,
0.03119446150958538,
0.0336746871471405,
0.01872147060930729,
0.024978728964924812,
-0.0008759087650105357,
0.02007586508989334,
0.01962895318865776,
-0.020965... |
https://github.com/scikit-learn/scikit-learn/issues/23141 | [
"Bug",
"module:cluster"
] | MiniBatchKMeans returns fewer clusters than requested
### Describe the bug
For some samples and requested n_clusters MiniBatchKMeans does not return a proper clustering in terms of the number of clusters and consecutive labels.
The example given below shows, that when requesting 11 clusters the result only consist... | 23,141 | [
0.06040720269083977,
-0.10427885502576828,
-0.017819367349147797,
0.06522341817617416,
0.029502779245376587,
-0.02672729454934597,
0.03119446150958538,
0.0336746871471405,
0.01872147060930729,
0.024978728964924812,
-0.0008759087650105357,
0.02007586508989334,
0.01962895318865776,
-0.020965... |
https://github.com/scikit-learn/scikit-learn/issues/23134 | [
"Bug",
"module:isotonic"
] | IsotonicRegression returns Null on normal input
### Describe the bug
calibration model IsotonicRegression returns null on scalar numeric input.
```
calibration_model.predict([[0.95803388], [0.313388]])
Out[29]: array([nan, 1.])
```
### Steps/Code to Reproduce
```
from sklearn.calibration import IsotonicRe... | 23,134 | [
-0.02288644202053547,
-0.01496968138962984,
0.03771738335490227,
-0.009436892345547676,
0.04761669412255287,
-0.025054246187210083,
0.023073630407452583,
0.015286829322576523,
0.0031170700676739216,
0.009416885673999786,
0.008482024073600769,
0.038221828639507294,
0.0018839307595044374,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
https://github.com/scikit-learn/scikit-learn/issues/23132 | [
"New Feature",
"module:calibration",
"Needs Decision - Include Feature"
] | Add PAV algorithm for calibration_curve/reliability diagrams
### Describe the workflow you want to enable
```python
import numpy as np
from sklearn.calibration import calibration_curve
y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
prob_true, ... | 23,132 | [
-0.0358508862555027,
0.0548502542078495,
0.02355937846004963,
-0.012690534815192223,
-0.0018652566941455007,
-0.027876529842615128,
-0.03320256993174553,
-0.018643880262970924,
-0.05521441623568535,
0.04677298665046692,
0.05776239186525345,
0.013958590105175972,
0.0528278611600399,
0.04947... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.