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/22977 | [
"New Feature",
"Needs Triage"
] | SequentialFeatureSelector should have a feature order attribute
### Describe the workflow you want to enable
I want to be able to run Forward Sequential Feature Selector and know the order which it picked the features.
Right now you can only get the indices or a boolean mask of all the features you've selected. Of c... | 22,977 | [
-0.0005681538605131209,
0.04814625903964043,
0.007451535202562809,
-0.0005309868138283491,
0.019717760384082794,
0.010036409832537174,
0.05754336714744568,
-0.030214456841349602,
0.04245660454034805,
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0.05256267264485359,
0.028165314346551895,
0.0331365242600441,
0.058... |
https://github.com/scikit-learn/scikit-learn/issues/22977 | [
"New Feature",
"Needs Triage"
] | SequentialFeatureSelector should have a feature order attribute
### Describe the workflow you want to enable
I want to be able to run Forward Sequential Feature Selector and know the order which it picked the features.
Right now you can only get the indices or a boolean mask of all the features you've selected. Of c... | 22,977 | [
-0.009557222947478294,
0.040051475167274475,
0.00858556292951107,
0.0017442251555621624,
0.019658289849758148,
0.008459504693746567,
0.04541075974702835,
-0.028927497565746307,
0.044018786400556564,
-0.039057038724422455,
0.04760399088263512,
0.04055079072713852,
0.026983505114912987,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/22975 | [
"Build / CI"
] | CI MacOS job pylatest_conda_mkl_no_openmp install fails
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40226&view=logs&jobId=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&t=83107d01-18db-5293-bb0f-49ac2bf2f625 for instance
Fails because of
```
CondaVerific... | 22,975 | [
0.028771746903657913,
0.027803389355540276,
-0.03205182030797005,
-0.05958590656518936,
0.037559494376182556,
0.011212420649826527,
0.004202402196824551,
0.028373420238494873,
-0.0490875206887722,
-0.0014170327922329307,
0.002186024794355035,
0.07013706862926483,
0.006725606508553028,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22975 | [
"Build / CI"
] | CI MacOS job pylatest_conda_mkl_no_openmp install fails
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40226&view=logs&jobId=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&t=83107d01-18db-5293-bb0f-49ac2bf2f625 for instance
Fails because of
```
CondaVerific... | 22,975 | [
0.029117511585354805,
0.028325550258159637,
-0.031846627593040466,
-0.05947062745690346,
0.04311039671301842,
0.013772313483059406,
0.007767884060740471,
0.03280700370669365,
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0.009802557528018951,
0.0630974993109703,
0.0048286207020282745,
0.01749... |
https://github.com/scikit-learn/scikit-learn/issues/22974 | [
"module:decomposition"
] | Variance explained by the components of an pca, zero from a certain moment
### Describe the bug
I'm doing a PCA on data of quite large dimension (about 5000 individuals, about 3000 variables).
The problem presented does not depend on the type of data, I find it with my data but also with synthetically generated... | 22,974 | [
-0.002627232577651739,
-0.0004112923052161932,
-0.003480729879811406,
0.015188385732471943,
0.06396028399467468,
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0.0589672215282917,
0.03174526244401932,
0.024726610630750656,
0.021129921078681946,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22974 | [
"module:decomposition"
] | Variance explained by the components of an pca, zero from a certain moment
### Describe the bug
I'm doing a PCA on data of quite large dimension (about 5000 individuals, about 3000 variables).
The problem presented does not depend on the type of data, I find it with my data but also with synthetically generated... | 22,974 | [
-0.002627232577651739,
-0.0004112923052161932,
-0.003480729879811406,
0.015188385732471943,
0.06396028399467468,
-0.020377647131681442,
-0.0421319380402565,
-0.017857054248452187,
-0.06240926682949066,
0.0589672215282917,
0.03174526244401932,
0.024726610630750656,
0.021129921078681946,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 | [
-0.0077736349776387215,
-0.013134048320353031,
0.030963441357016563,
0.0042046308517456055,
0.09183480590581894,
-0.0066026546992361546,
0.017716538161039352,
0.008722100406885147,
-0.012646103277802467,
0.008768223226070404,
-0.01789340004324913,
-0.015388596802949905,
0.019169669598340988,... |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 | [
-0.0077736349776387215,
-0.013134048320353031,
0.030963441357016563,
0.0042046308517456055,
0.09183480590581894,
-0.0066026546992361546,
0.017716538161039352,
0.008722100406885147,
-0.012646103277802467,
0.008768223226070404,
-0.01789340004324913,
-0.015388596802949905,
0.019169669598340988,... |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 | [
-0.0077736349776387215,
-0.013134048320353031,
0.030963441357016563,
0.0042046308517456055,
0.09183480590581894,
-0.0066026546992361546,
0.017716538161039352,
0.008722100406885147,
-0.012646103277802467,
0.008768223226070404,
-0.01789340004324913,
-0.015388596802949905,
0.019169669598340988,... |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 | [
-0.0077736349776387215,
-0.013134048320353031,
0.030963441357016563,
0.0042046308517456055,
0.09183480590581894,
-0.0066026546992361546,
0.017716538161039352,
0.008722100406885147,
-0.012646103277802467,
0.008768223226070404,
-0.01789340004324913,
-0.015388596802949905,
0.019169669598340988,... |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 | [
-0.0486314557492733,
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0.047849804162979126,
0.01358285266906023,
0.002711889101192355,
0.022831151261925697,
0.029151152819395065,
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0.006832899525761604,
-0.009074529632925987,
0.020495327189564705,
0.036751966923475266,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 | [
-0.05937650054693222,
-0.07930316030979156,
-0.002669570967555046,
0.048283278942108154,
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0.03321314975619316,
0.030734289437532425,
-... |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 | [
-0.06089434772729874,
-0.07355444878339767,
0.004258878994733095,
0.03067729063332081,
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0.012023691087961197,
0.006888074334710836,
0.028009112924337387,
0.01701415702700615,
0.003468... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 | [
-0.07601676881313324,
-0.036724064499139786,
0.04994751513004303,
0.014503583312034607,
0.04528592899441719,
-0.00835928414016962,
0.0010704252636060119,
0.012434126809239388,
0.052571699023246765,
-0.05245717242360115,
-0.024977026507258415,
0.0370028056204319,
0.002154536312445998,
0.115... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
-0.032137345522642136,
0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
-0.032137345522642136,
0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
-0.032137345522642136,
0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
-0.032137345522642136,
0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
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0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 | [
0.012929766438901424,
0.04792946204543114,
0.03311290591955185,
0.020519733428955078,
0.05857459083199501,
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0.057385120540857315,
0.06928875297307968,
0.020113544538617134,
0.01611730456352234,
0.03397265449166298,
0.00566300842911005,
0.03907361999154091,
-0.03752274... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 | [
0.03051779232919216,
0.014481371268630028,
0.0033410789910703897,
0.038541529327631,
0.013526244089007378,
0.007984483614563942,
0.0181830283254385,
-0.0022839016746729612,
-0.01273451279848814,
0.010911577381193638,
0.048805564641952515,
0.01914023421704769,
0.04499664530158043,
0.0365925... |
https://github.com/scikit-learn/scikit-learn/issues/22944 | [
"Needs Triage"
] | AttributeError when use ‘CountVectorizer‘ with ‘preprocessor ’
I define a preprocessing function like:
def clean_parens(text):
text = re.sub("=","e",text)
return text
Then, word vector extraction is defined as：
SelfDicCvB = CountVectorizer(input='filename',vocabulary=voc['word'].tolist(),
... | 22,944 | [
0.0289901252835989,
0.061577070504426956,
0.0069575682282447815,
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0.0320320725440979,
0.01596611738204956,
0.008066670969128609,
0.011981320567429066,
-0.04897424578666687,
-0.03941360488533974,
0.01643313840031624,
-0.0028878117445856333,
-0.003734075464308262,
0.0330... |
https://github.com/scikit-learn/scikit-learn/issues/22939 | [
"New Feature",
"module:preprocessing"
] | sklearn.preprocessing.LabelEncoder: new feature to manage unknown value in `transform()` method
### Describe the workflow you want to enable
Hi, I'm working with `from sklearn.preprocessing import LabelEncoder` and I would find interesting (and useful) to have an option to not get an error in case of when using the... | 22,939 | [
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0.07154138386249542,
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0.023644445464015007,
0.04436285048723221,
0.02702825516462326,
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0.0011956426315009594,
0.045852839946746826,
0.06156201288104057,
-0.02347506769001484,
0.0440... |
https://github.com/scikit-learn/scikit-learn/issues/22939 | [
"New Feature",
"module:preprocessing"
] | sklearn.preprocessing.LabelEncoder: new feature to manage unknown value in `transform()` method
### Describe the workflow you want to enable
Hi, I'm working with `from sklearn.preprocessing import LabelEncoder` and I would find interesting (and useful) to have an option to not get an error in case of when using the... | 22,939 | [
-0.000932344002649188,
0.07154138386249542,
0.01738247089087963,
0.0008564477902837098,
0.10487967729568481,
0.023644445464015007,
0.04436285048723221,
0.02702825516462326,
-0.009920574724674225,
0.0011956426315009594,
0.045852839946746826,
0.06156201288104057,
-0.02347506769001484,
0.0440... |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
It looks like the URL changed. Here is an example of the URL to one of the PRS:
https://output.circle-artifacts.com/output/job/... | 22,931 | [
0.0031617572531104088,
-0.018379483371973038,
-0.02551114559173584,
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0.02894148789346218,
0.017248565331101418,
0.01391240581870079,
-0.08379644900560379,
0.03077850490808487,
0.015186864882707596,
0.029320694506168365,
0.003996311686933041,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
Cross linking the issue opened upstream : https://github.com/larsoner/circleci-artifacts-redirector/issues/8 | 22,931 | [
-0.0037026323843747377,
-0.02361901104450226,
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0.0032193236984312534,
0.015758410096168518,
0.03803925961256027,
0.0022714142687618732,
... |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
The link has been fixed upstream but only for the action for now (https://github.com/larsoner/circleci-artifacts-redirector-action... | 22,931 | [
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0.02939750626683235,
0.00479343393817544,
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0.041095003485679626,
0.05842897295951843,
-0.0221867635846138,
0.085... |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
Closing via #22991 | 22,931 | [
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0.015398416668176651,
0.011001678183674812,
0.01790592074394226,
0.019668536260724068,
0... |
https://github.com/scikit-learn/scikit-learn/issues/22930 | [
"New Feature",
"module:metrics"
] | roc_auc_score, support `average=None` for multiclass labels
### Describe the workflow you want to enable
Currently as of version 1.0.2 If you try to calculate the roc_auc_score for a multiclass `y_true` with `average=None` you get an error:
````
import sklearn.metrics as metrics
import numpy as np
y_true =... | 22,930 | [
-0.03245808929204941,
0.03135211765766144,
0.017034592106938362,
0.02019251137971878,
0.03619825094938278,
-0.0015671737492084503,
0.03868703171610832,
-0.0018153456039726734,
-0.004261936526745558,
-0.025314996019005775,
0.024963323026895523,
0.00005355554094421677,
-0.014779431745409966,
... |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 | [
0.018619291484355927,
-0.028145423159003258,
0.03177948296070099,
-0.019187437370419502,
0.03696916252374649,
0.010017013177275658,
0.027359304949641228,
0.0151320556178689,
0.05950259789824486,
0.006588248535990715,
0.03208281844854355,
0.05318746715784073,
0.05446688085794449,
0.02165882... |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 | [
0.01920429617166519,
-0.036379385739564896,
0.03255770727992058,
-0.027622921392321587,
0.03501947969198227,
0.007091294042766094,
0.028635401278734207,
0.014205950312316418,
0.0656660720705986,
0.010739373974502087,
0.03875913470983505,
0.061483751982450485,
0.05427396297454834,
0.0150629... |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 | [
-0.0026997255627065897,
-0.03215426206588745,
0.03139006346464157,
-0.014010784216225147,
0.019004948437213898,
0.009250638075172901,
0.016787925735116005,
0.022469621151685715,
0.059308309108018875,
0.009518028236925602,
0.05980910360813141,
0.03240448608994484,
0.0424468070268631,
0.0034... |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 | [
0.027178186923265457,
-0.040380626916885376,
0.0350126288831234,
-0.021922780200839043,
0.034664034843444824,
0.006451825611293316,
0.02330770157277584,
0.012472054921090603,
0.07009995728731155,
0.011367438361048698,
0.03939167037606239,
0.05525362119078636,
0.052875131368637085,
0.015171... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 | [
-0.010050047188997269,
0.025601280853152275,
0.01925291121006012,
-0.011097927577793598,
0.1202964335680008,
0.00844013411551714,
0.02533959224820137,
0.041464947164058685,
0.015985922887921333,
0.01253355573862791,
-0.014733019284904003,
0.06535647064447403,
-0.06806159764528275,
0.012343... |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 | [
0.004745637997984886,
-0.03125923126935959,
0.022753974422812462,
0.03246752917766571,
0.08961024135351181,
-0.035727668553590775,
0.012653445824980736,
0.004703112877905369,
-0.011821163818240166,
0.006100270431488752,
0.06324648857116699,
0.020300282165408134,
-0.016739124432206154,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 | [
0.004745637997984886,
-0.03125923126935959,
0.022753974422812462,
0.03246752917766571,
0.08961024135351181,
-0.035727668553590775,
0.012653445824980736,
0.004703112877905369,
-0.011821163818240166,
0.006100270431488752,
0.06324648857116699,
0.020300282165408134,
-0.016739124432206154,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 | [
0.004745637997984886,
-0.03125923126935959,
0.022753974422812462,
0.03246752917766571,
0.08961024135351181,
-0.035727668553590775,
0.012653445824980736,
0.004703112877905369,
-0.011821163818240166,
0.006100270431488752,
0.06324648857116699,
0.020300282165408134,
-0.016739124432206154,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 | [
0.004745637997984886,
-0.03125923126935959,
0.022753974422812462,
0.03246752917766571,
0.08961024135351181,
-0.035727668553590775,
0.012653445824980736,
0.004703112877905369,
-0.011821163818240166,
0.006100270431488752,
0.06324648857116699,
0.020300282165408134,
-0.016739124432206154,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22907 | [
"Bug"
] | UserWarning is thrown when calling `HistGradientBoostingRegressor.fit` while specifying `scoring` argument
### Describe the bug
`UserWarning: X does not have valid feature names` is thrown when calling `HistGradientBoostingRegressor.fit` using `pandas.DataFrame`.
The regressor was constructed by specifying `scorin... | 22,907 | [
0.009431798942387104,
0.015039632096886635,
0.036750130355358124,
-0.05411543697118759,
0.1102990135550499,
-0.006548822857439518,
0.025189409032464027,
0.017719736322760582,
-0.002182060619816184,
0.02413487248122692,
0.04184609279036522,
0.013044451363384724,
0.0028917090967297554,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/22904 | [
"Bug",
"Needs Triage"
] | Pytest plugins in a non-top-level conftest is no longer supported
### Describe the bug
When running pytest, an issue pops up due to https://github.com/scikit-learn/scikit-learn/blob/7116165f493998cde7989a29458f36bdfb0a9ab5/sklearn/conftest.py#L24-L25
I was able to fix this by moving the offending line to `scikit... | 22,904 | [
-0.002733647357672453,
-0.006798787973821163,
-0.009713911451399326,
-0.013850772753357887,
0.035859450697898865,
0.0018052192172035575,
0.06506083905696869,
0.05206939950585365,
-0.006472546607255936,
-0.007281120400875807,
-0.030213650315999985,
0.03025631420314312,
-0.03286868706345558,
... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
0.05046156421303749,
-0.0008620767621323466,
-0.05303877592086792,
-0.018777882680296898,
-0.027679692953824997,
0.04777989163994789,
-0.003907065372914076,
-0.07766923308372498,
-0.05809446796774864,
0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
0.0003015365800820291,
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0.05662613362073898,
0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 | [
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0.07435235381126404,
0.00541668850928545,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/22890 | [
"New Feature",
"Needs Triage"
] | KMeans support for complex numbers
### Describe the workflow you want to enable
It would be nice with K-means clustering (`KMeans`) for complex-valued vectors. Today complex-valued vectors in the `fit` method throw a `ValueError`.
### Describe your proposed solution
Each element of a complex vector is associated wi... | 22,890 | [
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0.035063304007053375,
-0.033969249576330185,
-0.02838529832661152,
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0.025147223845124245,
0.044006943702697754,
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0.00... |
https://github.com/scikit-learn/scikit-learn/issues/22885 | [
"Bug",
"module:model_selection"
] | Modified order of operations in _approximate_mode() changes function behavior
### Describe the bug
In issue [#20774](https://github.com/scikit-learn/scikit-learn/issues/20774) and subsequent [PR #20904](https://github.com/scikit-learn/scikit-learn/pull/20904), the order of operations in the calculation of `continuo... | 22,885 | [
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0.03589289262890816,
... |
https://github.com/scikit-learn/scikit-learn/issues/22884 | [
"Bug",
"Needs Triage"
] | SGDRegressor intercept exactly around half of the value it should be
### Describe the bug
It seems that the recent sklearn.SGDRegressor produces intercepts half of the real values.
### Steps/Code to Reproduce
import numpy as np
from sklearn.linear_model import SGDRegressor, LinearRegression
x = np.random.randn(1... | 22,884 | [
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-0.0608130544424057,
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0.011564449407160282,
0.040676482021808624,
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0.0346... |
https://github.com/scikit-learn/scikit-learn/issues/22881 | [
"help wanted",
"Hard",
"module:test-suite",
"Meta-issue",
"float32"
] | Improve tests to make them run on variously typed data using the `global_dtype` fixture
## Context: the new `global_dtype` fixture and `SKLEARN_RUN_FLOAT32_TESTS` environment variable
Introduction of low-level computational routines for 32bit motivated an extension of tests to run them on 32bit.
In this regards,... | 22,881 | [
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-0.040778160095214844,
0.026359522715210915,
-0.010787741281092167,
-0.012147189117968082,
0.02... |
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