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/26321 | [
"Bug",
"module:covariance"
] | Duality gap computation in covariance.GraphicalLasso yields negative values.
### Describe the bug
The computation of the duality gap in `_dual_gap(emp_cov, precision_, alpha)` of `GraphicalLasso` uses the definition from `Duchi et al., 2012`.
However, their duality gap is expressed given a _feasible_ dual variabl... | 26,321 | [
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0.003574835602194071,
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0.0524630397558212... |
https://github.com/scikit-learn/scikit-learn/issues/26310 | [
"New Feature",
"Needs Triage"
] | SimpleImputer.strategy = 'random'
### Describe the workflow you want to enable
SimpleImputer.strategy = 'random' will randomly choose a non-NaN value.
### Describe your proposed solution
Find a list of non-NaN indices. Randomly pick one index for each NaN.
### Describe alternatives you've considered, if re... | 26,310 | [
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-0... |
https://github.com/scikit-learn/scikit-learn/issues/26308 | [
"Moderate",
"help wanted",
"module:preprocessing",
"Refactor"
] | Use scipy.stats.yeojohnson PowerTransformer
Inside `PowerTransformer`, we should use [`scipy.stats.yeojohnson`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html#scipy-stats-yeojohnson) instead of our own implementation.
`scipy.stats.yeojohnson` was release with scipy 1.2.0. With PR #... | 26,308 | [
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0.03546399623155594,
0.022... |
https://github.com/scikit-learn/scikit-learn/issues/26308 | [
"Moderate",
"help wanted",
"module:preprocessing",
"Refactor"
] | Use scipy.stats.yeojohnson PowerTransformer
Inside `PowerTransformer`, we should use [`scipy.stats.yeojohnson`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html#scipy-stats-yeojohnson) instead of our own implementation.
`scipy.stats.yeojohnson` was release with scipy 1.2.0. With PR #... | 26,308 | [
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0.02265... |
https://github.com/scikit-learn/scikit-learn/issues/26308 | [
"Moderate",
"help wanted",
"module:preprocessing",
"Refactor"
] | Use scipy.stats.yeojohnson PowerTransformer
Inside `PowerTransformer`, we should use [`scipy.stats.yeojohnson`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html#scipy-stats-yeojohnson) instead of our own implementation.
`scipy.stats.yeojohnson` was release with scipy 1.2.0. With PR #... | 26,308 | [
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0.02363... |
https://github.com/scikit-learn/scikit-learn/issues/26308 | [
"Moderate",
"help wanted",
"module:preprocessing",
"Refactor"
] | Use scipy.stats.yeojohnson PowerTransformer
Inside `PowerTransformer`, we should use [`scipy.stats.yeojohnson`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html#scipy-stats-yeojohnson) instead of our own implementation.
`scipy.stats.yeojohnson` was release with scipy 1.2.0. With PR #... | 26,308 | [
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0.039207592606544495,
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0.03886621072888374,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26308 | [
"Moderate",
"help wanted",
"module:preprocessing",
"Refactor"
] | Use scipy.stats.yeojohnson PowerTransformer
Inside `PowerTransformer`, we should use [`scipy.stats.yeojohnson`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html#scipy-stats-yeojohnson) instead of our own implementation.
`scipy.stats.yeojohnson` was release with scipy 1.2.0. With PR #... | 26,308 | [
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0.039184849709272385,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
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-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
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-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26307 | [
"Bug",
"module:neighbors",
"Needs Reproducible Code",
"Needs Investigation",
"upstream bug"
] | KNeighborsClassifier OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata
### Describe the bug
when i run flask app on 56 core cpus,it show this warning and app exit, when i change \site-packages\joblib\externals\loky\backend\context.py can solved
```py
os_cpu_count = m... | 26,307 | [
0.0037935245782136917,
-0.03512827679514885,
-0.025309454649686813,
0.016315985471010208,
0.06248285993933678,
0.02775157056748867,
0.04268361255526543,
0.03274240344762802,
0.02416985295712948,
-0.002972936723381281,
-0.005198400933295488,
0.07940763980150223,
-0.018206065520644188,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26306 | [
"Bug",
"module:compose",
"Pandas compatibility"
] | `ColumnTransformer.set_output` ignores the `remainder` if it's an estimator
### Describe the bug
When using `set_output` on a `ColumnTransformer`, it sets the output to its sub-transformers but it ignores the transformer defined in `remainder`.
This issue causes the following `if` to fail when gathering the resu... | 26,306 | [
0.028073688969016075,
0.056467290967702866,
0.0227996613830328,
-0.014412702061235905,
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0.053145330399274826,
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0.020864998921751976,
0.03664141893386841,
0.03572371229529381,
0.00099... |
https://github.com/scikit-learn/scikit-learn/issues/26305 | [
"Bug",
"Needs Triage"
] | Pandas DataFrame dtypes aren't preserved
### Describe the bug
When the input is a Pandas DataFrame with multiple dtypes in the columns and the output is also Pandas, the dtypes aren't preserved but cast to a common type.
I believe this happens because `check_array` does this cast:
https://github.com/scikit-lear... | 26,305 | [
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0.022388992831110954,
0.055778730660676956,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
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-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
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0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26303 | [
"Bug",
"help wanted",
"module:preprocessing"
] | PowerTransformer fails with unhelpful stack trace with all-nan feature and method='box-cox'
### Describe the bug
`PowerTransformer("box-cox").fit(x)` throws a difficult-to-debug error if x contains an all-nan column.
### Steps/Code to Reproduce
```python
import pandas as pd
import numpy as np
from sklea... | 26,303 | [
-0.030223006382584572,
0.017115909606218338,
0.04426250606775284,
-0.025236839428544044,
0.0901876911520958,
-0.0014321383787319064,
0.056090645492076874,
0.03368267044425011,
-0.025974426418542862,
0.0030186621006578207,
0.04864388331770897,
-0.01674177311360836,
0.05125850811600685,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/26301 | [
"New Feature",
"Needs Triage"
] | Support custom callable in SimpleImputer
### Describe the workflow you want to enable
Is it possible to support any aggregation function as `strategy` in `SimpleImputer`?
The only thing we will need to check is that function returns one non-null value (aggregating) consistently (not random).
### Describe your p... | 26,301 | [
-0.00018864560115616769,
0.007021908648312092,
-0.0017360347555950284,
-0.06540346890687943,
0.015838615596294403,
-0.01786763221025467,
0.050628770142793655,
-0.009819785133004189,
-0.006768610794097185,
0.003587368642911315,
-0.00502079026773572,
0.0036833398044109344,
0.010641301982104778... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.0206023920327425,
0.02811145782470703,
0.014472742564976215,
-0.025873007252812386,
0.035671669989824295,
0.036726828664541245,
0.039823416620492935,
0.03794461488723755,
-0.06338618695735931,
-0.05570855364203453,
-0.0017657388234511018,
0.03736134618520737,
-0.034677427262067795,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.02229064330458641,
0.03243229165673256,
0.01922748051583767,
-0.01865861378610134,
0.03920145705342293,
0.03375602141022682,
0.0267998855561018,
0.03524971380829811,
-0.060281217098236084,
-0.062126196920871735,
-0.01054859533905983,
0.03961607441306114,
-0.035001497715711594,
0.0084814... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.01277412474155426,
0.03937156870961189,
0.012811453081667423,
-0.040335334837436676,
0.03167177736759186,
0.03411169350147247,
0.029864266514778137,
0.0237868744879961,
-0.047015514224767685,
-0.06591970473527908,
0.005714117083698511,
0.0390428751707077,
-0.03844553977251053,
0.0387510... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.04653468355536461,
0.015140382573008537,
0.019798357039690018,
-0.046520527452230453,
0.02159779518842697,
0.027872541919350624,
0.07115922123193741,
0.025679683312773705,
-0.019456075504422188,
-0.06271157413721085,
0.00601782463490963,
0.04840432479977608,
-0.01999439299106598,
0.0616... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.025196576490998268,
0.029972510412335396,
0.013088707812130451,
-0.024219077080488205,
0.03506295382976532,
0.037187233567237854,
0.03976703807711601,
0.034228213131427765,
-0.06310353428125381,
-0.05552366375923157,
-0.0046873860992491245,
0.03743081912398338,
-0.037004467099905014,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.025004612281918526,
0.01937546767294407,
0.029164966195821762,
-0.02131878025829792,
0.03724220395088196,
0.028392933309078217,
0.02085014246404171,
0.029365157708525658,
-0.06693194806575775,
-0.05336674302816391,
0.0045897988602519035,
0.04163115844130516,
-0.02255059964954853,
0.0104... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
-0.014324627816677094,
0.06934521347284317,
0.019630400463938713,
-0.0011331174755468965,
0.020587963983416557,
0.02944782003760338,
0.00625782273709774,
0.025620706379413605,
-0.058027807623147964,
-0.10030069947242737,
0.007323544938117266,
0.03775646910071373,
-0.006528383120894432,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
0.0022422699257731438,
0.0412573404610157,
0.044589657336473465,
-0.049838606268167496,
0.08749350160360336,
0.0395473912358284,
0.07087890058755875,
0.015973640605807304,
-0.024295534938573837,
-0.06232094764709473,
0.0027526207268238068,
0.04528526961803436,
0.046948231756687164,
0.03119... |
https://github.com/scikit-learn/scikit-learn/issues/26296 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Add support for `dtype` arg in `MultiLabelBinarizer`
### Describe the workflow you want to enable
Similar to other transformers (e.g., `OneHotEncoder`), support an arg `dtype` in `MultiLabelBinarizer`.
### Describe your proposed solution
Add an arg `dtype` in `MultiLabelBinarizer`.
### Describe alternatives you've... | 26,296 | [
0.002663112012669444,
0.048243988305330276,
0.05141683667898178,
-0.03381667286157608,
0.08127987384796143,
0.06149233877658844,
0.05423096567392349,
0.03907104209065437,
-0.02336118556559086,
-0.06738048791885376,
-0.006720287725329399,
0.04722139984369278,
0.00940603669732809,
0.01058794... |
https://github.com/scikit-learn/scikit-learn/issues/26295 | [
"module:tree",
"cython"
] | [MAINT] Remove deprecated implementation of properties in extension class in `tree/`
I was using some of the sklearn/tree Cython code and noticed my IDE raised an issue stating that the current implementation of the class properties is deprecated: https://github.com/scikit-learn/scikit-learn/blob/188267212cb5459bfba94... | 26,295 | [
0.005645852070301771,
0.04729679226875305,
-0.016121840104460716,
-0.012551275081932545,
0.027336666360497475,
0.008266609162092209,
-0.049714989960193634,
-0.017480017617344856,
-0.0706821084022522,
-0.00930628553032875,
0.04229031503200531,
0.0556216798722744,
-0.0317547582089901,
-0.017... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26292 | [
"New Feature",
"Moderate",
"help wanted"
] | Add support for bools in `SimpleImputer`
### Describe the workflow you want to enable
Suppose you wanna impute a bool array. Because it has NaNs, it's gonna be of dtype float and work fine:
```pycon
>>> np.asarray([[True, False, np.nan]]).dtype
dtype('float64')
```
However, now suppose that the value you pas... | 26,292 | [
-0.0017090836772695184,
-0.029992321506142616,
0.021632898598909378,
-0.05649121478199959,
0.05425013601779938,
0.013018177822232246,
0.07496053725481033,
0.01904962956905365,
0.041766904294490814,
-0.014967907220125198,
-0.0032189814373850822,
0.04612751305103302,
0.015773454681038857,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26290 | [
"cython"
] | Investigate incompatible signatures of `scipy.linalg.cython_blas.dasum`
### Describe your issue.
In MNE-Python we run a pip-pre job with thhe latest `scipy-wheels-nightly` builds for NumPy/SciPy/sklearn. The latest `1.11.0.dev0+1956.7c74503` SciPy + `1.3.dev0` sklearn pip-pre wheel combination appears to be buggy:
... | 26,290 | [
-0.009664308279752731,
-0.0037687409203499556,
-0.009011714719235897,
-0.025364713743329048,
0.03897634521126747,
0.01943378895521164,
0.01661330834031105,
0.019239122048020363,
-0.034745339304208755,
-0.0395890511572361,
0.04717273265123367,
0.06230904161930084,
-0.013607856817543507,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26288 | [
"Bug",
"Needs Triage"
] | kMeans stopped working with numpy 1.24.2
### Describe the bug
following [this](https://github.com/scikit-learn/scikit-learn/issues/22689) and [this](https://github.com/scikit-learn/scikit-learn/issues/22683) threads and this [SO question](https://stackoverflow.com/questions/71352354/sklearn-kmeans-is-not-working-as... | 26,288 | [
-0.0018487799679860473,
0.030518269166350365,
-0.012316230684518814,
-0.001649053767323494,
0.06436404585838318,
-0.003312020329758525,
0.03446957841515541,
0.07168606668710709,
0.0016971378354355693,
-0.015522054396569729,
0.024566980078816414,
0.08415708690881729,
-0.025855833664536476,
... |
https://github.com/scikit-learn/scikit-learn/issues/26285 | [
"Bug",
"Needs Triage"
] | radius_neighbors incorrect behavior
### Describe the bug
The `radius_neighbors` function is intended to return neighbors within a given distance. However, the following example seems to behave differently.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.neighbors import NearestNeighbors
... | 26,285 | [
0.03336643427610397,
-0.06323008984327316,
0.0037976347375661135,
0.056673526763916016,
0.004726166371256113,
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0.056919243186712265,
0.02327074483036995,
0.03213347867131233,
-0.005608712323009968,
-0.01682984083890915,
-0.016995638608932495,
0.007513870019465685,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26280 | [
"Bug",
"module:decomposition"
] | KernelPCA inverse transform behaves unexpectly.
### Describe the bug
Hi!
we wanted to use inverse transform in kernel PCA. There is a parameter gamma, which is defined as 1/num_features if gamma=None. However if gamma is provided as gamma=1/num_features, the resultant inverse transform results is different. Why is t... | 26,280 | [
0.03153973072767258,
-0.03162671998143196,
0.018748780712485313,
0.030353065580129623,
0.026292283087968826,
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-0.013123768381774426,
0.00011204320617252961,
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0.03292844444513321,
-0.012552687898278236,
0.013293513096868992,
0.06244564428925514,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26280 | [
"Bug",
"module:decomposition"
] | KernelPCA inverse transform behaves unexpectly.
### Describe the bug
Hi!
we wanted to use inverse transform in kernel PCA. There is a parameter gamma, which is defined as 1/num_features if gamma=None. However if gamma is provided as gamma=1/num_features, the resultant inverse transform results is different. Why is t... | 26,280 | [
0.03153973072767258,
-0.03162671998143196,
0.018748780712485313,
0.030353065580129623,
0.026292283087968826,
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-0.013123768381774426,
0.00011204320617252961,
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0.03292844444513321,
-0.012552687898278236,
0.013293513096868992,
0.06244564428925514,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26280 | [
"Bug",
"module:decomposition"
] | KernelPCA inverse transform behaves unexpectly.
### Describe the bug
Hi!
we wanted to use inverse transform in kernel PCA. There is a parameter gamma, which is defined as 1/num_features if gamma=None. However if gamma is provided as gamma=1/num_features, the resultant inverse transform results is different. Why is t... | 26,280 | [
0.03153973072767258,
-0.03162671998143196,
0.018748780712485313,
0.030353065580129623,
0.026292283087968826,
-0.014392457902431488,
-0.013123768381774426,
0.00011204320617252961,
-0.04709814488887787,
0.03292844444513321,
-0.012552687898278236,
0.013293513096868992,
0.06244564428925514,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26280 | [
"Bug",
"module:decomposition"
] | KernelPCA inverse transform behaves unexpectly.
### Describe the bug
Hi!
we wanted to use inverse transform in kernel PCA. There is a parameter gamma, which is defined as 1/num_features if gamma=None. However if gamma is provided as gamma=1/num_features, the resultant inverse transform results is different. Why is t... | 26,280 | [
0.03153973072767258,
-0.03162671998143196,
0.018748780712485313,
0.030353065580129623,
0.026292283087968826,
-0.014392457902431488,
-0.013123768381774426,
0.00011204320617252961,
-0.04709814488887787,
0.03292844444513321,
-0.012552687898278236,
0.013293513096868992,
0.06244564428925514,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26277 | [
"New Feature",
"module:ensemble"
] | Support `max_bins > 255` in Hist-GBDT estimators and categorical features with high cardinality
As originally sketched in https://github.com/scikit-learn/scikit-learn/pull/26268#issuecomment-1520504489 there might be a way to enable support for arbitrary high values of `max_bins` for both categorical and numerical fea... | 26,277 | [
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0.06796552240848541,
0.005792665760964155,
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0.0031471566762775183,
0.06492394953966141,
0.020012395456433296,
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0.030918922275304794,
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-0.07712289690971375,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26277 | [
"New Feature",
"module:ensemble"
] | Support `max_bins > 255` in Hist-GBDT estimators and categorical features with high cardinality
As originally sketched in https://github.com/scikit-learn/scikit-learn/pull/26268#issuecomment-1520504489 there might be a way to enable support for arbitrary high values of `max_bins` for both categorical and numerical fea... | 26,277 | [
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0.01822195015847683,
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-0.07695507258176804,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26277 | [
"New Feature",
"module:ensemble"
] | Support `max_bins > 255` in Hist-GBDT estimators and categorical features with high cardinality
As originally sketched in https://github.com/scikit-learn/scikit-learn/pull/26268#issuecomment-1520504489 there might be a way to enable support for arbitrary high values of `max_bins` for both categorical and numerical fea... | 26,277 | [
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0.008631497621536255,
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-0.07732430845499039,
0.064504... |
https://github.com/scikit-learn/scikit-learn/issues/26277 | [
"New Feature",
"module:ensemble"
] | Support `max_bins > 255` in Hist-GBDT estimators and categorical features with high cardinality
As originally sketched in https://github.com/scikit-learn/scikit-learn/pull/26268#issuecomment-1520504489 there might be a way to enable support for arbitrary high values of `max_bins` for both categorical and numerical fea... | 26,277 | [
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0.01819184422492981,
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0.085... |
https://github.com/scikit-learn/scikit-learn/issues/26270 | [
"Bug",
"Easy"
] | Unhelpful error message when running a classifier on a regression outcome
### Describe the bug
When running a classifier on a regression outcome, we get a really unhelpful error message:
### Steps/Code to Reproduce
```python
In [2]: from sklearn import linear_model, datasets
In [3]: X, y = datasets.make_regre... | 26,270 | [
0.015720415860414505,
0.025195246562361717,
0.049286939203739166,
0.0031285537406802177,
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0.06728427112102509,
0.04805881157517433,
0.026051636785268784,
-0.001113120699301362,
0.03326227143406868,
0.0586991123855114,
-0.017306610941886902,
0.0230556... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.03909381851553917,
-0.029743539169430733,
-0.003729653311893344,
0.013517381623387337,
0.04838171973824501,
-0.0023666727356612682,
0.028971387073397636,
0.02549760416150093,
-0.0038867522962391376,
-0.009666440077126026,
0.040553364902734756,
-0.007285224739462137,
0.02276526391506195,
... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.036112744361162186,
-0.0376628115773201,
-0.005585532635450363,
0.007866233587265015,
0.049249742180109024,
0.0025600632652640343,
0.03050249069929123,
0.024126283824443817,
0.005112459417432547,
-0.012642213143408298,
0.03394579142332077,
-0.00788116455078125,
0.017958855256438255,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.037789493799209595,
-0.02478150464594364,
-0.00634740199893713,
0.009992175735533237,
0.047681912779808044,
0.0013081805082038045,
0.02909720502793789,
0.027951285243034363,
-0.001439492916688323,
-0.003763098269701004,
0.034725628793239594,
-0.006046260707080364,
0.022381052374839783,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.03108513355255127,
-0.023151583969593048,
-0.009023299440741539,
0.0041753146797418594,
0.05702533200383186,
0.004483528435230255,
0.022094374522566795,
0.027006305754184723,
0.004911020398139954,
-0.02055828645825386,
0.036617930978536606,
-0.0004329115618020296,
0.014264746569097042,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.03728656843304634,
-0.03643733635544777,
-0.00821597408503294,
0.012047054246068,
0.04880880191922188,
0.0028307822067290545,
0.02985982596874237,
0.026927335187792778,
0.00018806698790285736,
-0.008810632862150669,
0.03675973787903786,
-0.005896751303225756,
0.021201759576797485,
0.0255... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.038266852498054504,
-0.03543414548039436,
-0.007832980714738369,
0.010466025210916996,
0.04767974838614464,
0.0006002506706863642,
0.030119631439447403,
0.02914857491850853,
-0.001353553612716496,
-0.008048785850405693,
0.037989918142557144,
-0.006840941496193409,
0.01948952116072178,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.04029250517487526,
-0.02976122684776783,
-0.0056688436307013035,
0.013002007268369198,
0.04825204983353615,
0.0009588304092176259,
0.030181249603629112,
0.0331050269305706,
-0.005052164196968079,
-0.008446638472378254,
0.030436990782618523,
-0.007378069218248129,
0.020035481080412865,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26265 | [
"Documentation",
"Enhancement"
] | sklearn.tree.export_text failing when feature_names supplied
folks, I'm not sure why this works for
```py
import sklearn.tree
print(my_feature_names)
['0' '0 trump' '0 trump versus' ... 'zur' 'zur ckhalten' 'zur ckhalten muss']
tree.export_graphviz(clf, out_file=None, max_depth=4, feature_names=my_feature_names... | 26,265 | [
0.03417353332042694,
-0.03265347331762314,
-0.008426398038864136,
0.013407435268163681,
0.04814949631690979,
0.0005704304203391075,
0.022145187482237816,
0.023711465299129486,
-0.008459675125777721,
-0.007525435648858547,
0.03731608763337135,
-0.006823705043643713,
0.021426642313599586,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26248 | [
"New Feature"
] | Add sample_weight parameter to OneHotEncoder.fit(...)
### Describe the workflow you want to enable
I have a dataset with huge number of duplicates - so in order to speed-up learning process I prefer to remove these duplicates and pass `sample_weight=duplicate_counts` to the `fit(..)` method of estimator. I override... | 26,248 | [
-0.044385876506567,
0.0893617570400238,
0.02562936581671238,
-0.031879521906375885,
0.05095620080828667,
0.01580681838095188,
0.025726817548274994,
0.04560788348317146,
-0.0415344201028347,
0.018934179097414017,
0.06470983475446701,
0.04156707227230072,
0.026796743273735046,
0.046464487910... |
https://github.com/scikit-learn/scikit-learn/issues/26248 | [
"New Feature"
] | Add sample_weight parameter to OneHotEncoder.fit(...)
### Describe the workflow you want to enable
I have a dataset with huge number of duplicates - so in order to speed-up learning process I prefer to remove these duplicates and pass `sample_weight=duplicate_counts` to the `fit(..)` method of estimator. I override... | 26,248 | [
-0.04271763190627098,
0.09360331296920776,
0.02141571417450905,
-0.030115455389022827,
0.04817817360162735,
0.018364347517490387,
0.019964292645454407,
0.048365041613578796,
-0.061633411794900894,
0.015404945239424706,
0.04917628690600395,
0.029927486553788185,
0.02532653696835041,
0.04587... |
https://github.com/scikit-learn/scikit-learn/issues/26248 | [
"New Feature"
] | Add sample_weight parameter to OneHotEncoder.fit(...)
### Describe the workflow you want to enable
I have a dataset with huge number of duplicates - so in order to speed-up learning process I prefer to remove these duplicates and pass `sample_weight=duplicate_counts` to the `fit(..)` method of estimator. I override... | 26,248 | [
-0.037584710866212845,
0.1071980893611908,
0.02190873771905899,
-0.03845798224210739,
0.04613890126347542,
0.008006377145648003,
0.024493563920259476,
0.0492192842066288,
-0.042153533548116684,
0.000999210518784821,
0.030400650575757027,
0.029887476935982704,
0.014616391621530056,
0.038728... |
https://github.com/scikit-learn/scikit-learn/issues/26248 | [
"New Feature"
] | Add sample_weight parameter to OneHotEncoder.fit(...)
### Describe the workflow you want to enable
I have a dataset with huge number of duplicates - so in order to speed-up learning process I prefer to remove these duplicates and pass `sample_weight=duplicate_counts` to the `fit(..)` method of estimator. I override... | 26,248 | [
-0.04582909494638443,
0.08598961681127548,
0.019464213401079178,
-0.026718711480498314,
0.05086582154035568,
0.012462921440601349,
0.02389172464609146,
0.05235453322529793,
-0.058646585792303085,
0.015466940589249134,
0.04819892346858978,
0.027918798848986626,
0.02486044354736805,
0.046242... |
https://github.com/scikit-learn/scikit-learn/issues/26244 | [
"Bug",
"Needs Triage"
] | Random Tree Regressor crash the jupyter when fit to the data with bool columns
### Describe the bug
Got jupyter crash while trying to apply Random Forest Regression to data with about 1.5m rows and float, int and bool types.
Jupyter crashes every time even after rebooting the nootebook and PC.
### Steps/Code to Rep... | 26,244 | [
0.0023251823149621487,
0.03236701712012291,
0.027816463261842728,
-0.018113480880856514,
0.0925065204501152,
-0.017100030556321144,
-0.0007464132504537702,
0.03458490967750549,
0.032853201031684875,
-0.015323198400437832,
0.04234868660569191,
0.041184794157743454,
-0.0027655623853206635,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26233 | [
"New Feature",
"Needs Triage"
] | Addition of Feature That Detects And Treats Outliers As Per How The User Wishes
### Describe the workflow you want to enable
# Workflow
## Class
There Will Be A Class In sklearn.preprocessing whose instance will be created
## Function
A Function Will Be There Like fit_transform() Where User Can Pass pandas Dat... | 26,233 | [
-0.005794473923742771,
0.011534929275512695,
0.01838112063705921,
-0.013469165191054344,
-0.007823174819350243,
0.021998975425958633,
0.014115856029093266,
0.015908461064100266,
0.033739976584911346,
-0.00021466684120241553,
0.06773611158132553,
0.0706903487443924,
-0.04080088809132576,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26231 | [
"Needs Triage"
] | sklearn.metrics jaccard_score issue in 0,1 binary classification
when using the from sklearn.metrics import jaccard_score to compare two binary classes labeled as 0 and 1.
the sample that are both 0 won't be recognized as in the same class.
only samples that are both 1 will be recognized.
for example, consider... | 26,231 | [
-0.01677495427429676,
-0.006929879076778889,
0.032555174082517624,
0.021590400487184525,
0.03553922846913338,
0.011713845655322075,
0.013602931052446365,
0.03185013681650162,
-0.001273582922294736,
-0.02437262050807476,
0.04669464752078056,
0.020801113918423653,
0.04255925863981247,
0.0150... |
https://github.com/scikit-learn/scikit-learn/issues/26231 | [
"Needs Triage"
] | sklearn.metrics jaccard_score issue in 0,1 binary classification
when using the from sklearn.metrics import jaccard_score to compare two binary classes labeled as 0 and 1.
the sample that are both 0 won't be recognized as in the same class.
only samples that are both 1 will be recognized.
for example, consider... | 26,231 | [
-0.025216225534677505,
-0.00012760079698637128,
0.03591079264879227,
0.014843969605863094,
0.044365860521793365,
0.008311056531965733,
0.01926744170486927,
0.037395793944597244,
-0.012260198593139648,
-0.02950410731136799,
0.04553038999438286,
0.018406128510832787,
0.0320960208773613,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26231 | [
"Needs Triage"
] | sklearn.metrics jaccard_score issue in 0,1 binary classification
when using the from sklearn.metrics import jaccard_score to compare two binary classes labeled as 0 and 1.
the sample that are both 0 won't be recognized as in the same class.
only samples that are both 1 will be recognized.
for example, consider... | 26,231 | [
-0.027389846742153168,
-0.005369067657738924,
0.04059825465083122,
0.01948191225528717,
0.039540406316518784,
0.007507089991122484,
0.008857364766299725,
0.039258383214473724,
-0.020388241857290268,
-0.03803447261452675,
0.045440688729286194,
0.009319139644503593,
0.03929619863629341,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/26224 | [
"Bug",
"Needs Triage"
] | SequentialFeatureSelector may not be working correctly with transformers that transform with respect to each sample
### Describe the bug
This is (probably) an extended issue of #25711, in which when `SequentialFeatureSelector` is used with `ColumnTransformer`, an `IndexError` will be raised. After reading the code,... | 26,224 | [
-0.0029765458311885595,
0.08361830562353134,
0.008108178153634071,
-0.025921568274497986,
0.04081074148416519,
0.008515254594385624,
0.07576299458742142,
0.02533804066479206,
-0.02160841040313244,
-0.0021720617078244686,
0.045512162148952484,
0.009320860728621483,
0.03903285786509514,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26224 | [
"Bug",
"Needs Triage"
] | SequentialFeatureSelector may not be working correctly with transformers that transform with respect to each sample
### Describe the bug
This is (probably) an extended issue of #25711, in which when `SequentialFeatureSelector` is used with `ColumnTransformer`, an `IndexError` will be raised. After reading the code,... | 26,224 | [
-0.0029765458311885595,
0.08361830562353134,
0.008108178153634071,
-0.025921568274497986,
0.04081074148416519,
0.008515254594385624,
0.07576299458742142,
0.02533804066479206,
-0.02160841040313244,
-0.0021720617078244686,
0.045512162148952484,
0.009320860728621483,
0.03903285786509514,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/26222 | [
"Build / CI",
"RFC"
] | RFC Memory usage of tests
I recently tried to run the scikit-learn test suite with the [pytest-memray](https://github.com/bloomberg/pytest-memray) plugin. Here are the tests that allocate the most memory.
Here is are the top 10 worst offenders on my local machine (with CPython 3.11):
```
pytest --memray --most-... | 26,222 | [
-0.011593260802328587,
0.0072700222954154015,
0.00944034568965435,
0.016568444669246674,
0.03844473510980606,
-0.006222385913133621,
0.029483292251825333,
0.030275657773017883,
-0.005145817529410124,
0.004443515092134476,
0.02471078187227249,
0.024450846016407013,
-0.03260547295212746,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26222 | [
"Build / CI",
"RFC"
] | RFC Memory usage of tests
I recently tried to run the scikit-learn test suite with the [pytest-memray](https://github.com/bloomberg/pytest-memray) plugin. Here are the tests that allocate the most memory.
Here is are the top 10 worst offenders on my local machine (with CPython 3.11):
```
pytest --memray --most-... | 26,222 | [
-0.011593260802328587,
0.0072700222954154015,
0.00944034568965435,
0.016568444669246674,
0.03844473510980606,
-0.006222385913133621,
0.029483292251825333,
0.030275657773017883,
-0.005145817529410124,
0.004443515092134476,
0.02471078187227249,
0.024450846016407013,
-0.03260547295212746,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26222 | [
"Build / CI",
"RFC"
] | RFC Memory usage of tests
I recently tried to run the scikit-learn test suite with the [pytest-memray](https://github.com/bloomberg/pytest-memray) plugin. Here are the tests that allocate the most memory.
Here is are the top 10 worst offenders on my local machine (with CPython 3.11):
```
pytest --memray --most-... | 26,222 | [
-0.011593260802328587,
0.0072700222954154015,
0.00944034568965435,
0.016568444669246674,
0.03844473510980606,
-0.006222385913133621,
0.029483292251825333,
0.030275657773017883,
-0.005145817529410124,
0.004443515092134476,
0.02471078187227249,
0.024450846016407013,
-0.03260547295212746,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.019804973155260086,
-0.009649946354329586,
0.0013708920450881124,
-0.03802873194217682,
-0.033228788524866104,
-0.024630511179566383,
-0.0040896739810705185,
-0.006402102764695883,
-0.01588514633476734,
-0.025447839871048927,
0.036583613604307175,
-0.09226632118225098,
0.007009954657405615... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.029206879436969757,
-0.010352207347750664,
0.0048917303793132305,
-0.04300950840115547,
-0.03715066984295845,
-0.021111400797963142,
-0.013204144313931465,
-0.009115848690271378,
-0.01599559187889099,
-0.030300891026854515,
0.04785449802875519,
-0.07965429872274399,
0.0007331247325055301,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02020910382270813,
0.0026463125832378864,
0.0016580666415393353,
-0.04377823695540428,
-0.03580767288804054,
-0.02819018065929413,
-0.0000703235127730295,
-0.0059918127954006195,
-0.007966023869812489,
-0.031069356948137283,
0.045294176787137985,
-0.08641598373651505,
0.00237351655960083,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02582629583775997,
0.0076681398786604404,
0.000800156150944531,
-0.04444722458720207,
-0.026389138773083687,
-0.02272263914346695,
0.0004686559841502458,
-0.006305368151515722,
-0.008587894029915333,
-0.032041363418102264,
0.04943130537867546,
-0.07696983963251114,
0.005424520466476679,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.03171679005026817,
0.012891062535345554,
0.00198445119895041,
-0.058659132570028305,
-0.0385269969701767,
-0.026753151789307594,
0.0006809249753132463,
-0.006306633818894625,
-0.0022118249908089638,
-0.028153525665402412,
0.04450992867350578,
-0.07440190017223358,
0.008951062336564064,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.018824303522706032,
0.00209823134355247,
0.005790193099528551,
-0.05495262145996094,
-0.03879482299089432,
-0.028478236868977547,
0.004430430941283703,
-0.001437222003005445,
0.0019869371317327023,
-0.029547106474637985,
0.044204436242580414,
-0.07901833206415176,
0.006831672042608261,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.020441943779587746,
0.0015175013104453683,
0.004801114555448294,
-0.05600889027118683,
-0.033115286380052567,
-0.02226095274090767,
0.0004959337529726326,
0.00024561380269005895,
0.003564681624993682,
-0.033234648406505585,
0.03695645183324814,
-0.07778245955705643,
-0.000657648837659508,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.021802101284265518,
0.0037823105230927467,
0.0018024357268586755,
-0.04488605633378029,
-0.031122339889407158,
-0.017266634851694107,
-0.004759877920150757,
-0.006793346721678972,
-0.0009486133349128067,
-0.03348461911082268,
0.03787066787481308,
-0.08297806978225708,
-0.003885712940245866... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.021950336173176765,
0.0027363162953406572,
0.00037764510489068925,
-0.04202628508210182,
-0.035779401659965515,
-0.027293767780065536,
-0.0003285478742327541,
-0.005996125750243664,
-0.007667895406484604,
-0.03130794316530228,
0.046457041054964066,
-0.08612322807312012,
0.00263615325093269... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02566365711390972,
0.011584637686610222,
0.0047647044993937016,
-0.04457496106624603,
-0.032908614724874496,
-0.03152972087264061,
-0.00018686264229472727,
-0.005774933844804764,
-0.014857018366456032,
-0.03366156667470932,
0.04984840750694275,
-0.08121021091938019,
-0.00015720524243079126... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.024577723816037178,
0.0015017349505797029,
-0.0001732216333039105,
-0.04036705568432808,
-0.031576186418533325,
-0.025020616129040718,
-0.0006874204846099019,
-0.004857479128986597,
-0.008746745064854622,
-0.03075503557920456,
0.0458436980843544,
-0.0835784301161766,
0.00223335437476635,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02740674652159214,
0.01616389863193035,
-0.0012338481610640883,
-0.049561597406864166,
-0.02894720807671547,
-0.030606942251324654,
0.0027569010853767395,
0.0046233972534537315,
0.016040291637182236,
-0.027493346482515335,
0.03397480398416519,
-0.06765376031398773,
-0.00708853080868721,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.025946367532014847,
0.014045322313904762,
-0.0005550304776988924,
-0.033686719834804535,
-0.03125230595469475,
-0.029199667274951935,
0.005589884705841541,
-0.0099229346960783,
-0.0034099549520760775,
-0.0390646830201149,
0.03624403849244118,
-0.08136206120252609,
-0.00567018985748291,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02845684066414833,
0.0067323194816708565,
0.0012948691146448255,
-0.039288368076086044,
-0.031406745314598083,
-0.02867889776825905,
-0.0038596675731241703,
-0.005406249314546585,
-0.003357660723850131,
-0.03511669114232063,
0.042086973786354065,
-0.0788668543100357,
-0.0025924821384251118... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.018382452428340912,
-0.0011184532195329666,
0.001776150893419981,
-0.03863266855478287,
-0.036236103624105453,
-0.02253403700888157,
0.0009208839619532228,
-0.009163808077573776,
-0.003148639341816306,
-0.031921133399009705,
0.043267618864774704,
-0.08102219551801682,
0.0022814625408500433... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.02457527257502079,
-0.0026320384349673986,
0.004228981677442789,
-0.04919980466365814,
-0.04200151935219765,
-0.026455236598849297,
0.011413204483687878,
-0.006011417601257563,
-0.001697423867881298,
-0.034252047538757324,
0.05191665515303612,
-0.08277174085378647,
-0.0012526880018413067,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.023748524487018585,
0.0027544477488845587,
0.003311038948595524,
-0.04151829704642296,
-0.03812943026423454,
-0.025913815945386887,
0.0011244104243814945,
-0.004246424417942762,
-0.008568709716200829,
-0.03213731572031975,
0.044627588242292404,
-0.08592893928289413,
0.003933814819902182,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.0219322070479393,
0.008088275790214539,
0.004862655885517597,
-0.041620224714279175,
-0.03475618734955788,
-0.028035257011651993,
-0.0011143316514790058,
-0.004803214687854052,
-0.005047912709414959,
-0.03228403255343437,
0.04644950479269028,
-0.07679153233766556,
-0.0007360198651440442,
... |
https://github.com/scikit-learn/scikit-learn/issues/26220 | [
"Documentation",
"RFC"
] | RFC Suggesting HistGradientBoosting in RandomForest and GradientBoosting pages
Right now we have this in the GradientBoosting API page:
> [sklearn.ensemble.HistGradientBoostingClassifier](https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradien... | 26,220 | [
0.019731296226382256,
0.005535549949854612,
0.005896298214793205,
-0.04311688616871834,
-0.03830140084028244,
-0.030295642092823982,
-0.0028648413717746735,
-0.003220902057364583,
-0.01116582378745079,
-0.032454490661621094,
0.047841064631938934,
-0.0797201618552208,
0.0019872398115694523,
... |
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