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/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 | [
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0.012961054220795631,
0.044123124331235886,
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0.04969463869929314,
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0.015776578336954117,
0.0541992150247097,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 | [
0.010617686435580254,
-0.059248413890600204,
0.01343026477843523,
0.029090890660881996,
0.006449243985116482,
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0.026361428201198578,
0.031395357102155685,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 | [
0.006146085448563099,
-0.036509037017822266,
0.011869056150317192,
0.033593159168958664,
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0.0007107250858098269,
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0.014119754545390606,
0.05253149941563606,
... |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 | [
0.00837426632642746,
-0.06393524259328842,
0.015477357432246208,
0.03520285338163376,
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0.07329922169446945,
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0.010315624065697193,
0.045257989317178726,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24086 | [
"New Feature",
"module:feature_extraction",
"Needs Decision - Include Feature"
] | Add other connectivity definitions to grid_to_graph function
### Describe the workflow you want to enable
The current grid_to_graph function only defines voxel neighbors with the 6-connectivity definition. I would like to add 18 and 26 connectivity. (https://en.wikipedia.org/wiki/Pixel_connectivity#26-connected)
###... | 24,086 | [
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0.06980010867118835,
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0.015427510254085064,
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0.06310331076383591,
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0.03692639246582985,
0.02004154399037361,
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0.0055330595932900906,
0.023636018857359886,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
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0.012552749365568161,
0.025059521198272705,
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0.05760110169649124,
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0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 | [
0.0033368216827511787,
-0.028563309460878372,
0.012552749365568161,
0.025059521198272705,
0.0782107263803482,
-0.027535872533917427,
0.05760110169649124,
-0.009583680890500546,
-0.007928548380732536,
0.014941351488232613,
0.026899823918938637,
0.04014410823583603,
0.021480387076735497,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 | [
0.012948382645845413,
0.03479078784584999,
0.023154504597187042,
-0.012557150796055794,
0.11579396575689316,
0.021110547706484795,
0.00953947938978672,
0.08397752791643143,
-0.024839749559760094,
0.008469264954328537,
0.024932414293289185,
0.06454487144947052,
-0.00114397332072258,
0.02739... |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 | [
0.012948382645845413,
0.03479078784584999,
0.023154504597187042,
-0.012557150796055794,
0.11579396575689316,
0.021110547706484795,
0.00953947938978672,
0.08397752791643143,
-0.024839749559760094,
0.008469264954328537,
0.024932414293289185,
0.06454487144947052,
-0.00114397332072258,
0.02739... |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 | [
0.012948382645845413,
0.03479078784584999,
0.023154504597187042,
-0.012557150796055794,
0.11579396575689316,
0.021110547706484795,
0.00953947938978672,
0.08397752791643143,
-0.024839749559760094,
0.008469264954328537,
0.024932414293289185,
0.06454487144947052,
-0.00114397332072258,
0.02739... |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 | [
0.020323418080806732,
0.025434300303459167,
0.033339984714984894,
-0.0030690280254930258,
0.10002808272838593,
-0.007900197990238667,
0.05281351879239082,
0.04166841134428978,
0.012913912534713745,
0.004610994830727577,
0.0387469120323658,
0.09604060649871826,
0.011446718126535416,
0.03524... |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 | [
0.020323418080806732,
0.025434300303459167,
0.033339984714984894,
-0.0030690280254930258,
0.10002808272838593,
-0.007900197990238667,
0.05281351879239082,
0.04166841134428978,
0.012913912534713745,
0.004610994830727577,
0.0387469120323658,
0.09604060649871826,
0.011446718126535416,
0.03524... |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 | [
0.020323418080806732,
0.025434300303459167,
0.033339984714984894,
-0.0030690280254930258,
0.10002808272838593,
-0.007900197990238667,
0.05281351879239082,
0.04166841134428978,
0.012913912534713745,
0.004610994830727577,
0.0387469120323658,
0.09604060649871826,
0.011446718126535416,
0.03524... |
https://github.com/scikit-learn/scikit-learn/issues/24078 | [
"Documentation",
"Needs Triage"
] | Binder link does not work
### Describe the issue linked to the documentation
I have tried the binder link given here: [link](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)
but it does not work.
### Suggest a... | 24,078 | [
-0.002908715745434165,
-0.00581857655197382,
0.01975267194211483,
-0.07399353384971619,
0.005607680417597294,
0.01099789422005415,
0.05762165039777756,
0.032023802399635315,
0.07146535813808441,
-0.04025884345173836,
-0.0076982383616268635,
0.05913867428898811,
-0.001482177060097456,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24078 | [
"Documentation",
"Needs Triage"
] | Binder link does not work
### Describe the issue linked to the documentation
I have tried the binder link given here: [link](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)
but it does not work.
### Suggest a... | 24,078 | [
-0.01843150332570076,
-0.04199247807264328,
0.01905115135014057,
-0.05920085683465004,
0.0002832071913871914,
0.00801183097064495,
0.07716801762580872,
0.01213028747588396,
0.07666891068220139,
-0.021355533972382545,
-0.01941823400557041,
0.0416005402803421,
0.0076384274289011955,
-0.00548... |
https://github.com/scikit-learn/scikit-learn/issues/24064 | [
"Documentation",
"Needs Triage"
] | Permutation feature importance documentation page with misleading code
### Describe the issue linked to the documentation
In the documentation page of the feature importance algorithm (https://scikit-learn.org/stable/modules/permutation_importance.html) there are misleading code snippets.
When performing the fol... | 24,064 | [
0.031812917441129684,
-0.02505570463836193,
0.011353998444974422,
0.0012074372498318553,
0.010277573019266129,
0.061030469834804535,
0.05741157755255699,
-0.046191874891519547,
0.02696753479540348,
-0.03300575166940689,
0.022571248933672905,
0.008065817877650261,
0.049452926963567734,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24064 | [
"Documentation",
"Needs Triage"
] | Permutation feature importance documentation page with misleading code
### Describe the issue linked to the documentation
In the documentation page of the feature importance algorithm (https://scikit-learn.org/stable/modules/permutation_importance.html) there are misleading code snippets.
When performing the fol... | 24,064 | [
0.031812917441129684,
-0.02505570463836193,
0.011353998444974422,
0.0012074372498318553,
0.010277573019266129,
0.061030469834804535,
0.05741157755255699,
-0.046191874891519547,
0.02696753479540348,
-0.03300575166940689,
0.022571248933672905,
0.008065817877650261,
0.049452926963567734,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24063 | [
"New Feature",
"Needs Triage"
] | CI/Pre-commit Incompatibility between black and flake8
### Describe the workflow you want to enable
While `black` *tries* to make line lengths 88 characters maximum, there are cases where it will leave the code alone. On the other hand `flake8` is a strict check whether the line is longer than 88 characters, so in ca... | 24,063 | [
-0.011845405213534832,
0.006441050674766302,
-0.027942491695284843,
-0.03585727885365486,
0.006498584523797035,
-0.009084419347345829,
0.0409204363822937,
0.00022155903570819646,
-0.03402208536863327,
-0.00992444809526205,
0.06970085203647614,
-0.02030651643872261,
0.0018126245122402906,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24063 | [
"New Feature",
"Needs Triage"
] | CI/Pre-commit Incompatibility between black and flake8
### Describe the workflow you want to enable
While `black` *tries* to make line lengths 88 characters maximum, there are cases where it will leave the code alone. On the other hand `flake8` is a strict check whether the line is longer than 88 characters, so in ca... | 24,063 | [
-0.01101768109947443,
0.007514954078942537,
-0.02820739522576332,
-0.04633277654647827,
0.009835761971771717,
-0.009297803975641727,
0.03288647159934044,
-0.002893168479204178,
-0.05183043330907822,
-0.013928648084402084,
0.05738159641623497,
-0.024191247299313545,
0.004495678469538689,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 | [
0.009380155242979527,
-0.043991442769765854,
0.044444482773542404,
-0.04403217136859894,
0.08576422184705734,
0.01170461717993021,
0.021930446848273277,
0.04569106176495552,
-0.007457820698618889,
-0.018484583124518394,
0.02460252307355404,
0.0400143601000309,
-0.003372953040525317,
0.0264... |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 | [
0.009380155242979527,
-0.043991442769765854,
0.044444482773542404,
-0.04403217136859894,
0.08576422184705734,
0.01170461717993021,
0.021930446848273277,
0.04569106176495552,
-0.007457820698618889,
-0.018484583124518394,
0.02460252307355404,
0.0400143601000309,
-0.003372953040525317,
0.0264... |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 | [
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0.0400143601000309,
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0.0264... |
https://github.com/scikit-learn/scikit-learn/issues/24056 | [
"Needs Triage"
] | > The Conference Paper Suggested has helped us to do detail analysis for our Research Activity
> The Conference Paper Suggested has helped us to do detail analysis for our Research Activity
Thanks for Reply. Will Surely revert you back with new article..
_Originally posted by @sujataoak799 in https://github.com/... | 24,056 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/24054 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2768157160)** (Jul 31, 2022)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/2772243666) on Aug 01, 2022 | 24,054 | [
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0.08435636758804321,
0.04123077541589737,
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0.071... |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 | [
-0.020599238574504852,
0.03401690721511841,
-0.04203806445002556,
0.09663061797618866,
-0.04153672605752945,
-0.004500530194491148,
0.05410602688789368,
0.028378859162330627,
-0.035688113421201706,
-0.03297755494713783,
0.007621689233928919,
0.062130775302648544,
0.024112338200211525,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 | [
-0.020599238574504852,
0.03401690721511841,
-0.04203806445002556,
0.09663061797618866,
-0.04153672605752945,
-0.004500530194491148,
0.05410602688789368,
0.028378859162330627,
-0.035688113421201706,
-0.03297755494713783,
0.007621689233928919,
0.062130775302648544,
0.024112338200211525,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 | [
-0.020599238574504852,
0.03401690721511841,
-0.04203806445002556,
0.09663061797618866,
-0.04153672605752945,
-0.004500530194491148,
0.05410602688789368,
0.028378859162330627,
-0.035688113421201706,
-0.03297755494713783,
0.007621689233928919,
0.062130775302648544,
0.024112338200211525,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 | [
-0.020599238574504852,
0.03401690721511841,
-0.04203806445002556,
0.09663061797618866,
-0.04153672605752945,
-0.004500530194491148,
0.05410602688789368,
0.028378859162330627,
-0.035688113421201706,
-0.03297755494713783,
0.007621689233928919,
0.062130775302648544,
0.024112338200211525,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 | [
-0.020599238574504852,
0.03401690721511841,
-0.04203806445002556,
0.09663061797618866,
-0.04153672605752945,
-0.004500530194491148,
0.05410602688789368,
0.028378859162330627,
-0.035688113421201706,
-0.03297755494713783,
0.007621689233928919,
0.062130775302648544,
0.024112338200211525,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24045 | [
"Documentation",
"Needs Triage"
] | URL to PCA mle papers broken in docs
### Describe the issue linked to the documentation
Some urls to papers in the [PCA docs](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA) no longer work. Here
>For n_components == ‘mle’, this class uses the method from:... | 24,045 | [
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0.048019830137491226,
0.011387900449335575,
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-0.04405324161052704,
-0.0100313825532794,
0.0191662535071373,
0.015939563512802124,
-0.006675432... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 | [
0.02495880238711834,
-0.0077669075690209866,
0.04378553852438927,
0.021723974496126175,
0.06721971929073334,
-0.01164232101291418,
0.012305961921811104,
0.03662830963730812,
0.08172277361154556,
-0.00025025394279509783,
0.02994859404861927,
-0.020500393584370613,
-0.027869340032339096,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24025 | [
"Bug",
"module:linear_model",
"module:utils"
] | check_estimator cannot validate SGDClassifier with log_loss
### Describe the bug
It just tried to use **SGDClassifier** with **log_loss**, but it fail some test when I try to validated it with **check_estimator** function.
### Steps/Code to Reproduce
```python
from sklearn.linear_model import SGDClassifier
... | 24,025 | [
0.00952963251620531,
-0.037365105003118515,
0.0379573218524456,
0.016190912574529648,
0.1148480474948883,
0.004451314453035593,
0.043087366968393326,
0.04020719975233078,
0.04152316227555275,
-0.03931424766778946,
0.03869783505797386,
0.0649321973323822,
-0.016693470999598503,
0.0006471759... |
https://github.com/scikit-learn/scikit-learn/issues/24025 | [
"Bug",
"module:linear_model",
"module:utils"
] | check_estimator cannot validate SGDClassifier with log_loss
### Describe the bug
It just tried to use **SGDClassifier** with **log_loss**, but it fail some test when I try to validated it with **check_estimator** function.
### Steps/Code to Reproduce
```python
from sklearn.linear_model import SGDClassifier
... | 24,025 | [
0.00952963251620531,
-0.037365105003118515,
0.0379573218524456,
0.016190912574529648,
0.1148480474948883,
0.004451314453035593,
0.043087366968393326,
0.04020719975233078,
0.04152316227555275,
-0.03931424766778946,
0.03869783505797386,
0.0649321973323822,
-0.016693470999598503,
0.0006471759... |
https://github.com/scikit-learn/scikit-learn/issues/24024 | [
"Performance"
] | Optimize inference time for sklearn models in real time for single example
### Describe the bug
It is a multi-class classification model with sklearn.
I am using `OneVsOneClassifier` model to train and predict `150 intents`. Its a multi-class classification problem.
**Data:**
text intents
... | 24,024 | [
-0.01882835663855076,
0.04897681251168251,
0.01559014804661274,
0.028711942955851555,
0.03532159700989723,
-0.009713726118206978,
-0.009286322630941868,
0.04116426408290863,
0.01942548342049122,
-0.014280235394835472,
0.04712711274623871,
0.0394759438931942,
-0.008049443364143372,
0.052631... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 | [
0.022494591772556305,
0.004536901134997606,
0.0040209111757576466,
0.0006319503299891949,
0.02106914483010769,
0.001899821450933814,
0.03324243053793907,
0.010247180238366127,
0.0145949088037014,
-0.015596858225762844,
-0.004167351871728897,
0.03601086884737015,
-0.02083904854953289,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24009 | [
"Needs Triage"
] | `_sk_visual_block` can be executed before parameter validation
I found that `_sk_visual_block` can be executed before parameter validation. It can lead to a not really informative error message:
```python
from sklearn.ensemble import StackingClassifier
StackingClassifier(estimators=[])
```
```pytb
ValueError... | 24,009 | [
0.005783611908555031,
-0.03860585764050484,
0.02911035716533661,
-0.021702762693166733,
0.10099224746227264,
0.028428860008716583,
0.029922695830464363,
0.0015053247334435582,
-0.0021732428576797247,
0.004929180257022381,
0.03766469284892082,
0.02220064401626587,
0.032662712037563324,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24009 | [
"Needs Triage"
] | `_sk_visual_block` can be executed before parameter validation
I found that `_sk_visual_block` can be executed before parameter validation. It can lead to a not really informative error message:
```python
from sklearn.ensemble import StackingClassifier
StackingClassifier(estimators=[])
```
```pytb
ValueError... | 24,009 | [
0.005783611908555031,
-0.03860585764050484,
0.02911035716533661,
-0.021702762693166733,
0.10099224746227264,
0.028428860008716583,
0.029922695830464363,
0.0015053247334435582,
-0.0021732428576797247,
0.004929180257022381,
0.03766469284892082,
0.02220064401626587,
0.032662712037563324,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 | [
-0.046092357486486435,
-0.011248375289142132,
0.03457045555114746,
0.01448284462094307,
0.06680752336978912,
0.0068948459811508656,
0.008415971882641315,
0.007116901222616434,
-0.00979109387844801,
-0.041574783623218536,
0.002262694761157036,
-0.0156865194439888,
0.00264270999468863,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 | [
0.017769185826182365,
-0.019853457808494568,
-0.010377737693488598,
-0.005518034100532532,
0.06751003116369247,
-0.01401502639055252,
0.015331762842833996,
0.07028104364871979,
-0.02258038893342018,
-0.017741259187459946,
0.043394140899181366,
0.08935896307229996,
-0.015401937998831272,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
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-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 | [
0.029034273698925972,
0.0652022734284401,
0.0021848208270967007,
-0.01120695099234581,
-0.022099578753113747,
-0.017596295103430748,
0.015492614358663559,
-0.013011439703404903,
-0.04382701590657234,
-0.0539865605533123,
0.009320755489170551,
0.019137350842356682,
-0.03082042559981346,
0.0... |
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