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/30935 | [
"Bug"
] | The default token pattern in CountVectorizer breaks Indic sentences into non-sensical tokens
### Describe the bug
The default `token_pattern` in `CountVectorizer` is `r"(?u)\b\w\w+\b"` which tokenizes Indic texts in a wrong way - breaks whitespace tokenized words into multiple chunks and even omits several valid char... | 30,935 | [
0.05598331615328789,
0.018539490178227425,
0.015863319858908653,
0.03269343450665474,
0.05982275679707527,
0.0020870945882052183,
-0.006617846433073282,
-0.02814786322414875,
-0.06362085789442062,
0.0017951849149540067,
0.03316454216837883,
-0.05242127552628517,
0.03224828466773033,
0.0612... |
https://github.com/scikit-learn/scikit-learn/issues/30935 | [
"Bug"
] | The default token pattern in CountVectorizer breaks Indic sentences into non-sensical tokens
### Describe the bug
The default `token_pattern` in `CountVectorizer` is `r"(?u)\b\w\w+\b"` which tokenizes Indic texts in a wrong way - breaks whitespace tokenized words into multiple chunks and even omits several valid char... | 30,935 | [
0.05598331615328789,
0.018539490178227425,
0.015863319858908653,
0.03269343450665474,
0.05982275679707527,
0.0020870945882052183,
-0.006617846433073282,
-0.02814786322414875,
-0.06362085789442062,
0.0017951849149540067,
0.03316454216837883,
-0.05242127552628517,
0.03224828466773033,
0.0612... |
https://github.com/scikit-learn/scikit-learn/issues/30934 | [
"Documentation"
] | DOC Missing doc string in tests present in sklearn/linear_model/_glm/tests/test_glm.py
### Describe the issue related to documentation
The file `sklearn/linear_model/_glm/tests/test_glm.py` has the following tests without any doc string to describe what these functions aim to test.
- test_glm_wrong_y_range
- test_war... | 30,934 | [
-0.0020375349558889866,
0.006186032667756081,
0.0014723580097779632,
0.028876161202788353,
0.019856056198477745,
0.03519168496131897,
0.05603555217385292,
0.04091159254312515,
0.03129401430487633,
-0.028304189443588257,
0.06617096811532974,
0.04713853821158409,
-0.002548141870647669,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/30934 | [
"Documentation"
] | DOC Missing doc string in tests present in sklearn/linear_model/_glm/tests/test_glm.py
### Describe the issue related to documentation
The file `sklearn/linear_model/_glm/tests/test_glm.py` has the following tests without any doc string to describe what these functions aim to test.
- test_glm_wrong_y_range
- test_war... | 30,934 | [
0.011715218424797058,
-0.0024846091400831938,
0.005540173966437578,
0.019774246960878372,
0.01582447625696659,
0.049527376890182495,
0.06839828193187714,
0.045144226402044296,
0.03239552304148674,
-0.03152519091963768,
0.061650004237890244,
0.04416624829173088,
-0.010153746232390404,
-0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30934 | [
"Documentation"
] | DOC Missing doc string in tests present in sklearn/linear_model/_glm/tests/test_glm.py
### Describe the issue related to documentation
The file `sklearn/linear_model/_glm/tests/test_glm.py` has the following tests without any doc string to describe what these functions aim to test.
- test_glm_wrong_y_range
- test_war... | 30,934 | [
0.007065355312079191,
-0.0023902757093310356,
0.005054680164903402,
0.020833972841501236,
0.016812996938824654,
0.04912757873535156,
0.06822391599416733,
0.04703763872385025,
0.03035982884466648,
-0.03135859593749046,
0.06377069652080536,
0.04450547322630882,
-0.010120633989572525,
-0.0260... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30924 | [
"Bug"
] | KBinsDiscretizer uniform strategy bin assignment wrong due to floating point multiplication
### Describe the bug
KBinsDiscretizer uniform strategy uses numpy.linspace to make bin edges.
numpy.linspace works out a delta like: delta = (max - min)/num_bins
Then the bin edges are computed: delta * n
The issue is the... | 30,924 | [
-0.00020615733228623867,
-0.044924911111593246,
-0.008319463580846786,
-0.018938345834612846,
-0.004608527757227421,
-0.007877158932387829,
0.029032323509454727,
0.024349980056285858,
-0.08116548508405685,
-0.005033497232943773,
0.014779305085539818,
-0.026697378605604172,
0.0145185766741633... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30921 | [
"Bug",
"Needs Info"
] | Persistent UserWarning about KMeans Memory Leak on Windows Despite Applying Suggested Fixes
### Describe the bug
Issue Description
When running code involving GaussianMixture (or KMeans), a UserWarning about a known memory leak on Windows with MKL is raised, even after implementing the suggested workaround (OMP_NUM_T... | 30,921 | [
-0.00662970682606101,
0.03697560355067253,
-0.012643247842788696,
0.02788255549967289,
0.06729158759117126,
0.0057094101794064045,
-0.025511866435408592,
0.017040230333805084,
-0.012143304571509361,
0.008589968085289001,
0.043509699404239655,
0.05536043271422386,
-0.01796914078295231,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30917 | [
"Bug"
] | DecisionTreeClassifier having unexpected behaviour with 'min_weight_fraction_leaf=0.5'
### Describe the bug
When fitting DecisionTreeClassifier on a duplicated sample set (i.e. each sample repeated by two), the result is not the same as when fitting on the original sample set. This only happens for 'min_weight_fracti... | 30,917 | [
0.020505456253886223,
-0.056637778878211975,
0.013800607062876225,
0.004757897462695837,
0.07114886492490768,
-0.05009664222598076,
-0.041614990681409836,
0.045788705348968506,
-0.04708681255578995,
0.0013928000116720796,
0.03572288528084755,
0.02337166853249073,
0.014192876406013966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30913 | [
"Documentation",
"Needs Triage"
] | Typo in _k_means_lloyd.pyx
### Describe the issue linked to the documentation
I noticed that in the lloyd_iter_chunked_sparse function of _k_means_lloyd.pyx, there is a potential typo in the comment for handling an empty array. It reads (starting on line 280):
"An empty array was passed, do nothing and return early ... | 30,913 | [
0.024997247382998466,
-0.03659873828291893,
-0.002812804887071252,
0.007945518009364605,
0.03890059515833855,
-0.002201130846515298,
0.03452353924512863,
-0.00988868996500969,
-0.057079195976257324,
-0.021780414506793022,
0.04039308801293373,
0.03414794057607651,
0.0014573958469554782,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30910 | [
"Bug",
"Needs Triage"
] | Wrong result in log_loss when labels and corresponding y_pred columns are not ordered
### Describe the bug
Log loss is not computed correctly when labels (and their corresponding columns in `y_pred`) are not in ascending (for numbers) / lexicographic (for strings) order.
### Steps/Code to Reproduce
```
from sklear... | 30,910 | [
0.053909171372652054,
-0.004165250342339277,
0.028746329247951508,
0.019230781123042107,
0.10354644805192947,
0.004709980916231871,
0.026537898927927017,
0.025036977604031563,
-0.009729842655360699,
-0.04929305613040924,
0.018298426643013954,
0.010999336838722229,
0.011776131577789783,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30909 | [
"RFC"
] | Improve `pos_label` switching for metrics
Supercedes #26758
Switching `pos_label` for metrics, involves some manipulation for `predict_proba` (switch column you pass) and `decision_function` (for binary, multiply by -1) as you must pass the values for the positive class.
In discussions in #26758 we thought of two ... | 30,909 | [
-0.013315269723534584,
0.018207557499408722,
0.008653654716908932,
-0.036704227328300476,
0.0014588331105187535,
-0.03179764375090599,
0.04090404510498047,
-0.005626399535685778,
-0.014851173385977745,
-0.026655767112970352,
0.031008737161755562,
0.022653864696621895,
0.005019255448132753,
... |
https://github.com/scikit-learn/scikit-learn/issues/30909 | [
"RFC"
] | Improve `pos_label` switching for metrics
Supercedes #26758
Switching `pos_label` for metrics, involves some manipulation for `predict_proba` (switch column you pass) and `decision_function` (for binary, multiply by -1) as you must pass the values for the positive class.
In discussions in #26758 we thought of two ... | 30,909 | [
-0.030160028487443924,
0.037831615656614304,
0.015539843589067459,
-0.03013995662331581,
0.00470490287989378,
-0.036173101514577866,
0.03236015886068344,
-0.005177874583750963,
-0.0138494111597538,
-0.019528407603502274,
0.02019624039530754,
0.022620825096964836,
-0.0011838909704238176,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30909 | [
"RFC"
] | Improve `pos_label` switching for metrics
Supercedes #26758
Switching `pos_label` for metrics, involves some manipulation for `predict_proba` (switch column you pass) and `decision_function` (for binary, multiply by -1) as you must pass the values for the positive class.
In discussions in #26758 we thought of two ... | 30,909 | [
-0.01934061013162136,
0.022597288712859154,
0.017032895237207413,
-0.021516045555472374,
-0.008380302228033543,
-0.02766636572778225,
0.027237366884946823,
-0.014717759564518929,
-0.018298763781785965,
-0.002065766602754593,
0.013870209455490112,
0.03944629058241844,
0.01659621298313141,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30909 | [
"RFC"
] | Improve `pos_label` switching for metrics
Supercedes #26758
Switching `pos_label` for metrics, involves some manipulation for `predict_proba` (switch column you pass) and `decision_function` (for binary, multiply by -1) as you must pass the values for the positive class.
In discussions in #26758 we thought of two ... | 30,909 | [
-0.023993665352463722,
0.05424455180764198,
0.024124519899487495,
-0.023728685453534126,
0.007729369215667248,
-0.04249051213264465,
0.03723931685090065,
-0.011634301394224167,
-0.009151878766715527,
-0.012661580927670002,
0.024658773094415665,
0.038366638123989105,
0.004353353753685951,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30909 | [
"RFC"
] | Improve `pos_label` switching for metrics
Supercedes #26758
Switching `pos_label` for metrics, involves some manipulation for `predict_proba` (switch column you pass) and `decision_function` (for binary, multiply by -1) as you must pass the values for the positive class.
In discussions in #26758 we thought of two ... | 30,909 | [
-0.03222181648015976,
0.05577566474676132,
0.02019227296113968,
-0.018687253817915916,
0.010174049064517021,
-0.03996653854846954,
0.03077167458832264,
0.00024310636217705905,
-0.015907179564237595,
-0.02458878606557846,
0.0219588465988636,
0.037920862436294556,
0.0028790608048439026,
0.09... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30907 | [
"Documentation"
] | DOC Update wikipedia article for scikit-learn
### Describe the issue linked to the documentation
The [wikipedia article on scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) covers its basic history, development, and features, but there are a few areas where additional details could enhance the content
Notice... | 30,907 | [
0.04885121434926987,
0.0061165220104157925,
0.004316744394600391,
-0.013434307649731636,
-0.012190388515591621,
0.018457653000950813,
0.016899975016713142,
0.014270510524511337,
0.008318550884723663,
-0.0728304535150528,
0.04621461033821106,
0.053639862686395645,
0.02131771109998226,
0.050... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.00552028464153409,
0.017831852659583092,
-0.01679394207894802,
0.001398740103468299,
0.02137480303645134,
0.028553277254104614,
0.0432250052690506,
-0.03164960443973541,
-0.013562540523707867,
-0.00732545368373394,
0.05136105790734291,
0.02060787007212639,
0.05583058297634125,
0.0366798... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.004495635163038969,
0.01128297671675682,
-0.016065847128629684,
0.005437181796878576,
0.02031957358121872,
0.030312294140458107,
0.046053189784288406,
-0.030767813324928284,
-0.009423516690731049,
-0.006217846646904945,
0.051487118005752563,
0.020033003762364388,
0.05414631590247154,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.004176741000264883,
0.010574987158179283,
-0.015964683145284653,
0.004817679524421692,
0.02116827666759491,
0.03267695754766464,
0.046368326991796494,
-0.03329654410481453,
-0.009108936414122581,
-0.008659366518259048,
0.052436236292123795,
0.022229356691241264,
0.05843920633196831,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.0008768694824539125,
0.02202957309782505,
-0.015692351385951042,
-0.0012887497432529926,
0.015122552402317524,
0.025101205334067345,
0.038077350705862045,
-0.030515240505337715,
-0.023368002846837044,
-0.0077235158532857895,
0.046726103872060776,
0.016470041126012802,
0.0618206150829792,
... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.0003673644387163222,
0.017125172540545464,
-0.01344570517539978,
0.0028939491603523493,
0.01670984923839569,
0.025349153205752373,
0.03851953148841858,
-0.03233800828456879,
-0.023456428200006485,
-0.005719323642551899,
0.0556337870657444,
0.01542479544878006,
0.054026488214731216,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30905 | [
"Documentation"
] | Unclear information in Explained variance
### Describe the issue linked to the documentation
Hi, the text in Explained variance page is somewhat unclear, so I want to propose a clearer text. On line 1005, the detail says this:
> "The Explained Variance score is similar to the R^2 score, with the notable difference t... | 30,905 | [
-0.00818262156099081,
0.019624412059783936,
-0.015800081193447113,
0.004225153475999832,
0.022764813154935837,
0.027520036324858665,
0.04343901202082634,
-0.015451807528734207,
-0.014441605657339096,
-0.0061117298901081085,
0.05775129422545433,
0.005401423200964928,
0.06139242649078369,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
-0.02036212384700775,
-0.06879384815692902,
-0.029305072501301765,
0.06507006287574768,
0.01367188896983862,
-0.010288052260875702,
-0.029690487310290337,
0.02719021774828434,
0.005719386041164398,
0.009704416617751122,
0.008219484239816666,
0.09463365375995636,
-0.007480598520487547,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
-0.02036212384700775,
-0.06879384815692902,
-0.029305072501301765,
0.06507006287574768,
0.01367188896983862,
-0.010288052260875702,
-0.029690487310290337,
0.02719021774828434,
0.005719386041164398,
0.009704416617751122,
0.008219484239816666,
0.09463365375995636,
-0.007480598520487547,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
-0.02036212384700775,
-0.06879384815692902,
-0.029305072501301765,
0.06507006287574768,
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0.09463365375995636,
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https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
-0.02036212384700775,
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https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
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https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
-0.02036212384700775,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30896 | [
"Bug",
"Performance"
] | Kmeans Elkans deteriorates with different cores settings
### Describe the bug
Currently I'm trying to run Kmeans Elkan on a large-scale dataset. I have tried to run it with 2 configuration: 8-thread setting and 16-thread setting. While the former one seems to work normally, the running time for the later surge surpri... | 30,896 | [
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https://github.com/scikit-learn/scikit-learn/issues/30893 | [
"Documentation"
] | The `alpha` parameter for lasso regression can only be a `float`
### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso
The line "If an array is passed, penalties are assumed to be specific to the targets. Hence ... | 30,893 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30889 | [
"API",
"RFC"
] | RFC Make `n_outputs_` consistent across regressors
The scikit-learn API defines `classes_` as part of the API for classifier.
A similar handy thing for regressor models, IMO, would be to know if it was fit on a single or multioutput target. Currently, some regressors expose the `n_outputs_` parameter, but other not. ... | 30,889 | [
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https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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0.12291757762432098,
0.006787941325455904,
-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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0.006787941325455904,
-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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0.04227433353662491,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
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0.04227433353662491,
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0.12291757762432098,
0.006787941325455904,
-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30888 | [
"RFC"
] | RFC Write an explicit rule about bumping our minimum dependencies
Roughly a year ago, [SPEC0](https://scientific-python.org/specs/spec-0000/) was rejected following a vote and we said we would write our own rule, but we did not 😅.
Until now 💪.
This was spurred by a [Discord discussion](https://discord.com/channel... | 30,888 | [
0.058588020503520966,
0.04227433353662491,
0.01975768432021141,
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0.12291757762432098,
0.006787941325455904,
-0.0005998914712108672,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30868 | [
"Bug"
] | Calibration cannot handle different dtype for prediction and sample weight.
### Describe the bug
This is from the comment: https://github.com/scikit-learn/scikit-learn/issues/28245#issuecomment-2106845979 . I did not find a corresponding issue. Please close if this is duplicated.
Aligning the types here https://git... | 30,868 | [
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0.02... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
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0.07008171826601028,
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0.02945243939757347,
0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
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0.07008171826601028,
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0.02945243939757347,
0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
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0.07008171826601028,
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
0.050524696707725525,
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0.07008171826601028,
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0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
0.050524696707725525,
0.07085876911878586,
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0.04259197413921356,
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-0.04528074339032173,
0.07008171826601028,
-0.02938789501786232,
0.02945243939757347,
0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
0.050524696707725525,
0.07085876911878586,
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-0.04528074339032173,
0.07008171826601028,
-0.02938789501786232,
0.02945243939757347,
0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30854 | [
"Documentation",
"Sprint",
"good first issue",
"Meta-issue"
] | Add `assert_docstring_consistency` checks
The [`assert_docstring_consistency`](https://github.com/scikit-learn/scikit-learn/blob/4ec5f69061a9c37e0f6b9920e296e06c6b4669ac/sklearn/utils/_testing.py#L734) function allows you to check the consistency between docstring parameters/attributes/returns of objects.
In scikit-l... | 30,854 | [
0.029297100380063057,
0.026496706530451775,
0.0024477315600961447,
0.050524696707725525,
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0.07008171826601028,
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0.02945243939757347,
0.036... |
https://github.com/scikit-learn/scikit-learn/issues/30852 | [
"New Feature"
] | Add a progress bar to the randomized and grid search
### Describe the workflow you want to enable
When working on a large hyper-parameter set, setting the verbosity of `{Randomized, Grid}SearchCV` doesn't make the CV more informative. The display should help users estimate their waiting time and take a look at their ... | 30,852 | [
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0.02070080116391182,
-0.002848008880391717,
0.05197257548570633,
-0.010990038514137268,
0... |
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