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/26128 | [
"New Feature",
"Performance",
"Needs Decision",
"Needs Benchmarks",
"module:ensemble",
"Breaking Change"
] | HistGradientBoosting counts and sample weights
Related issues: #25210
### Current State
`HistGradientBootingClassifier` and `HistGradientBootingRegressor` both:
- Calculate the sample size `count` in histograms
- Use `count` for splitting (mostly excluding split candidates)
- Save the `count` in the final trees and u... | 26,128 | [
-0.014653654769062996,
0.04174579307436943,
0.010355891659855843,
0.00342201697640121,
-0.013389579020440578,
-0.06000608205795288,
-0.017491724342107773,
0.012527072802186012,
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0.012604068033397198,
0.027804607525467873,
-0.048370685428380966,
-0.02375778928399086,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/26128 | [
"New Feature",
"Performance",
"Needs Decision",
"Needs Benchmarks",
"module:ensemble",
"Breaking Change"
] | HistGradientBoosting counts and sample weights
Related issues: #25210
### Current State
`HistGradientBootingClassifier` and `HistGradientBootingRegressor` both:
- Calculate the sample size `count` in histograms
- Use `count` for splitting (mostly excluding split candidates)
- Save the `count` in the final trees and u... | 26,128 | [
-0.012495745904743671,
0.03118736855685711,
0.009134480729699135,
0.007665890734642744,
-0.0053330352529883385,
-0.054954156279563904,
-0.0355202853679657,
0.01774427480995655,
-0.07066038995981216,
0.0029103669803589582,
0.022306259721517563,
-0.044992245733737946,
-0.023360485211014748,
... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26116 | [
"New Feature",
"module:metrics"
] | Changing classification_report to also output the number of predictions along with the support.
### Describe the workflow you want to enable
The current [`classification_report`](https://github.com/scikit-learn/scikit-learn/blob/0eb23ff7b80eafbea9578568a5407ebe5072a11a/sklearn/metrics/_classification.py#L2405) return... | 26,116 | [
-0.009334050118923187,
0.021843425929546356,
0.02067745476961136,
-0.0017252214020118117,
0.07733243703842163,
-0.006261810194700956,
0.0006045806803740561,
-0.012869580648839474,
-0.0032754188869148493,
-0.0009255163022316992,
0.0010614331113174558,
-0.02343672886490822,
0.00575888669118285... |
https://github.com/scikit-learn/scikit-learn/issues/26114 | [
"Documentation"
] | ElasticNet does not support sparse matrices
### Describe the bug
Documentation says that I can use ElasticNet with `ndarray, sparse matrix`, but I can't make it work with sparse.
### Steps/Code to Reproduce
```
from scipy.sparse import csr_matrix
from sklearn.linear_model import ElasticNet
import numpy as ... | 26,114 | [
-0.005942993797361851,
-0.0201142355799675,
0.0013103295350447297,
0.020710622891783714,
0.08817659318447113,
-0.004942139610648155,
0.04749017581343651,
0.01965055987238884,
-0.017531536519527435,
0.011940567754209042,
0.017111632972955704,
0.030794017016887665,
0.0012138808378949761,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26114 | [
"Documentation"
] | ElasticNet does not support sparse matrices
### Describe the bug
Documentation says that I can use ElasticNet with `ndarray, sparse matrix`, but I can't make it work with sparse.
### Steps/Code to Reproduce
```
from scipy.sparse import csr_matrix
from sklearn.linear_model import ElasticNet
import numpy as ... | 26,114 | [
-0.005942993797361851,
-0.0201142355799675,
0.0013103295350447297,
0.020710622891783714,
0.08817659318447113,
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0.04749017581343651,
0.01965055987238884,
-0.017531536519527435,
0.011940567754209042,
0.017111632972955704,
0.030794017016887665,
0.0012138808378949761,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26109 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | HalvingRandomSearchCV and HalvingGridSearchCV do not support multimetric scoring.
According to https://scikit-learn.org/stable/modules/grid_search.html,
_HalvingRandomSearchCV and HalvingGridSearchCV do not support multimetric scoring._
When will this be implemented?
When trying to use multimeric scoring tod... | 26,109 | [
-0.050727132707834244,
-0.025355948135256767,
0.01397001277655363,
-0.05171619728207588,
0.04517154023051262,
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0.02628403902053833,
0.03225312381982803,
-0.021157724782824516,
-0.029945166781544685,
0.027257852256298065,
0.005394110921770334,
-0.04866607487201691,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26109 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | HalvingRandomSearchCV and HalvingGridSearchCV do not support multimetric scoring.
According to https://scikit-learn.org/stable/modules/grid_search.html,
_HalvingRandomSearchCV and HalvingGridSearchCV do not support multimetric scoring._
When will this be implemented?
When trying to use multimeric scoring tod... | 26,109 | [
-0.0413549579679966,
-0.028904054313898087,
0.013327312655746937,
-0.05111465975642204,
0.05058978125452995,
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0.02064269222319126,
0.03704630210995674,
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-0.026703448966145515,
0.03276192769408226,
0.004796879831701517,
-0.04948808625340462,
0.023... |
https://github.com/scikit-learn/scikit-learn/issues/26100 | [
"Performance",
"Regression"
] | Performance regression in KMeans.transform
https://scikit-learn.org/scikit-learn-benchmarks/#cluster.KMeansBenchmark.time_transform?p-representation='dense'&p-algorithm='elkan'&p-init='k-means%2B%2B'&commits=ab7e3d19-b397b8f2
The increment is quite small in absolute value but quite consistent so it could be worth i... | 26,100 | [
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-0.0014114046934992075,
-0.018383245915174484,
-0.007533823139965534,
0.03523288294672966,
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-0.020303156226873398,
0.03674595057964325,
-0.06449379771947861,
-0.023384172469377518,
0.04536289721727371,
0.09488821774721146,
-0.0028071587439626455... |
https://github.com/scikit-learn/scikit-learn/issues/26100 | [
"Performance",
"Regression"
] | Performance regression in KMeans.transform
https://scikit-learn.org/scikit-learn-benchmarks/#cluster.KMeansBenchmark.time_transform?p-representation='dense'&p-algorithm='elkan'&p-init='k-means%2B%2B'&commits=ab7e3d19-b397b8f2
The increment is quite small in absolute value but quite consistent so it could be worth i... | 26,100 | [
-0.015916014090180397,
-0.0016484626103192568,
-0.021096251904964447,
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0.033201444894075394,
0.0022260218393057585,
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0.054034505039453506,
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-0.006810363382101059,
0.06005037575960159,
0.05951766297221184,
0.0017340151825919747... |
https://github.com/scikit-learn/scikit-learn/issues/26099 | [
"Performance",
"Regression"
] | Memory usage regression in RandomForestClassifier.fit on sparse data
There was an old (around the time of the 1.1 release) memory usage regression in `RandomForestClassifier.fit`:
- https://scikit-learn.org/scikit-learn-benchmarks/#ensemble.RandomForestClassifierBenchmark.peakmem_fit?p-representation='sparse'&p-n_j... | 26,099 | [
0.029349111020565033,
0.02973707765340805,
0.022874580696225166,
0.048607997596263885,
0.04383566975593567,
-0.03430579602718353,
-0.024703484028577805,
0.03911278769373894,
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0.02959839440882206,
0.04227032512426376,
-0.03114541992545128,
0.022089054808020592,
-0.02135... |
https://github.com/scikit-learn/scikit-learn/issues/26099 | [
"Performance",
"Regression"
] | Memory usage regression in RandomForestClassifier.fit on sparse data
There was an old (around the time of the 1.1 release) memory usage regression in `RandomForestClassifier.fit`:
- https://scikit-learn.org/scikit-learn-benchmarks/#ensemble.RandomForestClassifierBenchmark.peakmem_fit?p-representation='sparse'&p-n_j... | 26,099 | [
0.027809489518404007,
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0.022160464897751808,
0.03386624529957771,
-0.015844976529479027,
0.00665412237867713,
-0.0155... |
https://github.com/scikit-learn/scikit-learn/issues/26099 | [
"Performance",
"Regression"
] | Memory usage regression in RandomForestClassifier.fit on sparse data
There was an old (around the time of the 1.1 release) memory usage regression in `RandomForestClassifier.fit`:
- https://scikit-learn.org/scikit-learn-benchmarks/#ensemble.RandomForestClassifierBenchmark.peakmem_fit?p-representation='sparse'&p-n_j... | 26,099 | [
0.03606279566884041,
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0.040399305522441864,
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0.03289462998509407,
0.04724946990609169,
-0.037603091448545456,
0.017077140510082245,
0.016... |
https://github.com/scikit-learn/scikit-learn/issues/26099 | [
"Performance",
"Regression"
] | Memory usage regression in RandomForestClassifier.fit on sparse data
There was an old (around the time of the 1.1 release) memory usage regression in `RandomForestClassifier.fit`:
- https://scikit-learn.org/scikit-learn-benchmarks/#ensemble.RandomForestClassifierBenchmark.peakmem_fit?p-representation='sparse'&p-n_j... | 26,099 | [
0.03264370933175087,
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0.04725157096982002,
0.050208382308483124,
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0.04433035850524902,
-0.025411667302250862,
0.021152015775442123,
-0.0194... |
https://github.com/scikit-learn/scikit-learn/issues/26098 | [
"Performance",
"Regression"
] | Memory usage performance regression in several scikit-learn estimators
There is a weird yet significant memory usage increase in many seemingly unrelated scikit-learn estimators that happened on 2023-03-22, e.g.:
- https://scikit-learn.org/scikit-learn-benchmarks/#neighbors.KNeighborsClassifierBenchmark.peakmem_fit... | 26,098 | [
0.032078757882118225,
0.04308151826262474,
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0.004088295623660088,
0.0425623394548893,
0.049545768648386,
-0.02004840038716793,
0.00532... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.03721170872449875,
0.031176405027508736,
0.023182420060038567,
0.03720962256193161,
0.006466258782893419,
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-0.0017559572588652372,
0.0036611470859497786,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.04349921643733978,
0.0254605021327734,
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0.010247168131172657,
-0.024700727313756943,
0.011425725184381008,
-0.0241... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.056431762874126434,
0.0258792657405138,
0.008523092605173588,
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0.02229534462094307,
-0.037663839757442474,
0.008056088350713253,
-0.0008... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.03587205708026886,
0.05600286275148392,
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0.021978231146931648,
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0.019492674618959427,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.03478999808430672,
0.05600477755069733,
0.012772963382303715,
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0.010170139372348785,
0.019079839810729027,
0.003273593494668603,
0.015... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.04586800932884216,
0.05053165555000305,
0.03302186727523804,
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0.005132017191499472,
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0.012953969649970531,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/26097 | [
"Performance",
"Regression"
] | Performance regression in pairwise_distances with the Euclidean metric on sparse data
As spotted by our continuous benchmark suite, there is a more than 2x slowdown in `pairwise_distances` on sparse input data (for the Euclidean metric).
- https://scikit-learn.org/scikit-learn-benchmarks/#metrics.PairwiseDistancesB... | 26,097 | [
-0.04639583081007004,
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0.021582886576652527,
-0.02953960932791233,
-0.010231644846498966,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26090 | [
"API",
"Needs Decision",
"module:ensemble",
"module:linear_model"
] | Rename quantile parameter in QuantileRegressor and HistGradientBoostingRegressor
[`QuantileRegressor`](https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html#sklearn.linear_model.QuantileRegressor) and [`HistGradientBoostingRegressor`](https://scikit-learn.org/dev/modules/generated/... | 26,090 | [
0.01886589080095291,
0.036456845700740814,
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0.028272883966565132,
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0.05021544545888901,
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0.024693189188838005,
0.046846792101860046,
0.014266177080571651,
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26090 | [
"API",
"Needs Decision",
"module:ensemble",
"module:linear_model"
] | Rename quantile parameter in QuantileRegressor and HistGradientBoostingRegressor
[`QuantileRegressor`](https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html#sklearn.linear_model.QuantileRegressor) and [`HistGradientBoostingRegressor`](https://scikit-learn.org/dev/modules/generated/... | 26,090 | [
0.02864312380552292,
0.03388984128832817,
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0.03209323436021805,
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0.05122736468911171,
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0.02298029698431492,
0.04329200088977814,
0.013683630153536797,
0.002485688077285886,
0.00231... |
https://github.com/scikit-learn/scikit-learn/issues/26090 | [
"API",
"Needs Decision",
"module:ensemble",
"module:linear_model"
] | Rename quantile parameter in QuantileRegressor and HistGradientBoostingRegressor
[`QuantileRegressor`](https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html#sklearn.linear_model.QuantileRegressor) and [`HistGradientBoostingRegressor`](https://scikit-learn.org/dev/modules/generated/... | 26,090 | [
0.017789896577596664,
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0.024871692061424255,
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0.04610764607787132,
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0.022476736456155777,
0.0488571971654892,
0.013281340710818768,
-0.009522011503577232,
0.005... |
https://github.com/scikit-learn/scikit-learn/issues/26089 | [
"Bug",
"Needs Triage"
] | 1.2.2: documentation build fails
### Describe the bug
I'm executing below command after `/usr/bin/python3 -sBm build -w --no-isolation`.
Looks like documentation buiuld fails because wrong import in sklearn/__check_build/__init__.py
### Steps/Code to Reproduce
```
/usr/bin/python3 -sBm build -w --no-isolation... | 26,089 | [
0.06486181914806366,
-0.03634091839194298,
0.0040742214769124985,
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0.05182018131017685,
0.03897589072585106,
0.028928684070706367,
0.061088111251592636,
0.03476334363222122,
-0.024200493469834328,
0.03966148942708969,
0.0634610652923584,
0.030920570716261864,
-0.033082... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
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-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26084 | [
"Documentation",
"module:ensemble"
] | Integers vs floats in categorical features in HistGradientBoostingRegressor
### Describe the bug
Hi there,
I'm having an issue with the HistGradientBoostingRegressor model and its native usage of categorical features. The documentation suggests that categorical features should be encoded as integers. However, once... | 26,084 | [
-0.0014229638036340475,
0.05419478937983513,
0.011244003660976887,
-0.029572462663054466,
0.08622037619352341,
0.013810882344841957,
0.07225222140550613,
0.007058487739413977,
-0.04565494507551193,
-0.03231899067759514,
0.03535589575767517,
-0.05359983816742897,
0.022489342838525772,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.0003144953807350248,
0.09559851884841919,
0.002297056373208761,
-0.02141684666275978,
0.03144610673189163,
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0.14061860740184784,
-0.024297358468174934,
0.03920089825987816,
-0.03450794517993927,
0.011683996766805649,
0.0006663796957582235,
0.024897197261452675,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
-0.009581568650901318,
0.07812488079071045,
0.009479879401624203,
-0.01731601357460022,
0.03799387812614441,
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0.13485218584537506,
-0.018315045163035393,
0.037008676677942276,
-0.03030732274055481,
0.015197697095572948,
-0.0034579546190798283,
0.016697946935892105,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.008145528845489025,
0.09131449460983276,
0.005413535982370377,
-0.01820129156112671,
0.03308876231312752,
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0.1394154280424118,
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0.04491065442562103,
-0.032867249101400375,
0.01725202426314354,
0.004246945027261972,
0.023588242009282112,
0.04739... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.0035346492659300566,
0.08860243111848831,
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0.028007159009575844,
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0.14517489075660706,
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0.030609440058469772,
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0.0084715960547328,
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0.021712206304073334,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.001881983713246882,
0.09065276384353638,
0.001060393755324185,
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0.02801387384533882,
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0.1404106765985489,
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0.03733140230178833,
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0.017248231917619705,
-0.006858340930193663,
0.027330568060278893,
0.039... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
-0.012891440652310848,
0.09251188486814499,
0.003551477799192071,
-0.016767974942922592,
0.018383141607046127,
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0.15047140419483185,
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0.015055726282298565,
-0.03972431272268295,
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0.0008561966824345291,
0.01729930378496647,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
-0.006294408347457647,
0.09581734240055084,
0.002122697653248906,
-0.013617492280900478,
0.03429838642477989,
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0.15018874406814575,
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0.03842005878686905,
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0.010579705238342285,
-0.0021951005328446627,
0.02560381405055523,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.010816272348165512,
0.07987971603870392,
0.0006122216582298279,
-0.019603464752435684,
0.029886534437537193,
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0.1424994021654129,
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0.04527340829372406,
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0.015069869346916676,
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0.03559952601790428,
0.041... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.002808164106681943,
0.09576678276062012,
0.008416391909122467,
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0.037886761128902435,
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0.13748390972614288,
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0.03395085781812668,
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0.01627066358923912,
0.003797044511884451,
0.022942088544368744,
0.0584... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
-0.0029592993669211864,
0.09671829640865326,
0.005167050752788782,
-0.02477811463177204,
0.03462729603052139,
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0.12532971799373627,
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0.039866652339696884,
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0.02423211932182312,
0.009763798676431179,
0.023876048624515533,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/26083 | [
"API",
"RFC",
"Array API"
] | RFC/API (Array API) mixing devices and data types with estimators
Right now, if the user fits an estimator using a `pandas.DataFrame`, but passes a `numpy.ndarray` during `predict`, they get a warning due to missing feature names.
The situation is only to get more complicated as we're adding support for more types ... | 26,083 | [
0.003194891382008791,
0.09839735180139542,
0.0023398417979478836,
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0.01899992860853672,
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0.1304493546485901,
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0.00785556249320507,
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0.008237342350184917,
0.0063043576665222645,
0.02398047037422657,
0.04471... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.050224680453538895,
0.056017614901065826,
0.01468606386333704,
-0.016048714518547058,
0.04407525062561035,
0.0313832052052021,
0.0694088563323021,
0.03647555783390999,
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-0.03512270748615265,
0.055339377373456955,
0.020060788840055466,
-0.012709833681583405,
0.034186... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.05765175446867943,
0.07523076236248016,
0.025190241634845734,
-0.028960492461919785,
0.05409359559416771,
0.02570754662156105,
0.08326965570449829,
0.044826388359069824,
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0.04413723573088646,
0.023861099034547806,
-0.004624079912900925,
0.033... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.059088073670864105,
0.07508382946252823,
0.025588639080524445,
-0.02422947809100151,
0.061190295964479446,
0.02966267615556717,
0.05058026313781738,
0.04452703148126602,
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-0.03202587366104126,
0.0639975443482399,
0.03198083117604256,
-0.011175435036420822,
0.0391473... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.05110607296228409,
0.09664374589920044,
0.032751332968473434,
-0.025109384208917618,
0.06548250466585159,
0.03856875002384186,
0.055574044585227966,
0.04685216769576073,
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-0.03981998562812805,
0.058450087904930115,
0.031022559851408005,
-0.01025894284248352,
0.04359... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.03690851479768753,
0.07975953817367554,
0.02952541410923004,
-0.012553594075143337,
0.047107383608818054,
0.023901790380477905,
0.06983266770839691,
0.0516456700861454,
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0.03439488634467125,
0.013232494704425335,
0.0065686977468431,
0.023992678... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.03718024119734764,
0.07654976844787598,
0.02810695581138134,
-0.00987765472382307,
0.051357198506593704,
0.023437218740582466,
0.06788727641105652,
0.05472301319241524,
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0.03262228146195412,
0.016779135912656784,
0.005701432470232248,
0.026537... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.04747135937213898,
0.047252099961042404,
0.03166089206933975,
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0.048530641943216324,
0.03001689910888672,
0.06006385758519173,
0.032143887132406235,
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0.052416592836380005,
0.025250103324651718,
-0.012971028685569763,
0.0329... |
https://github.com/scikit-learn/scikit-learn/issues/26063 | [
"New Feature",
"module:preprocessing"
] | Use `np.uint8` as default dtype for `OneHotEncoder` instead of `np.float64`
### Describe the workflow you want to enable
`sklearn.preprocessing.OneHotEncoder` should use as `dtype` the `np.uint8` by default instead of `np.float64` as it is currently. I don't see the reasoning behind using `np.float64`, this if anythi... | 26,063 | [
-0.0456254705786705,
0.05227658525109291,
0.03953908756375313,
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0.04287654906511307,
0.044171784073114395,
0.06925301253795624,
0.03303363546729088,
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-0.037185825407505035,
0.06683811545372009,
0.0135465944185853,
-0.019933000206947327,
0.04276762... |
https://github.com/scikit-learn/scikit-learn/issues/26062 | [
"New Feature",
"module:ensemble"
] | Add `class_weight` feature to KNeighbors, GradientBoosting and AdaBoost classifiers
### Describe the workflow you want to enable
I propose adding a `class_weight` parameter to the [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), [GradientBoosting... | 26,062 | [
0.0035404847003519535,
0.04173794016242027,
-0.007621229160577059,
-0.029662713408470154,
0.019111137837171555,
0.0029217922128736973,
0.05202913284301758,
0.006157699041068554,
0.025266971439123154,
-0.035854458808898926,
0.020410507917404175,
0.042610861361026764,
-0.05262644961476326,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26062 | [
"New Feature",
"module:ensemble"
] | Add `class_weight` feature to KNeighbors, GradientBoosting and AdaBoost classifiers
### Describe the workflow you want to enable
I propose adding a `class_weight` parameter to the [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), [GradientBoosting... | 26,062 | [
0.0035404847003519535,
0.04173794016242027,
-0.007621229160577059,
-0.029662713408470154,
0.019111137837171555,
0.0029217922128736973,
0.05202913284301758,
0.006157699041068554,
0.025266971439123154,
-0.035854458808898926,
0.020410507917404175,
0.042610861361026764,
-0.05262644961476326,
0... |
https://github.com/scikit-learn/scikit-learn/issues/26061 | [
"New Feature",
"Needs Info"
] | Store spectral embeddings in SpectralClustering
### Describe the workflow you want to enable
Save the spectral embeddings used for clustering in the [SpectralClustering](https://github.com/scikit-learn/scikit-learn/blob/9aaed4987/sklearn/cluster/_spectral.py#L394) class and make them accessible through an attribute, ... | 26,061 | [
-0.015407206490635872,
-0.06046081334352493,
0.0014466919237747788,
0.016672253608703613,
0.016305988654494286,
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-0.015788055956363678,
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0.10050250589847565,
0.004750440362840891,
-0.06185692921280861,
0.09636007249355316,
-0.036839067935943604,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26061 | [
"New Feature",
"Needs Info"
] | Store spectral embeddings in SpectralClustering
### Describe the workflow you want to enable
Save the spectral embeddings used for clustering in the [SpectralClustering](https://github.com/scikit-learn/scikit-learn/blob/9aaed4987/sklearn/cluster/_spectral.py#L394) class and make them accessible through an attribute, ... | 26,061 | [
-0.015407206490635872,
-0.06046081334352493,
0.0014466919237747788,
0.016672253608703613,
0.016305988654494286,
-0.01705639809370041,
-0.015788055956363678,
-0.047695331275463104,
0.10050250589847565,
0.004750440362840891,
-0.06185692921280861,
0.09636007249355316,
-0.036839067935943604,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26061 | [
"New Feature",
"Needs Info"
] | Store spectral embeddings in SpectralClustering
### Describe the workflow you want to enable
Save the spectral embeddings used for clustering in the [SpectralClustering](https://github.com/scikit-learn/scikit-learn/blob/9aaed4987/sklearn/cluster/_spectral.py#L394) class and make them accessible through an attribute, ... | 26,061 | [
-0.015407206490635872,
-0.06046081334352493,
0.0014466919237747788,
0.016672253608703613,
0.016305988654494286,
-0.01705639809370041,
-0.015788055956363678,
-0.047695331275463104,
0.10050250589847565,
0.004750440362840891,
-0.06185692921280861,
0.09636007249355316,
-0.036839067935943604,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26061 | [
"New Feature",
"Needs Info"
] | Store spectral embeddings in SpectralClustering
### Describe the workflow you want to enable
Save the spectral embeddings used for clustering in the [SpectralClustering](https://github.com/scikit-learn/scikit-learn/blob/9aaed4987/sklearn/cluster/_spectral.py#L394) class and make them accessible through an attribute, ... | 26,061 | [
-0.015407206490635872,
-0.06046081334352493,
0.0014466919237747788,
0.016672253608703613,
0.016305988654494286,
-0.01705639809370041,
-0.015788055956363678,
-0.047695331275463104,
0.10050250589847565,
0.004750440362840891,
-0.06185692921280861,
0.09636007249355316,
-0.036839067935943604,
-... |
https://github.com/scikit-learn/scikit-learn/issues/26056 | [
"Documentation",
"Needs Triage"
] | Typo in HistGradientBoosting documentation | no_interaction should be no_interactions
### Describe the issue linked to the documentation
In documentation, the `interaction_cst` field of [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.htm... | 26,056 | [
0.002784896409139037,
-0.06117008998990059,
-0.003977959044277668,
-0.007714089471846819,
0.03573284670710564,
-0.016998276114463806,
0.06561671942472458,
-0.040882453322410583,
-0.006264322903007269,
0.022830912843346596,
0.03434905409812927,
-0.08915335685014725,
0.036285627633333206,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/26056 | [
"Documentation",
"Needs Triage"
] | Typo in HistGradientBoosting documentation | no_interaction should be no_interactions
### Describe the issue linked to the documentation
In documentation, the `interaction_cst` field of [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.htm... | 26,056 | [
0.0015171681297942996,
-0.056235410273075104,
-0.004486937541514635,
-0.010464371182024479,
0.03414212539792061,
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0.060649994760751724,
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0.024843160063028336,
0.03491077572107315,
-0.08464135229587555,
0.03672698512673378,
... |
https://github.com/scikit-learn/scikit-learn/issues/26056 | [
"Documentation",
"Needs Triage"
] | Typo in HistGradientBoosting documentation | no_interaction should be no_interactions
### Describe the issue linked to the documentation
In documentation, the `interaction_cst` field of [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.htm... | 26,056 | [
0.00568747753277421,
-0.04688819497823715,
-0.006523618008941412,
-0.014180940575897694,
0.029087282717227936,
-0.016841432079672813,
0.06615443527698517,
-0.03584001213312149,
-0.007556932047009468,
0.010639145970344543,
0.035906415432691574,
-0.08157230913639069,
0.04096103087067604,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/26050 | [
"API"
] | SLEP006: globally setting request values
One of the issues raised in https://github.com/scikit-learn/scikit-learn/issues/25776 and https://github.com/scikit-learn/scikit-learn/issues/23928 is about a code such as following to work under SLEP6:
```py
est = AdaBoostClassifier(LogisticRegression())
est.fit(X, y, sam... | 26,050 | [
0.007379763759672642,
0.05125594511628151,
0.055374644696712494,
0.005441603250801563,
0.03979191929101944,
-0.03185716271400452,
0.042929962277412415,
-0.025243569165468216,
0.03608384355902672,
0.006117150187492371,
0.02765447460114956,
0.04537263885140419,
-0.02268831990659237,
0.051731... |
https://github.com/scikit-learn/scikit-learn/issues/26050 | [
"API"
] | SLEP006: globally setting request values
One of the issues raised in https://github.com/scikit-learn/scikit-learn/issues/25776 and https://github.com/scikit-learn/scikit-learn/issues/23928 is about a code such as following to work under SLEP6:
```py
est = AdaBoostClassifier(LogisticRegression())
est.fit(X, y, sam... | 26,050 | [
0.007379763759672642,
0.05125594511628151,
0.055374644696712494,
0.005441603250801563,
0.03979191929101944,
-0.03185716271400452,
0.042929962277412415,
-0.025243569165468216,
0.03608384355902672,
0.006117150187492371,
0.02765447460114956,
0.04537263885140419,
-0.02268831990659237,
0.051731... |
https://github.com/scikit-learn/scikit-learn/issues/26050 | [
"API"
] | SLEP006: globally setting request values
One of the issues raised in https://github.com/scikit-learn/scikit-learn/issues/25776 and https://github.com/scikit-learn/scikit-learn/issues/23928 is about a code such as following to work under SLEP6:
```py
est = AdaBoostClassifier(LogisticRegression())
est.fit(X, y, sam... | 26,050 | [
0.007379763759672642,
0.05125594511628151,
0.055374644696712494,
0.005441603250801563,
0.03979191929101944,
-0.03185716271400452,
0.042929962277412415,
-0.025243569165468216,
0.03608384355902672,
0.006117150187492371,
0.02765447460114956,
0.04537263885140419,
-0.02268831990659237,
0.051731... |
https://github.com/scikit-learn/scikit-learn/issues/26050 | [
"API"
] | SLEP006: globally setting request values
One of the issues raised in https://github.com/scikit-learn/scikit-learn/issues/25776 and https://github.com/scikit-learn/scikit-learn/issues/23928 is about a code such as following to work under SLEP6:
```py
est = AdaBoostClassifier(LogisticRegression())
est.fit(X, y, sam... | 26,050 | [
0.007379763759672642,
0.05125594511628151,
0.055374644696712494,
0.005441603250801563,
0.03979191929101944,
-0.03185716271400452,
0.042929962277412415,
-0.025243569165468216,
0.03608384355902672,
0.006117150187492371,
0.02765447460114956,
0.04537263885140419,
-0.02268831990659237,
0.051731... |
https://github.com/scikit-learn/scikit-learn/issues/26050 | [
"API"
] | SLEP006: globally setting request values
One of the issues raised in https://github.com/scikit-learn/scikit-learn/issues/25776 and https://github.com/scikit-learn/scikit-learn/issues/23928 is about a code such as following to work under SLEP6:
```py
est = AdaBoostClassifier(LogisticRegression())
est.fit(X, y, sam... | 26,050 | [
0.007379763759672642,
0.05125594511628151,
0.055374644696712494,
0.005441603250801563,
0.03979191929101944,
-0.03185716271400452,
0.042929962277412415,
-0.025243569165468216,
0.03608384355902672,
0.006117150187492371,
0.02765447460114956,
0.04537263885140419,
-0.02268831990659237,
0.051731... |
https://github.com/scikit-learn/scikit-learn/issues/26045 | [
"API"
] | SLEP006: introduction of metadata routing through a feature flag
In https://github.com/scikit-learn/scikit-learn/issues/25776 we started talking about introducing SLEP6 through a feature flag. This would mean, the user can control whether SLEP6 is enabled or not. This will be done via our global configs:
```py
imp... | 26,045 | [
0.008932662196457386,
0.02662545070052147,
0.0017566218739375472,
-0.0010200937977060676,
0.020207971334457397,
-0.02913806214928627,
0.05793781578540802,
-0.05282139405608177,
-0.022536175325512886,
-0.043852850794792175,
0.045168131589889526,
0.10585390031337738,
-0.05760304629802704,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26045 | [
"API"
] | SLEP006: introduction of metadata routing through a feature flag
In https://github.com/scikit-learn/scikit-learn/issues/25776 we started talking about introducing SLEP6 through a feature flag. This would mean, the user can control whether SLEP6 is enabled or not. This will be done via our global configs:
```py
imp... | 26,045 | [
0.008932662196457386,
0.02662545070052147,
0.0017566218739375472,
-0.0010200937977060676,
0.020207971334457397,
-0.02913806214928627,
0.05793781578540802,
-0.05282139405608177,
-0.022536175325512886,
-0.043852850794792175,
0.045168131589889526,
0.10585390031337738,
-0.05760304629802704,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26040 | [
"New Feature",
"module:feature_extraction",
"Needs Decision - Include Feature"
] | Implementation of an additional patch extraction strategy.
### Describe the workflow you want to enable
I think it would be valuable to add a function that extracts non-overlapping patches from an image. This alternative patch extraction strategy might be more suitable for certain use cases where overlapping patches ... | 26,040 | [
-0.023146742954850197,
0.024203110486268997,
-0.0012097577564418316,
0.06045842915773392,
-0.020045306533575058,
-0.013663334771990776,
0.003626508405432105,
0.021181851625442505,
-0.008318418636918068,
-0.030812397599220276,
0.05119382590055466,
0.005842084065079689,
-0.01788334921002388,
... |
https://github.com/scikit-learn/scikit-learn/issues/26040 | [
"New Feature",
"module:feature_extraction",
"Needs Decision - Include Feature"
] | Implementation of an additional patch extraction strategy.
### Describe the workflow you want to enable
I think it would be valuable to add a function that extracts non-overlapping patches from an image. This alternative patch extraction strategy might be more suitable for certain use cases where overlapping patches ... | 26,040 | [
-0.023146742954850197,
0.024203110486268997,
-0.0012097577564418316,
0.06045842915773392,
-0.020045306533575058,
-0.013663334771990776,
0.003626508405432105,
0.021181851625442505,
-0.008318418636918068,
-0.030812397599220276,
0.05119382590055466,
0.005842084065079689,
-0.01788334921002388,
... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
0.033207159489393234,
0.0932261198759079,
-0.021183544769883156,
-0.0276840478181839,
-0.0027006608434021473,
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0.05456572398543358,
-0.06026806682348251,
0.011208534240722656,
-0.004251161590218544,
0.058839451521635056,
0.05391883850097656,
-0.0037055672146379948,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
0.0357944592833519,
0.09119990468025208,
-0.024997951462864876,
-0.035941723734140396,
-0.006620698142796755,
0.00009610053530195728,
0.07157950103282928,
-0.0608261302113533,
0.016760004684329033,
-0.006972986273467541,
0.050678424537181854,
0.044340889900922775,
-0.0023904531262815,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
0.024955706670880318,
0.08418822288513184,
-0.01591332070529461,
-0.04417186602950096,
-0.0032721078023314476,
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0.05277794972062111,
-0.05394439026713371,
0.015951596200466156,
0.006454932037740946,
0.07323361933231354,
0.06627120822668076,
-0.010372016578912735,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
0.028567597270011902,
0.09298479557037354,
-0.018013667315244675,
-0.03627713397145271,
-0.0008521894924342632,
-0.005332716275006533,
0.04920627549290657,
-0.0551932118833065,
0.024506235495209694,
0.0032552543561905622,
0.06609678268432617,
0.06327269226312637,
-0.012303551658987999,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
0.02633674442768097,
0.08769455552101135,
-0.01790013536810875,
-0.03932363912463188,
-0.00022629676095675677,
-0.004979755729436874,
0.050879403948783875,
-0.053702887147665024,
0.024507008492946625,
0.00314655271358788,
0.06324321031570435,
0.06443719565868378,
-0.009751183912158012,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
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https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
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https://github.com/scikit-learn/scikit-learn/issues/26035 | [
"Documentation"
] | Request for project inclusion in scikit-learn related projects: skforecast
### Describe the issue linked to the documentation
Hi! We have seen on the [Related Projects](https://scikit-learn.org/stable/related_projects.html) page that projects closely related to **scikit-learn** are mentioned. We would like to propose... | 26,035 | [
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https://github.com/scikit-learn/scikit-learn/issues/26026 | [
"New Feature",
"Needs Info",
"Needs Decision - Include Feature"
] | `Fmax` score (or maximum of F1/Fbeta)
### Describe the workflow you want to enable
The maximum of F1 across thresholds is a well-studied metric and it is both robust and valid in binary and multilabel classification problems. Basically, it can be computed with `precision_recall_curve`, as shown below, for binary pro... | 26,026 | [
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https://github.com/scikit-learn/scikit-learn/issues/26026 | [
"New Feature",
"Needs Info",
"Needs Decision - Include Feature"
] | `Fmax` score (or maximum of F1/Fbeta)
### Describe the workflow you want to enable
The maximum of F1 across thresholds is a well-studied metric and it is both robust and valid in binary and multilabel classification problems. Basically, it can be computed with `precision_recall_curve`, as shown below, for binary pro... | 26,026 | [
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https://github.com/scikit-learn/scikit-learn/issues/26026 | [
"New Feature",
"Needs Info",
"Needs Decision - Include Feature"
] | `Fmax` score (or maximum of F1/Fbeta)
### Describe the workflow you want to enable
The maximum of F1 across thresholds is a well-studied metric and it is both robust and valid in binary and multilabel classification problems. Basically, it can be computed with `precision_recall_curve`, as shown below, for binary pro... | 26,026 | [
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https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.021357817575335503,
0.090168297290802,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.021357817575335503,
0.090168297290802,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.10159214586019516,
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-0.01974082924425602,
0.021357817575335503,
0.090168297290802,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.10159214586019516,
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0.021357817575335503,
0.090168297290802,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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0.10159214586019516,
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0.021357817575335503,
0.090168297290802,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
-0.017468353733420372,
0.10159214586019516,
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0.09609... |
https://github.com/scikit-learn/scikit-learn/issues/26024 | [
"API",
"Meta-issue",
"Array API"
] | Make more of the "tools" of scikit-learn Array API compatible
🚨 🚧 This issue requires a bit of patience and experience to contribute to 🚧 🚨
- Original issue introducing array API in scikit-learn: #22352
- array API official doc/spec: https://data-apis.org/array-api/
- scikit-learn doc: https://scikit-learn.o... | 26,024 | [
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