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/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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0.05622491240501404,
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https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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0.05622491240501404,
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https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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0.05622491240501404,
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https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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0.05622491240501404,
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-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
0.041101809591054916,
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0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
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0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
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0.058001480996608734,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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0.05622491240501404,
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https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
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0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
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0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
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0.058001480996608734,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
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0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
0.041101809591054916,
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0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
0.041101809591054916,
0.05449190363287926,
-0.01275148056447506,
0.01478977408260107,
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-0.014337758533656597,
0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24875 | [
"Easy",
"Build / CI",
"cython",
"Meta-issue",
"C/C++"
] | MAINT Remove all Cython, C and C++ compilations warnings
## Context
scikit-learn builds with Cython, C and C++ warnings (when building wheels or for when installing locally (for development)). There are several kinds of warnings, each kind having its own cause, solutions and mitigations.
### 🏷 Use of the deprec... | 24,875 | [
-0.0033310248982161283,
0.05622491240501404,
-0.008077607490122318,
-0.007057316601276398,
0.041101809591054916,
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-0.014337758533656597,
0.03517940267920494,
0.058001480996608734,
-0.0041588423773646355,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24872 | [
"New Feature",
"module:inspection"
] | partial_dependence should respect sample weights
### Describe the workflow you want to enable
Currently, the inspect.partial_dependence funtions calculate arithmetic averages over predictions. For models fitted with sample weights, this is between suboptimal and wrong.
### Describe your proposed solution
Add new ar... | 24,872 | [
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0.02614928036928177,
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0.0228040162473917,
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0.03459799662232399,
0.007737002335488796,
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0.02240... |
https://github.com/scikit-learn/scikit-learn/issues/24872 | [
"New Feature",
"module:inspection"
] | partial_dependence should respect sample weights
### Describe the workflow you want to enable
Currently, the inspect.partial_dependence funtions calculate arithmetic averages over predictions. For models fitted with sample weights, this is between suboptimal and wrong.
### Describe your proposed solution
Add new ar... | 24,872 | [
-0.003192343981936574,
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0.025140706449747086,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24872 | [
"New Feature",
"module:inspection"
] | partial_dependence should respect sample weights
### Describe the workflow you want to enable
Currently, the inspect.partial_dependence funtions calculate arithmetic averages over predictions. For models fitted with sample weights, this is between suboptimal and wrong.
### Describe your proposed solution
Add new ar... | 24,872 | [
-0.0045604403130710125,
0.07471473515033722,
0.01897958666086197,
0.07170324772596359,
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0.01934080570936203,
0.03132035210728645,
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-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24872 | [
"New Feature",
"module:inspection"
] | partial_dependence should respect sample weights
### Describe the workflow you want to enable
Currently, the inspect.partial_dependence funtions calculate arithmetic averages over predictions. For models fitted with sample weights, this is between suboptimal and wrong.
### Describe your proposed solution
Add new ar... | 24,872 | [
-0.008801182731986046,
0.05965391919016838,
0.03140141814947128,
0.06328850984573364,
-0.0003675082407426089,
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0.010716426186263561,
0.04431327059864998,
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-... |
https://github.com/scikit-learn/scikit-learn/issues/24872 | [
"New Feature",
"module:inspection"
] | partial_dependence should respect sample weights
### Describe the workflow you want to enable
Currently, the inspect.partial_dependence funtions calculate arithmetic averages over predictions. For models fitted with sample weights, this is between suboptimal and wrong.
### Describe your proposed solution
Add new ar... | 24,872 | [
0.005452118813991547,
0.025134999305009842,
0.01132366992533207,
0.03766816854476929,
0.016575418412685394,
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https://github.com/scikit-learn/scikit-learn/issues/24867 | [
"New Feature",
"Needs Triage"
] | Logisitic Regression: Questions
### Describe the workflow you want to enable
Hi all,
I found an exact fomrula on my own playing with maths for the logisitc regression.
I do not know if it is implemented yet for logisitc regression in sklearn.
Sorry if it is
### Describe your proposed solution
https://mediu... | 24,867 | [
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https://github.com/scikit-learn/scikit-learn/issues/24867 | [
"New Feature",
"Needs Triage"
] | Logisitic Regression: Questions
### Describe the workflow you want to enable
Hi all,
I found an exact fomrula on my own playing with maths for the logisitc regression.
I do not know if it is implemented yet for logisitc regression in sklearn.
Sorry if it is
### Describe your proposed solution
https://mediu... | 24,867 | [
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https://github.com/scikit-learn/scikit-learn/issues/24867 | [
"New Feature",
"Needs Triage"
] | Logisitic Regression: Questions
### Describe the workflow you want to enable
Hi all,
I found an exact fomrula on my own playing with maths for the logisitc regression.
I do not know if it is implemented yet for logisitc regression in sklearn.
Sorry if it is
### Describe your proposed solution
https://mediu... | 24,867 | [
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https://github.com/scikit-learn/scikit-learn/issues/24864 | [
"New Feature",
"module:linear_model"
] | LogisticRegression coefficients should be stored in column major order to improve sparse inference performance.
### Describe the workflow you want to enable
I want to enable the workflow of training a `LogisticRegression` model on sparse data, with substantial numbers of features and classes, with acceptable perfor... | 24,864 | [
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https://github.com/scikit-learn/scikit-learn/issues/24864 | [
"New Feature",
"module:linear_model"
] | LogisticRegression coefficients should be stored in column major order to improve sparse inference performance.
### Describe the workflow you want to enable
I want to enable the workflow of training a `LogisticRegression` model on sparse data, with substantial numbers of features and classes, with acceptable perfor... | 24,864 | [
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https://github.com/scikit-learn/scikit-learn/issues/24864 | [
"New Feature",
"module:linear_model"
] | LogisticRegression coefficients should be stored in column major order to improve sparse inference performance.
### Describe the workflow you want to enable
I want to enable the workflow of training a `LogisticRegression` model on sparse data, with substantial numbers of features and classes, with acceptable perfor... | 24,864 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
0.018612811341881752,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
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https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
0.018612811341881752,
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0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
0.018612811341881752,
0.03997433930635452,
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0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
0.018612811341881752,
0.03997433930635452,
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0.10474829375743866,
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0.06165993586182594,
0.04324548318982124,
0.009992876090109348,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24862 | [
"Sprint",
"good first issue",
"Meta-issue",
"Validation"
] | Make automatic validation for all scikit-learn public functions
PR #22722 introduced a decorator to validate the parameters of functions. We now need to use it for all functions where it is applicable.
Please open one PR per function. The title of the PR must mention which function it's dealing with. We recommend u... | 24,862 | [
-0.008106524124741554,
0.018612811341881752,
0.03997433930635452,
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0.06165993586182594,
0.04324548318982124,
0.009992876090109348,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
-0.03793647140264511,
0.05639635771512985,
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0.03747513145208359,
0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
-0.03793647140264511,
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24860 | [
"New Feature"
] | Preserving dtypes for DataFrame output by transformers that do not modify the input values
### Describe the workflow you want to enable
It would be nice to optionally preserve the dtypes of the input using pandas output for transformers #72.
Dtypes can contain information relevant for later steps of the analyses. ... | 24,860 | [
-0.03793647140264511,
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0.063717... |
https://github.com/scikit-learn/scikit-learn/issues/24859 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48488&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 08, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
The job got killed. It ... | 24,859 | [
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https://github.com/scikit-learn/scikit-learn/issues/24859 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48488&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 08, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no longer fail... | 24,859 | [
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https://github.com/scikit-learn/scikit-learn/issues/24859 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48488&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 08, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
The error on the Novemb... | 24,859 | [
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https://github.com/scikit-learn/scikit-learn/issues/24859 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly_PyPy.pypy3 ⚠️
**CI failed on [Linux_Nightly_PyPy.pypy3](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=48488&view=logs&j=0b16f832-29d6-5b92-1c23-eb006f606a66)** (Nov 08, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
The PyPy scheduled buil... | 24,859 | [
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https://github.com/scikit-learn/scikit-learn/issues/24852 | [
"Enhancement"
] | Make it possible to specify interaction_cst and monotonic_cst with feature names.
### Describe the workflow you want to enable
Instead of passing an array of monotonicity constraints (`-1` for a decrease constraint, `+1` for an increase constraint or `0` for no constraint) specified by feature positions in the trai... | 24,852 | [
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0.0898... |
https://github.com/scikit-learn/scikit-learn/issues/24852 | [
"Enhancement"
] | Make it possible to specify interaction_cst and monotonic_cst with feature names.
### Describe the workflow you want to enable
Instead of passing an array of monotonicity constraints (`-1` for a decrease constraint, `+1` for an increase constraint or `0` for no constraint) specified by feature positions in the trai... | 24,852 | [
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0.091... |
https://github.com/scikit-learn/scikit-learn/issues/24852 | [
"Enhancement"
] | Make it possible to specify interaction_cst and monotonic_cst with feature names.
### Describe the workflow you want to enable
Instead of passing an array of monotonicity constraints (`-1` for a decrease constraint, `+1` for an increase constraint or `0` for no constraint) specified by feature positions in the trai... | 24,852 | [
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https://github.com/scikit-learn/scikit-learn/issues/24852 | [
"Enhancement"
] | Make it possible to specify interaction_cst and monotonic_cst with feature names.
### Describe the workflow you want to enable
Instead of passing an array of monotonicity constraints (`-1` for a decrease constraint, `+1` for an increase constraint or `0` for no constraint) specified by feature positions in the trai... | 24,852 | [
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https://github.com/scikit-learn/scikit-learn/issues/24852 | [
"Enhancement"
] | Make it possible to specify interaction_cst and monotonic_cst with feature names.
### Describe the workflow you want to enable
Instead of passing an array of monotonicity constraints (`-1` for a decrease constraint, `+1` for an increase constraint or `0` for no constraint) specified by feature positions in the trai... | 24,852 | [
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0.090320... |
https://github.com/scikit-learn/scikit-learn/issues/24846 | [
"Bug"
] | "X does not have valid feature names" when training on DataFrame input with MLP
### Describe the bug
Bug happens only when early_stopping option is True.
Looks like `y_val` and `y` inside of `_fit()` training loop are ndarrays, but model detects column names of input `pandas.DataFrame` on `self._validate_input` st... | 24,846 | [
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https://github.com/scikit-learn/scikit-learn/issues/24846 | [
"Bug"
] | "X does not have valid feature names" when training on DataFrame input with MLP
### Describe the bug
Bug happens only when early_stopping option is True.
Looks like `y_val` and `y` inside of `_fit()` training loop are ndarrays, but model detects column names of input `pandas.DataFrame` on `self._validate_input` st... | 24,846 | [
0.04139479622244835,
0.009883222170174122,
0.04699639976024628,
-0.02536878176033497,
0.15207037329673767,
0.033957209438085556,
0.08676519989967346,
0.02791242115199566,
0.020376887172460556,
0.030361713841557503,
0.04525255784392357,
0.03976203501224518,
0.014578486792743206,
0.033496711... |
https://github.com/scikit-learn/scikit-learn/issues/24846 | [
"Bug"
] | "X does not have valid feature names" when training on DataFrame input with MLP
### Describe the bug
Bug happens only when early_stopping option is True.
Looks like `y_val` and `y` inside of `_fit()` training loop are ndarrays, but model detects column names of input `pandas.DataFrame` on `self._validate_input` st... | 24,846 | [
0.04139479622244835,
0.009883222170174122,
0.04699639976024628,
-0.02536878176033497,
0.15207037329673767,
0.033957209438085556,
0.08676519989967346,
0.02791242115199566,
0.020376887172460556,
0.030361713841557503,
0.04525255784392357,
0.03976203501224518,
0.014578486792743206,
0.033496711... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
0.0054726204834878445,
0.029102766886353493,
0.002971607493236661,
-0.013280139304697514,
0.029353532940149307,
-0.014290438033640385,
0.0629085823893547,
-0.010240199975669384,
-0.014622410759329796,
0.007592257112264633,
0.014550857245922089,
-0.03339845687150955,
-0.04327329993247986,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.008981910534203053,
0.038505904376506805,
0.009238493628799915,
-0.0037749793846160173,
0.04237071052193642,
-0.02242257259786129,
0.05553516373038292,
-0.0016426517395302653,
0.005838459357619286,
0.00725410645827651,
0.00301575381308794,
-0.044775839895009995,
-0.040576279163360596,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.012644053436815739,
0.03730927035212517,
0.010881771333515644,
-0.005123447626829147,
0.041192661970853806,
-0.021711334586143494,
0.05266491323709488,
-0.004347614012658596,
0.008651554584503174,
0.007981051690876484,
-0.0027969107031822205,
-0.04228070378303528,
-0.04111534729599953,
... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.012898806482553482,
0.03645753487944603,
0.01225293893367052,
-0.004668669309467077,
0.04330197721719742,
-0.02394944615662098,
0.04916168004274368,
-0.005921670235693455,
0.013423040509223938,
0.007508779875934124,
-0.0036170680541545153,
-0.04771288484334946,
-0.0375753678381443,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.017137806862592697,
0.04183684661984444,
0.013528701849281788,
-0.002245232928544283,
0.044766802340745926,
-0.022749144583940506,
0.050658438354730606,
-0.006898731924593449,
0.009779025800526142,
0.0022803847678005695,
-0.011286674998700619,
-0.03790095075964928,
-0.03392421081662178,
... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.007105471100658178,
0.039855800569057465,
0.010300025343894958,
-0.01517147570848465,
0.03941378369927406,
-0.026316916570067406,
0.04995876923203468,
-0.004088705871254206,
0.006049728486686945,
0.00579097168520093,
-0.004679462872445583,
-0.039175331592559814,
-0.03654616326093674,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24845 | [
"New Feature",
"module:ensemble"
] | Add user friendly string options for interaction constraints in HistGradientBoosting*
### Describe the workflow you want to enable
```python
model_no_interactions = HistGradientBoostingRegressor(
interaction_cst="no_interactions"
)
model_pairwise_interactions = HistGradientBoostingRegressor(
interact... | 24,845 | [
-0.0025286334566771984,
0.035698067396879196,
0.0088049853220582,
-0.016698097810149193,
0.03821922838687897,
-0.02486732043325901,
0.053168993443250656,
-0.004703260958194733,
0.0029028013814240694,
0.006588167045265436,
-0.001456123311072588,
-0.04052167013287544,
-0.03722440078854561,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
https://github.com/scikit-learn/scikit-learn/issues/24840 | [
"Enhancement",
"Performance"
] | OrdinalEncoder becomes slow in presence of numerous `nan` values
### Describe the bug
I want to use ordinalencoder with a feature with ~10 categories, but >99% values nan.
Execution time is very slow. ~4min for a 1e5 rows.
But strangely enough, if the feature is not sparsed, then fitting time is ~1s
Worth menti... | 24,840 | [
-0.007606152445077896,
0.0593322217464447,
0.040971361100673676,
-0.02153513953089714,
0.08681419491767883,
0.01565648801624775,
-0.007569832727313042,
0.03803807497024536,
-0.07208411395549774,
0.009573779068887234,
0.062207672744989395,
0.018502896651625633,
0.04108726605772972,
0.029209... |
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