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/30761 | [
"Build / CI"
] | Intermittent HTTP 403 on fetch_california_housing and other Figshare hosted data on Azure CI
Already noticed in https://github.com/scikit-learn/scikit-learn/pull/30636#issuecomment-2604425878.
This seems to happen from time to time in doctests ([build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/result... | 30,761 | [
0.012938341125845909,
0.07310691475868225,
0.005639990326017141,
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0.0409969724714756,
0.023533431813120842,
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0.028829598799347878,
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0.03269147872924805,
0.0269... |
https://github.com/scikit-learn/scikit-learn/issues/30761 | [
"Build / CI"
] | Intermittent HTTP 403 on fetch_california_housing and other Figshare hosted data on Azure CI
Already noticed in https://github.com/scikit-learn/scikit-learn/pull/30636#issuecomment-2604425878.
This seems to happen from time to time in doctests ([build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/result... | 30,761 | [
0.012938341125845909,
0.07310691475868225,
0.005639990326017141,
-0.016286231577396393,
0.0409969724714756,
0.023533431813120842,
-0.0018782392144203186,
0.05242452397942543,
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0.028829598799347878,
-0.03152617812156677,
-0.02071811817586422,
0.03269147872924805,
0.0269... |
https://github.com/scikit-learn/scikit-learn/issues/30761 | [
"Build / CI"
] | Intermittent HTTP 403 on fetch_california_housing and other Figshare hosted data on Azure CI
Already noticed in https://github.com/scikit-learn/scikit-learn/pull/30636#issuecomment-2604425878.
This seems to happen from time to time in doctests ([build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/result... | 30,761 | [
0.012938341125845909,
0.07310691475868225,
0.005639990326017141,
-0.016286231577396393,
0.0409969724714756,
0.023533431813120842,
-0.0018782392144203186,
0.05242452397942543,
-0.02003505825996399,
0.028829598799347878,
-0.03152617812156677,
-0.02071811817586422,
0.03269147872924805,
0.0269... |
https://github.com/scikit-learn/scikit-learn/issues/30761 | [
"Build / CI"
] | Intermittent HTTP 403 on fetch_california_housing and other Figshare hosted data on Azure CI
Already noticed in https://github.com/scikit-learn/scikit-learn/pull/30636#issuecomment-2604425878.
This seems to happen from time to time in doctests ([build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/result... | 30,761 | [
0.012938341125845909,
0.07310691475868225,
0.005639990326017141,
-0.016286231577396393,
0.0409969724714756,
0.023533431813120842,
-0.0018782392144203186,
0.05242452397942543,
-0.02003505825996399,
0.028829598799347878,
-0.03152617812156677,
-0.02071811817586422,
0.03269147872924805,
0.0269... |
https://github.com/scikit-learn/scikit-learn/issues/30748 | [
"Documentation"
] | Unexpected behavior for subclassing `Pipeline`
### Describe the issue linked to the documentation
Hey, I don't know if I should call this a bug, but for me at least it was unexpected behavior. I tried to subclass from `Pipeline`
to implement a customization, so having a simplified configuration, which is used to buil... | 30,748 | [
0.032908305525779724,
0.038317110389471054,
0.03210657089948654,
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0.06913001090288162,
0.03890030086040497,
0.10915590822696686,
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0.001400801818817854,
0.02171933837234974,
-0.012531647458672523,
0.012814400717616081,
-0.0239... |
https://github.com/scikit-learn/scikit-learn/issues/30748 | [
"Documentation"
] | Unexpected behavior for subclassing `Pipeline`
### Describe the issue linked to the documentation
Hey, I don't know if I should call this a bug, but for me at least it was unexpected behavior. I tried to subclass from `Pipeline`
to implement a customization, so having a simplified configuration, which is used to buil... | 30,748 | [
0.032908305525779724,
0.038317110389471054,
0.03210657089948654,
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0.06913001090288162,
0.03890030086040497,
0.10915590822696686,
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0.001400801818817854,
0.02171933837234974,
-0.012531647458672523,
0.012814400717616081,
-0.0239... |
https://github.com/scikit-learn/scikit-learn/issues/30748 | [
"Documentation"
] | Unexpected behavior for subclassing `Pipeline`
### Describe the issue linked to the documentation
Hey, I don't know if I should call this a bug, but for me at least it was unexpected behavior. I tried to subclass from `Pipeline`
to implement a customization, so having a simplified configuration, which is used to buil... | 30,748 | [
0.032908305525779724,
0.038317110389471054,
0.03210657089948654,
-0.018760811537504196,
0.06913001090288162,
0.03890030086040497,
0.10915590822696686,
-0.02708255685865879,
-0.04052197188138962,
0.001400801818817854,
0.02171933837234974,
-0.012531647458672523,
0.012814400717616081,
-0.0239... |
https://github.com/scikit-learn/scikit-learn/issues/30748 | [
"Documentation"
] | Unexpected behavior for subclassing `Pipeline`
### Describe the issue linked to the documentation
Hey, I don't know if I should call this a bug, but for me at least it was unexpected behavior. I tried to subclass from `Pipeline`
to implement a customization, so having a simplified configuration, which is used to buil... | 30,748 | [
0.032908305525779724,
0.038317110389471054,
0.03210657089948654,
-0.018760811537504196,
0.06913001090288162,
0.03890030086040497,
0.10915590822696686,
-0.02708255685865879,
-0.04052197188138962,
0.001400801818817854,
0.02171933837234974,
-0.012531647458672523,
0.012814400717616081,
-0.0239... |
https://github.com/scikit-learn/scikit-learn/issues/30744 | [
"Needs Reproducible Code"
] | Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive
My team changed to scikit-learn v1.6.1 this week. We had v1.5.1 before. Our code crashes in this exact line with the error "Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive'".
We cannot d... | 30,744 | [
-0.012926554307341576,
0.015897003933787346,
0.010687265545129776,
0.0075444323010742664,
0.06962298601865768,
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0.01619517058134079,
0.03922716900706291,
0.08233100175857544,
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0.07584444433450699,
0.0729944035410881,
-0.01134004071354866,
0.0750... |
https://github.com/scikit-learn/scikit-learn/issues/30744 | [
"Needs Reproducible Code"
] | Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive
My team changed to scikit-learn v1.6.1 this week. We had v1.5.1 before. Our code crashes in this exact line with the error "Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive'".
We cannot d... | 30,744 | [
-0.018740640953183174,
0.04247928783297539,
0.023418858647346497,
-0.0023238572757691145,
0.05809378623962402,
0.009858046658337116,
0.026080284267663956,
0.029115892946720123,
0.08123839646577835,
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0.062050145119428635,
0.08458209037780762,
-0.02028086595237255,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30744 | [
"Needs Reproducible Code"
] | Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive
My team changed to scikit-learn v1.6.1 this week. We had v1.5.1 before. Our code crashes in this exact line with the error "Unexpected <class 'AttributeError'>. 'LinearRegression' object has no attribute 'positive'".
We cannot d... | 30,744 | [
-0.006847134791314602,
0.009049663320183754,
0.004645174369215965,
0.007018798030912876,
0.06657908111810684,
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0.006777425296604633,
0.04262688755989075,
0.07880949974060059,
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0.07532384991645813,
0.07529783993959427,
-0.01353244949132204,
0.061... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30742 | [
"Documentation",
"Validation"
] | `y`, and `groups` parameters to`StratifiedGroupKFold.split()` are optional
### Describe the bug
`StratifiedGroupKFold.split` has the signature `(self, X, y=None, groups=None)` indicating that both `y`, and `groups` may not be specified when calling `split`.
However, omitting only `groups` results in `TypeError: iter... | 30,742 | [
0.012731725350022316,
0.026058057323098183,
0.029009679332375526,
0.03470616415143013,
0.057778652757406235,
0.0028790170326828957,
0.10182058811187744,
0.0008116124081425369,
-0.025368671864271164,
-0.05088945850729942,
0.01069182064384222,
0.0012881349539384246,
-0.0016535876784473658,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30739 | [
"Bug",
"Documentation",
"wontfix",
"Metadata Routing"
] | Edge case bug in metadata routing (n_samples == n_features)
### Describe the bug
Hello, while using metadata routing I encountered what seems to be a bug. I do not have enough understanding of metadata routing to determine if it is actually a bug or an incorrect use.
Below is an example where I am using a meta estim... | 30,739 | [
0.0294602420181036,
-0.010156530886888504,
0.024802902713418007,
0.0001878963375929743,
0.05420565605163574,
-0.03509746119379997,
-0.005516308359801769,
-0.023353612050414085,
-0.05348850041627884,
-0.021141400560736656,
0.04632272198796272,
0.08199570327997208,
0.03163447603583336,
0.005... |
https://github.com/scikit-learn/scikit-learn/issues/30739 | [
"Bug",
"Documentation",
"wontfix",
"Metadata Routing"
] | Edge case bug in metadata routing (n_samples == n_features)
### Describe the bug
Hello, while using metadata routing I encountered what seems to be a bug. I do not have enough understanding of metadata routing to determine if it is actually a bug or an incorrect use.
Below is an example where I am using a meta estim... | 30,739 | [
0.0294602420181036,
-0.010156530886888504,
0.024802902713418007,
0.0001878963375929743,
0.05420565605163574,
-0.03509746119379997,
-0.005516308359801769,
-0.023353612050414085,
-0.05348850041627884,
-0.021141400560736656,
0.04632272198796272,
0.08199570327997208,
0.03163447603583336,
0.005... |
https://github.com/scikit-learn/scikit-learn/issues/30739 | [
"Bug",
"Documentation",
"wontfix",
"Metadata Routing"
] | Edge case bug in metadata routing (n_samples == n_features)
### Describe the bug
Hello, while using metadata routing I encountered what seems to be a bug. I do not have enough understanding of metadata routing to determine if it is actually a bug or an incorrect use.
Below is an example where I am using a meta estim... | 30,739 | [
0.0294602420181036,
-0.010156530886888504,
0.024802902713418007,
0.0001878963375929743,
0.05420565605163574,
-0.03509746119379997,
-0.005516308359801769,
-0.023353612050414085,
-0.05348850041627884,
-0.021141400560736656,
0.04632272198796272,
0.08199570327997208,
0.03163447603583336,
0.005... |
https://github.com/scikit-learn/scikit-learn/issues/30732 | [
"New Feature",
"Needs Decision - Include Feature"
] | Add Weighted Euclidean Distance Metric
### Describe the workflow you want to enable
The workflow I want to enable is the ability for users to easily incorporate feature importance into distance-based algorithms like clustering (e.g., KMeans) and nearest neighbors (e.g., KNeighborsClassifier). Currently, scikit-learn ... | 30,732 | [
-0.009659195318818092,
0.05483009293675423,
-0.009160888381302357,
-0.04765478894114494,
0.009540611878037453,
0.030086621642112732,
0.06732185930013657,
0.021166149526834488,
0.06514540314674377,
-0.03212472051382065,
0.00507966335862875,
0.03717273846268654,
-0.036117371171712875,
0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/30732 | [
"New Feature",
"Needs Decision - Include Feature"
] | Add Weighted Euclidean Distance Metric
### Describe the workflow you want to enable
The workflow I want to enable is the ability for users to easily incorporate feature importance into distance-based algorithms like clustering (e.g., KMeans) and nearest neighbors (e.g., KNeighborsClassifier). Currently, scikit-learn ... | 30,732 | [
-0.009659195318818092,
0.05483009293675423,
-0.009160888381302357,
-0.04765478894114494,
0.009540611878037453,
0.030086621642112732,
0.06732185930013657,
0.021166149526834488,
0.06514540314674377,
-0.03212472051382065,
0.00507966335862875,
0.03717273846268654,
-0.036117371171712875,
0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/30732 | [
"New Feature",
"Needs Decision - Include Feature"
] | Add Weighted Euclidean Distance Metric
### Describe the workflow you want to enable
The workflow I want to enable is the ability for users to easily incorporate feature importance into distance-based algorithms like clustering (e.g., KMeans) and nearest neighbors (e.g., KNeighborsClassifier). Currently, scikit-learn ... | 30,732 | [
-0.009659195318818092,
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0.00507966335862875,
0.03717273846268654,
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0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/30732 | [
"New Feature",
"Needs Decision - Include Feature"
] | Add Weighted Euclidean Distance Metric
### Describe the workflow you want to enable
The workflow I want to enable is the ability for users to easily incorporate feature importance into distance-based algorithms like clustering (e.g., KMeans) and nearest neighbors (e.g., KNeighborsClassifier). Currently, scikit-learn ... | 30,732 | [
-0.009659195318818092,
0.05483009293675423,
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0.009540611878037453,
0.030086621642112732,
0.06732185930013657,
0.021166149526834488,
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-0.03212472051382065,
0.00507966335862875,
0.03717273846268654,
-0.036117371171712875,
0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/30732 | [
"New Feature",
"Needs Decision - Include Feature"
] | Add Weighted Euclidean Distance Metric
### Describe the workflow you want to enable
The workflow I want to enable is the ability for users to easily incorporate feature importance into distance-based algorithms like clustering (e.g., KMeans) and nearest neighbors (e.g., KNeighborsClassifier). Currently, scikit-learn ... | 30,732 | [
-0.009659195318818092,
0.05483009293675423,
-0.009160888381302357,
-0.04765478894114494,
0.009540611878037453,
0.030086621642112732,
0.06732185930013657,
0.021166149526834488,
0.06514540314674377,
-0.03212472051382065,
0.00507966335862875,
0.03717273846268654,
-0.036117371171712875,
0.0128... |
https://github.com/scikit-learn/scikit-learn/issues/30717 | [
"good first issue",
"module:model_selection"
] | MNT Make binary display method parameters' order consistent
This came up while working on #30399 . These are all classes inheriting the `_BinaryClassifierCurveDisplayMixin`.
* `RocCurveDisplay` and `PrecisionRecallDisplay` are pretty consistent, we would just need to change where `pos_label` is. No strong preference ... | 30,717 | [
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0.05962430313229561,
0.04754777252674103,
0.02263963... |
https://github.com/scikit-learn/scikit-learn/issues/30717 | [
"good first issue",
"module:model_selection"
] | MNT Make binary display method parameters' order consistent
This came up while working on #30399 . These are all classes inheriting the `_BinaryClassifierCurveDisplayMixin`.
* `RocCurveDisplay` and `PrecisionRecallDisplay` are pretty consistent, we would just need to change where `pos_label` is. No strong preference ... | 30,717 | [
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0.04390062764286995,
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0.01831626333296299,
0.05962430313229561,
0.04754777252674103,
0.02263963... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30714 | [
"Bug",
"Needs Triage"
] | Version 1.0.2 requires numpy<2
### Describe the bug
Installing scikit-learn version 1.0.2 leads to the following error:
```bash
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
This seems to indicate a mismatch between this version of scik... | 30,714 | [
0.009745048359036446,
0.021353306248784065,
-0.0032067105639725924,
-0.02028624154627323,
0.029233695939183235,
0.02522730827331543,
0.06040094420313835,
0.04487156495451927,
0.06789082288742065,
0.010141385719180107,
0.025750907137989998,
0.06649912148714066,
0.00007851546251913533,
0.042... |
https://github.com/scikit-learn/scikit-learn/issues/30713 | [
"Bug",
"Needs Investigation"
] | Error in `d2_log_loss_score` multiclass when one of the classes is missing in `y_true`.
### Describe the bug
Hello, I encountered an error with the `d2_log_loss_score` in the multiclass setting (i.e. when `y_pred` has shape (n, k) with k >= 3) when one of the classes is missing from the `y_true` labels, even when giv... | 30,713 | [
0.053308308124542236,
-0.02565406635403633,
0.024472713470458984,
0.015224418602883816,
0.11158312857151031,
0.002359161153435707,
0.04007694870233536,
0.03144444152712822,
0.0020795997697860003,
-0.034833990037441254,
0.05338428169488907,
-0.002259056083858013,
0.018705373629927635,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30713 | [
"Bug",
"Needs Investigation"
] | Error in `d2_log_loss_score` multiclass when one of the classes is missing in `y_true`.
### Describe the bug
Hello, I encountered an error with the `d2_log_loss_score` in the multiclass setting (i.e. when `y_pred` has shape (n, k) with k >= 3) when one of the classes is missing from the `y_true` labels, even when giv... | 30,713 | [
0.053308308124542236,
-0.02565406635403633,
0.024472713470458984,
0.015224418602883816,
0.11158312857151031,
0.002359161153435707,
0.04007694870233536,
0.03144444152712822,
0.0020795997697860003,
-0.034833990037441254,
0.05338428169488907,
-0.002259056083858013,
0.018705373629927635,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30713 | [
"Bug",
"Needs Investigation"
] | Error in `d2_log_loss_score` multiclass when one of the classes is missing in `y_true`.
### Describe the bug
Hello, I encountered an error with the `d2_log_loss_score` in the multiclass setting (i.e. when `y_pred` has shape (n, k) with k >= 3) when one of the classes is missing from the `y_true` labels, even when giv... | 30,713 | [
0.053308308124542236,
-0.02565406635403633,
0.024472713470458984,
0.015224418602883816,
0.11158312857151031,
0.002359161153435707,
0.04007694870233536,
0.03144444152712822,
0.0020795997697860003,
-0.034833990037441254,
0.05338428169488907,
-0.002259056083858013,
0.018705373629927635,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30713 | [
"Bug",
"Needs Investigation"
] | Error in `d2_log_loss_score` multiclass when one of the classes is missing in `y_true`.
### Describe the bug
Hello, I encountered an error with the `d2_log_loss_score` in the multiclass setting (i.e. when `y_pred` has shape (n, k) with k >= 3) when one of the classes is missing from the `y_true` labels, even when giv... | 30,713 | [
0.053308308124542236,
-0.02565406635403633,
0.024472713470458984,
0.015224418602883816,
0.11158312857151031,
0.002359161153435707,
0.04007694870233536,
0.03144444152712822,
0.0020795997697860003,
-0.034833990037441254,
0.05338428169488907,
-0.002259056083858013,
0.018705373629927635,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30707 | [
"New Feature",
"Moderate"
] | Add sample_weight support to QuantileTransformer
### Describe the workflow you want to enable
Would be good to get sample_weight support for QuantileTransformer for dealing with sparse or imbalanced data, a la [#15601](https://github.com/scikit-learn/scikit-learn/issues/15601).
```
scaler = QuantileTransformer(ou... | 30,707 | [
-0.030629782006144524,
0.062069568783044815,
0.02992810122668743,
-0.06573948264122009,
0.06050296127796173,
-0.029642289504408836,
0.021709667518734932,
0.06313236057758331,
0.0022388515062630177,
0.012764990329742432,
0.0002306116366526112,
0.04730480909347534,
-0.028463423252105713,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30707 | [
"New Feature",
"Moderate"
] | Add sample_weight support to QuantileTransformer
### Describe the workflow you want to enable
Would be good to get sample_weight support for QuantileTransformer for dealing with sparse or imbalanced data, a la [#15601](https://github.com/scikit-learn/scikit-learn/issues/15601).
```
scaler = QuantileTransformer(ou... | 30,707 | [
-0.035112541168928146,
0.06529965996742249,
0.03764358162879944,
-0.03042968176305294,
0.07543845474720001,
-0.027259986847639084,
0.018410159274935722,
0.08232458680868149,
0.010879380628466606,
0.01526555884629488,
-0.019992295652627945,
0.0636148527264595,
-0.03776414319872856,
0.020336... |
https://github.com/scikit-learn/scikit-learn/issues/30707 | [
"New Feature",
"Moderate"
] | Add sample_weight support to QuantileTransformer
### Describe the workflow you want to enable
Would be good to get sample_weight support for QuantileTransformer for dealing with sparse or imbalanced data, a la [#15601](https://github.com/scikit-learn/scikit-learn/issues/15601).
```
scaler = QuantileTransformer(ou... | 30,707 | [
-0.02840612269937992,
0.05429237335920334,
0.033297743648290634,
-0.06533192843198776,
0.058247487992048264,
-0.025257017463445663,
0.017303181812167168,
0.06320413202047348,
0.007100214250385761,
0.010345985181629658,
-0.007082527503371239,
0.04646071791648865,
-0.019366176798939705,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30707 | [
"New Feature",
"Moderate"
] | Add sample_weight support to QuantileTransformer
### Describe the workflow you want to enable
Would be good to get sample_weight support for QuantileTransformer for dealing with sparse or imbalanced data, a la [#15601](https://github.com/scikit-learn/scikit-learn/issues/15601).
```
scaler = QuantileTransformer(ou... | 30,707 | [
-0.03658225014805794,
0.06767985224723816,
0.02838483452796936,
-0.06717626750469208,
0.0580824539065361,
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0.01913544163107872,
0.06485114246606827,
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0.008771265856921673,
0.0000033984583751589525,
0.05017491802573204,
-0.02377104014158249,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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0.... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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0... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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0.06... |
https://github.com/scikit-learn/scikit-learn/issues/30702 | [
"Build / CI"
] | CI Use explicit permissions for GHA workflows
CodeQL scanning is nudging us towards using explicit permission, see https://github.com/scikit-learn/scikit-learn/security/code-scanning?query=is%3Aopen+branch%3Amain+rule%3Aactions%2Fmissing-workflow-permissions
Once this is done we could in principle set the default wor... | 30,702 | [
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https://github.com/scikit-learn/scikit-learn/issues/30699 | [
"Enhancement",
"module:datasets"
] | Make scikit-learn OpenML more generic for the data download URL
According to https://github.com/orgs/openml/discussions/20#discussioncomment-11913122 our code hardcodes where to find the OpenML data.
I am not quite sure what needs to be done right now but maybe @PGijsbers has some suggestions (not urgent at all thoug... | 30,699 | [
0.008372129872441292,
-0.018277835100889206,
0.01005144976079464,
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0.027963746339082718,
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0.02029445394873619,
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0.040729813277721405,
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0.1143694818019867,
0.00729066226631403,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30699 | [
"Enhancement",
"module:datasets"
] | Make scikit-learn OpenML more generic for the data download URL
According to https://github.com/orgs/openml/discussions/20#discussioncomment-11913122 our code hardcodes where to find the OpenML data.
I am not quite sure what needs to be done right now but maybe @PGijsbers has some suggestions (not urgent at all thoug... | 30,699 | [
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0.06546954065561295,
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0.0634... |
https://github.com/scikit-learn/scikit-learn/issues/30699 | [
"Enhancement",
"module:datasets"
] | Make scikit-learn OpenML more generic for the data download URL
According to https://github.com/orgs/openml/discussions/20#discussioncomment-11913122 our code hardcodes where to find the OpenML data.
I am not quite sure what needs to be done right now but maybe @PGijsbers has some suggestions (not urgent at all thoug... | 30,699 | [
0.01906587742269039,
-0.017296822741627693,
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... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
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-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30692 | [
"Documentation"
] | Inaccurate error message for parameter passing in Pipeline with enable_metadata_routing=True
### Describe the issue linked to the documentation
**The following error message is inaccurate:**
```
Passing extra keyword arguments to Pipeline.transform is only supported if enable_metadata_routing=True, which you can se... | 30,692 | [
0.0025466124061495066,
-0.0048340060748159885,
0.02658672258257866,
-0.0499202199280262,
0.07102779299020767,
-0.023613357916474342,
0.05547285079956055,
-0.00314479973167181,
-0.04413378983736038,
-0.009665410965681076,
0.03028615191578865,
0.022149641066789627,
0.018108373507857323,
0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.03725112974643707,
-0.0022014351561665535,
0.012753729708492756,
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0.02630775049328804,
0.10648049414157867,
0.015559084713459015,
0.020896274596452713,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.05207158252596855,
-0.01704457215964794,
-0.0022036780137568712,
-0.0009962789481505752,
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0.04780112951993942,
0.017238199710845947,
0.10460904985666275,
0.011841416358947754,
0.016552582383155823,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.05713773891329765,
-0.026955997571349144,
-0.000564601446967572,
-0.005045076832175255,
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0.052679285407066345,
0.003703121095895767,
0.047786712646484375,
0.020385047420859337,
0.10572101920843124,
0.002438956405967474,
0.027486270293593407,
... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.06793168932199478,
0.00513046607375145,
-0.0031791850924491882,
0.005703163333237171,
0.0002699648030102253,
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0.013946861028671265,
0.10517873615026474,
0.01656232215464115,
0.010944749228656292,
0.0728... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.05207158252596855,
-0.01704457215964794,
-0.0022036780137568712,
-0.0009962789481505752,
-0.008152509108185768,
-0.010639731772243977,
0.05551893264055252,
0.009700818918645382,
0.04780112951993942,
0.017238199710845947,
0.10460904985666275,
0.011841416358947754,
0.016552582383155823,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.04728329926729202,
0.01878780871629715,
-0.010998459532856941,
-0.004585307091474533,
0.0026520676910877228,
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0.03928527981042862,
0.012052965350449085,
0.04795320704579353,
0.00681885564699769,
0.10400425642728806,
0.0164326261729002,
0.0016599878435954452,
0.09022... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.022458931431174278,
0.028619863092899323,
-0.007689830847084522,
-0.027942713350057602,
-0.02610808238387108,
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0.08454415202140808,
-0.019404906779527664,
0.03162026405334473,
-0.01180961262434721,
0.09721487760543823,
0.015400934964418411,
0.005284509621560574,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.051712457090616226,
0.017084645107388496,
0.003961991984397173,
0.005643298849463463,
0.008010778576135635,
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0.03541797026991844,
0.02046758309006691,
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0.003594904439523816,
0.10637112706899643,
0.009973051026463509,
0.0024083000607788563,
0.0703... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.039476923644542694,
-0.013512419536709785,
0.00727038225159049,
-0.003863039892166853,
0.0134070860221982,
-0.002618917264044285,
0.05223371088504791,
0.013670777902007103,
0.05084589123725891,
0.018397396430373192,
0.11044565588235855,
0.03108261339366436,
0.013767621479928493,
0.07551... |
https://github.com/scikit-learn/scikit-learn/issues/30689 | [
"Enhancement",
"good first issue"
] | FeatureHasher and HashingVectorizer does not expose requires_fit=False tag
While `FeatureHasher` and `HashingVectorizer` are stateless estimator (at least in their docstrings), they do not expose the `requires_fit` tag to `False` as other stateless estimator.
@adrinjalali Do you recall when changing the tags if there... | 30,689 | [
-0.02903277985751629,
0.0012957847211509943,
-0.0007021021447144449,
-0.001130180899053812,
0.01817788928747177,
-0.014382447116076946,
0.0035412583965808153,
0.027306871488690376,
0.01012459397315979,
-0.009523102082312107,
0.11687911301851273,
0.021333560347557068,
0.020405590534210205,
... |
https://github.com/scikit-learn/scikit-learn/issues/30684 | [
"Bug"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jan 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73668&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jan 21, 2025)
- test_linear_regression_sample_weight... | 30,684 | [
-0.00930806528776884,
-0.00614591222256422,
-0.0004326733178459108,
-0.0008107510511763394,
0.0642530545592308,
0.011438604444265366,
0.03356308117508888,
0.05184435471892357,
0.033261820673942566,
0.01728382334113121,
0.03923940286040306,
0.07748796790838242,
0.005189853720366955,
0.04167... |
https://github.com/scikit-learn/scikit-learn/issues/30684 | [
"Bug"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jan 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73668&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jan 21, 2025)
- test_linear_regression_sample_weight... | 30,684 | [
-0.022203665226697922,
0.021715493872761726,
-0.02270951308310032,
-0.014758187346160412,
0.052286770194768906,
0.02637365460395813,
0.062337297946214676,
0.05824774131178856,
0.02818901464343071,
0.02362397313117981,
0.04456983879208565,
0.056086983531713486,
0.01691962219774723,
0.065466... |
https://github.com/scikit-learn/scikit-learn/issues/30684 | [
"Bug"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jan 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73668&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jan 21, 2025)
- test_linear_regression_sample_weight... | 30,684 | [
-0.029571138322353363,
0.030728871002793312,
-0.032651137560606,
-0.014358663000166416,
0.030576422810554504,
0.0038175180088728666,
0.024120675399899483,
0.054812025278806686,
0.022744925692677498,
0.041110508143901825,
0.060780059546232224,
0.05671080946922302,
0.019009655341506004,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/30684 | [
"Bug"
] | ⚠️ CI failed on Linux_Runs.pylatest_conda_forge_mkl (last failure: Jan 21, 2025) ⚠️
**CI failed on [Linux_Runs.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=73668&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a)** (Jan 21, 2025)
- test_linear_regression_sample_weight... | 30,684 | [
-0.020625021308660507,
0.04194674268364906,
-0.025802822783589363,
-0.010790075175464153,
0.05449213832616806,
0.024713218212127686,
0.05317610874772072,
0.045907262712717056,
0.0135223139077425,
0.04065217822790146,
0.05956757441163063,
0.04891771078109741,
0.008078531362116337,
0.0916869... |
https://github.com/scikit-learn/scikit-learn/issues/30675 | [
"Bug",
"Regression"
] | Possible bug in sklearn 1.6.1 PartialDependenceDisplay.from_estimator when target and feature are both binary
### Describe the bug
PartialDependenceDisplay.from_estimator does not seem able to handle dummy variables when the response variable is binary. See example below. The example works fine in 1.5.2 but returns `... | 30,675 | [
0.06383335590362549,
0.026187404990196228,
0.060427628457546234,
-0.02123667299747467,
0.07010147720575333,
-0.008475890383124352,
0.054174769669771194,
0.043537687510252,
0.031075619161128998,
-0.036802299320697784,
0.052285026758909225,
0.03667737543582916,
0.009984517470002174,
-0.00435... |
https://github.com/scikit-learn/scikit-learn/issues/30675 | [
"Bug",
"Regression"
] | Possible bug in sklearn 1.6.1 PartialDependenceDisplay.from_estimator when target and feature are both binary
### Describe the bug
PartialDependenceDisplay.from_estimator does not seem able to handle dummy variables when the response variable is binary. See example below. The example works fine in 1.5.2 but returns `... | 30,675 | [
0.06383335590362549,
0.026187404990196228,
0.060427628457546234,
-0.02123667299747467,
0.07010147720575333,
-0.008475890383124352,
0.054174769669771194,
0.043537687510252,
0.031075619161128998,
-0.036802299320697784,
0.052285026758909225,
0.03667737543582916,
0.009984517470002174,
-0.00435... |
https://github.com/scikit-learn/scikit-learn/issues/30675 | [
"Bug",
"Regression"
] | Possible bug in sklearn 1.6.1 PartialDependenceDisplay.from_estimator when target and feature are both binary
### Describe the bug
PartialDependenceDisplay.from_estimator does not seem able to handle dummy variables when the response variable is binary. See example below. The example works fine in 1.5.2 but returns `... | 30,675 | [
0.06383335590362549,
0.026187404990196228,
0.060427628457546234,
-0.02123667299747467,
0.07010147720575333,
-0.008475890383124352,
0.054174769669771194,
0.043537687510252,
0.031075619161128998,
-0.036802299320697784,
0.052285026758909225,
0.03667737543582916,
0.009984517470002174,
-0.00435... |
https://github.com/scikit-learn/scikit-learn/issues/30675 | [
"Bug",
"Regression"
] | Possible bug in sklearn 1.6.1 PartialDependenceDisplay.from_estimator when target and feature are both binary
### Describe the bug
PartialDependenceDisplay.from_estimator does not seem able to handle dummy variables when the response variable is binary. See example below. The example works fine in 1.5.2 but returns `... | 30,675 | [
0.06383335590362549,
0.026187404990196228,
0.060427628457546234,
-0.02123667299747467,
0.07010147720575333,
-0.008475890383124352,
0.054174769669771194,
0.043537687510252,
0.031075619161128998,
-0.036802299320697784,
0.052285026758909225,
0.03667737543582916,
0.009984517470002174,
-0.00435... |
https://github.com/scikit-learn/scikit-learn/issues/30675 | [
"Bug",
"Regression"
] | Possible bug in sklearn 1.6.1 PartialDependenceDisplay.from_estimator when target and feature are both binary
### Describe the bug
PartialDependenceDisplay.from_estimator does not seem able to handle dummy variables when the response variable is binary. See example below. The example works fine in 1.5.2 but returns `... | 30,675 | [
0.06383335590362549,
0.026187404990196228,
0.060427628457546234,
-0.02123667299747467,
0.07010147720575333,
-0.008475890383124352,
0.054174769669771194,
0.043537687510252,
0.031075619161128998,
-0.036802299320697784,
0.052285026758909225,
0.03667737543582916,
0.009984517470002174,
-0.00435... |
https://github.com/scikit-learn/scikit-learn/issues/30673 | [
"Needs Triage"
] | power_transform() lacks lambda retrieval in the new version
### Describe the issue
In the latest version of scikit-learn, the `power_transform()` function does not provide a way to access the lambda values (\(\lambda\)) used during the transformation. This was possible in the older version using the `PowerTransformer`... | 30,673 | [
0.0018867396283894777,
0.005860384088009596,
0.015579256229102612,
-0.022696202620863914,
0.060177769511938095,
0.004617543891072273,
0.06231856346130371,
-0.007974335923790932,
-0.036241766065359116,
-0.0010767284547910094,
0.01795562356710434,
0.03351101651787758,
0.011493765749037266,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30673 | [
"Needs Triage"
] | power_transform() lacks lambda retrieval in the new version
### Describe the issue
In the latest version of scikit-learn, the `power_transform()` function does not provide a way to access the lambda values (\(\lambda\)) used during the transformation. This was possible in the older version using the `PowerTransformer`... | 30,673 | [
0.0019421788165345788,
0.003316677175462246,
0.02304753102362156,
-0.024069104343652725,
0.06153403967618942,
-0.0028610152658075094,
0.06143901124596596,
-0.005367659032344818,
-0.021748213097453117,
0.004637704696506262,
0.01877516135573387,
0.03574905917048454,
0.01730548031628132,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30664 | [
"Enhancement",
"module:inspection"
] | UX `CalibrationDisplay`'s naive use can lead to very confusing results
The naive use of `CalibrationDisplay` parameter silently leads to degenerate, noisy results when some bins have with a few data points.
For instance, look at the variability obtained by displaying for calibration curve of a fitted model evaluated ... | 30,664 | [
-0.016939597204327583,
0.015478971414268017,
0.034018486738204956,
0.021546756848692894,
0.059626027941703796,
-0.012316667474806309,
0.04501768946647644,
0.010635776445269585,
-0.03508277237415314,
0.012721937149763107,
-0.007485085166990757,
0.009034686721861362,
0.042950764298439026,
-0... |
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