html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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