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/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 |
https://github.com/scikit-learn/scikit-learn/issues/22759 | [
"API",
"RFC"
] | RFC introduce methods to get and set estimators' state
Right now `clone` uses `{get, set}_params` to replicate an unfit estimator. These methods are designed to return esimators' hyperparameters. At the moment, we have no way of getting the state of a fitted estimator in a non-pickle format.
Pickle files are by des... | 22,759 |
https://github.com/scikit-learn/scikit-learn/issues/22758 | [
"Bug",
"Needs Reproducible Code"
] | can't convert a list to lowercase list
### Describe the bug
```pytb
[sklearn/feature_extraction/text.py]n _preprocess(doc, accent_function, lower)
69 """
70 if lower:
---> 71 doc = doc.lower()
72 if accent_function is not None:
73 doc = accent_function(doc)
A... | 22,758 |
https://github.com/scikit-learn/scikit-learn/issues/22758 | [
"Bug",
"Needs Reproducible Code"
] | can't convert a list to lowercase list
### Describe the bug
```pytb
[sklearn/feature_extraction/text.py]n _preprocess(doc, accent_function, lower)
69 """
70 if lower:
---> 71 doc = doc.lower()
72 if accent_function is not None:
73 doc = accent_function(doc)
A... | 22,758 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22755 | [
"New Feature"
] | Symmetric Mean Absolute Percentage Error
### Describe the workflow you want to enable
Make Symmetric Mean Absolute Percentage Error available as an error metric.
### Describe your proposed solution
implement as a metric under _regresion.py
smape = np.abs(y_pred - y_true) / np.maximum((np.abs(y_true) + np.abs... | 22,755 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22753 | [
"module:tree",
"Refactor"
] | [MAINT] Modularize Tree code and Splitter utility functions
From #20819 , developers expressed issues with the current tree code.
Part of that is the modularity and as a result, maintainability/upgradability of such code. I propose the following super-short refactors to the `_tree.pyx/pxd` and `_splitter.pyx/pxd` f... | 22,753 |
https://github.com/scikit-learn/scikit-learn/issues/22750 | [
"Bug",
"module:cluster",
"Needs Triage"
] | Unable to compute AgglomerativeClustering with affinity 'precomputed' and linkage 'ward'
### Describe the bug
When trying to compute AgglomerativeClustering with affinity='precomputed', linkage='ward' I get the following error:
`ValueError: precomputed was provided as affinity. Ward can only work with euclidean di... | 22,750 |
https://github.com/scikit-learn/scikit-learn/issues/22750 | [
"Bug",
"module:cluster",
"Needs Triage"
] | Unable to compute AgglomerativeClustering with affinity 'precomputed' and linkage 'ward'
### Describe the bug
When trying to compute AgglomerativeClustering with affinity='precomputed', linkage='ward' I get the following error:
`ValueError: precomputed was provided as affinity. Ward can only work with euclidean di... | 22,750 |
https://github.com/scikit-learn/scikit-learn/issues/22746 | [
"Bug",
"Needs Triage"
] | PCA.fit_transform() failing
### Describe the bug
I have data in a numpy array of shape (2970, 291) that contains `NaN` and `inf` values. `np.nan_to_num()` was called on the array prior to `fit_transform()` within the function provided below but `ValueError: array must not contain infs or NaNs` was raised instead. T... | 22,746 |
https://github.com/scikit-learn/scikit-learn/issues/22746 | [
"Bug",
"Needs Triage"
] | PCA.fit_transform() failing
### Describe the bug
I have data in a numpy array of shape (2970, 291) that contains `NaN` and `inf` values. `np.nan_to_num()` was called on the array prior to `fit_transform()` within the function provided below but `ValueError: array must not contain infs or NaNs` was raised instead. T... | 22,746 |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 |
https://github.com/scikit-learn/scikit-learn/issues/22744 | [
"Bug"
] | random Segfaults on distance_transform_edt with Intel 12 Alder lake (E-Core enabled)
Hi everyone
I am currently training a image segmentation network with PyTorch evaluated with hausdorff distance loss. To calculate hausdorff loss, I am using distance_transform_edt from scipy.ndimage
associated with morpholopy.py ... | 22,744 |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 |
https://github.com/scikit-learn/scikit-learn/issues/22731 | [
"Bug"
] | KBinsDiscretizer calling get_feature_names_out only works for encode = "onehot"
### Describe the bug
When using `KBinsDiscretizer` with encode set to anything but "onehot", calling `get_feature_names_out` on a fitted instance raises an AttributeError as shown below. It looks like as if the `self._encode` attribute ... | 22,731 |
https://github.com/scikit-learn/scikit-learn/issues/22730 | [
"Needs Triage"
] | How to use Hierarchical Navigable Small Worlds (HNSW) and LSH for Data classification rather than just retrieving Nearest neighbours
I want to use Hierarchical Navigable Small Worlds (HNSW) and LSH for data classification. How can I modify their fit and train functions???
For example if you want to use them like ba... | 22,730 |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 |
https://github.com/scikit-learn/scikit-learn/issues/22716 | [
"Bug",
"module:model_selection",
"Needs Triage"
] | RandomizedSearchCV's training time too much longer than cross_validate function sum of training times
### Describe the bug
I am currently working on a project and I have to make a choice between 5 machine learning algorithm's.
But my dataset is very large and I have more than 70 columns.
So to test my program... | 22,716 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22709 | [
"New Feature",
"module:cluster",
"Needs Decision - Include Feature"
] | Create a similar class to KMeans that uses medians instead of means (KMedians)
### Describe the workflow you want to enable
I would like a new class: sklearn.cluster.KMedians (or an option to sklearn.cluster.KMeans) that allows the methods to use medians instead of means.
K-n clustering can greatly improve some ... | 22,709 |
https://github.com/scikit-learn/scikit-learn/issues/22708 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Implement Repeated Group CV
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/model_selection/_split.py#L505
I tried to implement repeated group cv using GroupKFold and _RepeatedSplits. But it did not work unless I included `shuffle=False, random_state=None` to `def ... | 22,708 |
https://github.com/scikit-learn/scikit-learn/issues/22708 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | Implement Repeated Group CV
https://github.com/scikit-learn/scikit-learn/blob/7e1e6d09bcc2eaeba98f7e737aac2ac782f0e5f1/sklearn/model_selection/_split.py#L505
I tried to implement repeated group cv using GroupKFold and _RepeatedSplits. But it did not work unless I included `shuffle=False, random_state=None` to `def ... | 22,708 |
https://github.com/scikit-learn/scikit-learn/issues/22699 | [
"Bug",
"Packaging"
] | Installing scipy-wheels-nightly using pip shows error
### Describe the bug
When installing torch from https://pypi.anaconda.org/scipy-wheels-nightly/simple the console show a warning.
### Steps/Code to Reproduce
```bash
~: pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple ... | 22,699 |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 |
https://github.com/scikit-learn/scikit-learn/issues/22692 | [
"Question",
"module:ensemble"
] | Unexpected output from Random Forest Classifer
### Describe the bug
I attempted to use Random Forest Classifier on a data with binarized labels. And I realized the predictions given out always had one class missing. I tried on my data and also tried on one of the scikit-learn datasets and the same observation was m... | 22,692 |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 |
https://github.com/scikit-learn/scikit-learn/issues/22691 | [
"Enhancement",
"module:utils"
] | Include entire range in `check_scalar` error message
Currently docstrings description for scalar ranges uses the interval syntax:
https://github.com/scikit-learn/scikit-learn/blob/42cc05c5ddac0e0c4392871a6825c53ac88ace36/sklearn/linear_model/_glm/glm.py#L462
While the error message uses a different notation:
... | 22,691 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22689 | [
"Bug",
"module:cluster"
] | kMeans stopped working with numpy 1.22.2
### Describe the bug
kMeans is not working anymore with numpy 1.22.2
Probably similiar to (https://github.com/scikit-learn/scikit-learn/issues/22683) but not sure if it is the same fix
### Steps/Code to Reproduce
```
allLocations = np.array([[1, 2], [1, 4], [1, 0... | 22,689 |
https://github.com/scikit-learn/scikit-learn/issues/22683 | [
"Bug",
"module:neighbors"
] | KNeighborsRegressor with a callable weights stopped working with numpy 1.22.2
### Describe the bug
When you use a callable for the weights param you get:
AttributeError: 'list' object has no attribute 'shape'
`neigh = KNeighborsRegressor(n_neighbors=5, algorithm='brute', metric=euclidean_distance, weights=weigh... | 22,683 |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 |
https://github.com/scikit-learn/scikit-learn/issues/22682 | [
"New Feature",
"module:test-suite",
"float32"
] | Estimator check for dtype preservation for regressors
### Describe the workflow you want to enable
As discussed in https://github.com/scikit-learn/scikit-learn/pull/22663#issuecomment-1058368882, we should have a common test that checks that the `predict` method of regressors preserves the dtype, similarly to `chec... | 22,682 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22680 | [
"Build / CI",
"module:test-suite",
"workflow",
"float32"
] | TST Add option to run tests on 32bit data
### Context
Currently most implementations are tested against 64bit datasets only. The re-factoring of some internals for computations on 32bit datasets brought the need to test user-facing interfaces on 32bit datasets (see https://github.com/scikit-learn/scikit-learn/pull/... | 22,680 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
https://github.com/scikit-learn/scikit-learn/issues/22678 | [
"New Feature",
"module:model_selection"
] | GridSearchCV does not return trained estimator for each split vs cross_validate which does
### Describe the workflow you want to enable
GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which ... | 22,678 |
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