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/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25273 | [
"Regression"
] | __sklearn_pickle_version__ makes estimator.__dict__.keys() == loaded.__dict__.keys() to fail
Since https://github.com/scikit-learn/scikit-learn/pull/22094, this fails:
```py
est = <AnySklearnEstimator>
dict_before = est.__dict__.keys()
loaded = pickle.loads(pickle.dumps(est))
dict_after = loaded.__dict__.keys()... | 25,273 |
https://github.com/scikit-learn/scikit-learn/issues/25270 | [
"Needs Triage"
] | <!DOCTYPE HTML PUBLIC - 503 Server unavailable error - ALOPS Release Deployment failed
One of our customer's PROD environment release pipeline failed its first release.
Error:
##[error]Exception in BCConnector.GetAPIData: <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN""[http://www.w3.org/TR/html4/strict.dtd](h... | 25,270 |
https://github.com/scikit-learn/scikit-learn/issues/25270 | [
"Needs Triage"
] | <!DOCTYPE HTML PUBLIC - 503 Server unavailable error - ALOPS Release Deployment failed
One of our customer's PROD environment release pipeline failed its first release.
Error:
##[error]Exception in BCConnector.GetAPIData: <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN""[http://www.w3.org/TR/html4/strict.dtd](h... | 25,270 |
https://github.com/scikit-learn/scikit-learn/issues/25261 | [
"Bug"
] | 'DataFrame' object has no attribute 'dtype'
### Describe the bug
I believe my attached program is correct, but it errors out prematurely. with the error: DataFrame' object has no attribute 'dtype'.
I've spent many hours trying to debug this. I know the key trace line is
```pytb
packages/sklearn/preproces... | 25,261 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25258 | [
"Bug",
"module:metrics"
] | Matthews correlation coefficient gives wrong results for single-class result
### Describe the bug
for a single class input, (ie perfect correlation) MCC returns 0, when it should return 1.
since MCC is a special case of pearson correlation, it should fall back to pearson when appropriate
### Steps/Code to Reprodu... | 25,258 |
https://github.com/scikit-learn/scikit-learn/issues/25253 | [
"Bug",
"Needs Triage"
] | AttributeError: 'GridSearchCV' object has no attribute 'best_estimator_'
### Describe the bug
I'm trying to tune my model using the Grid search model in @kaggle notebook. In order to benefit from the GPU, I used this package hummingbird-ml. Thanks in advance
However, I get the following issue:
> AttributeError:... | 25,253 |
https://github.com/scikit-learn/scikit-learn/issues/25252 | [
"Documentation"
] | ledoit_wolf_shrinkage is not documented but still publicly available
### Describe the issue linked to the documentation
`ledoit_wolf_shrinkage` is not documented, but publicly available.
See discussion in #24870 (https://github.com/scikit-learn/scikit-learn/pull/24870#discussion_r1019598803)
### Suggest a pot... | 25,252 |
https://github.com/scikit-learn/scikit-learn/issues/25249 | [
"Bug",
"Regression"
] | Cannot increase verbosity of SGDRegressor fit
### Describe the bug
Increasing verbosity of SGDRegressor triggers an error from cython.
### Steps/Code to Reproduce
```
import numpy as np
import pandas as pd
from sklearn.linear_model import SGDRegressor
n_samples = 100
y = pd.DataFrame({"y": np.random.randin... | 25,249 |
https://github.com/scikit-learn/scikit-learn/issues/25249 | [
"Bug",
"Regression"
] | Cannot increase verbosity of SGDRegressor fit
### Describe the bug
Increasing verbosity of SGDRegressor triggers an error from cython.
### Steps/Code to Reproduce
```
import numpy as np
import pandas as pd
from sklearn.linear_model import SGDRegressor
n_samples = 100
y = pd.DataFrame({"y": np.random.randin... | 25,249 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25247 | [
"Bug",
"module:ensemble"
] | ValueError: buffer source array is read-only
### Describe the bug
Hi,
When training RandomForestClassifier using multiple cores (n_jobs=-1) I get the following error (full traceback below):
ValueError: buffer source array is read-only
This doesn't happen when using just one core or when a small dataset... | 25,247 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25239 | [
"Bug",
"module:compose"
] | ColumnTransformers don't honor set_config(transform_output="pandas") when multiprocessing with n_jobs>1
### Describe the bug
I'm trying to do a grid search with `n_jobs=-1`, working with pandas output, and it fails despite `set_config(transform_output = "pandas")`
I have to manually `.set_output(transform='panda... | 25,239 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25236 | [
"New Feature",
"module:feature_selection"
] | Allow for passing additional parameters to the estimator's `fit` method in `SequentialFeatureSelector`
### Describe the workflow you want to enable
I would like to be able to pass sample weights to the `fit` method of the estimator in `SequentialFeatureSelector`. (`SelectFromModel` has this feature as well.)
Loo... | 25,236 |
https://github.com/scikit-learn/scikit-learn/issues/25231 | [
"New Feature"
] | sample_weight parameter in KBinsDiscretizer with kmeans strategy
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/25208
<div type='discussions-op-text'>
<sup>Originally posted by **glevv** December 19, 2022</sup>
Would adding `sample_weight` support when `strategy='kmeans'` make sense?<... | 25,231 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25229 | [
"New Feature",
"Needs Decision",
"module:decomposition"
] | Handling NaNs in NMF
### Describe the workflow you want to enable
Motivation:
Sparse matrixes are very common in recommender systems problems. And matrix factorization approach is one of the most popular approaches for this task. But recommender systems often have sparse data with a big amount of missing values
Pr... | 25,229 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25227 | [
"Bug",
"Needs Triage"
] | Possible bug in the implementation of histogram in histogram based gradient boosting.
### Describe the bug
I was looking at the code in the file `sklearn/ensemble/_hist_gradient_boosting/histogram.pyx` and I believe there might be a bug. The note at the top of the file describes the following:
```
# Notes:
# -... | 25,227 |
https://github.com/scikit-learn/scikit-learn/issues/25216 | [
"Documentation",
"module:neural_network"
] | Mention that MLPRegressor can function as an autoencoder
### Describe the issue linked to the documentation
The MLPRegressor can function as an autoencoder by passing X as input and target (i.e. X == y).
```python
autoencoder = MLPRegressor(hidden_layer_sizes=(2,), activation="identity")
autoencoder.fit(X, X)
... | 25,216 |
https://github.com/scikit-learn/scikit-learn/issues/25216 | [
"Documentation",
"module:neural_network"
] | Mention that MLPRegressor can function as an autoencoder
### Describe the issue linked to the documentation
The MLPRegressor can function as an autoencoder by passing X as input and target (i.e. X == y).
```python
autoencoder = MLPRegressor(hidden_layer_sizes=(2,), activation="identity")
autoencoder.fit(X, X)
... | 25,216 |
https://github.com/scikit-learn/scikit-learn/issues/25216 | [
"Documentation",
"module:neural_network"
] | Mention that MLPRegressor can function as an autoencoder
### Describe the issue linked to the documentation
The MLPRegressor can function as an autoencoder by passing X as input and target (i.e. X == y).
```python
autoencoder = MLPRegressor(hidden_layer_sizes=(2,), activation="identity")
autoencoder.fit(X, X)
... | 25,216 |
https://github.com/scikit-learn/scikit-learn/issues/25215 | [
"Needs Triage"
] | You should have an issue with your install. Could you reinstall scipy and scikit-learn.
You should have an issue with your install. Could you reinstall scipy and scikit-learn.
You can try the following
```
conda install numpy scipy joblib scikit-learn --force-reinstall
```
_Originally posted by @glemaitre i... | 25,215 |
https://github.com/scikit-learn/scikit-learn/issues/25213 | [
"Bug",
"module:decomposition"
] | Segmentation fault occurs when KernelPCA is loaded after torchvision
### Describe the bug
If KernelPCA is loaded after torchvision, then I get a segmentation fault. If I load them in the opposite order then I get no segmentaton fault.
### Steps/Code to Reproduce
```
import torchvision
from sklearn.decomposition i... | 25,213 |
https://github.com/scikit-learn/scikit-learn/issues/25213 | [
"Bug",
"module:decomposition"
] | Segmentation fault occurs when KernelPCA is loaded after torchvision
### Describe the bug
If KernelPCA is loaded after torchvision, then I get a segmentation fault. If I load them in the opposite order then I get no segmentaton fault.
### Steps/Code to Reproduce
```
import torchvision
from sklearn.decomposition i... | 25,213 |
https://github.com/scikit-learn/scikit-learn/issues/25213 | [
"Bug",
"module:decomposition"
] | Segmentation fault occurs when KernelPCA is loaded after torchvision
### Describe the bug
If KernelPCA is loaded after torchvision, then I get a segmentation fault. If I load them in the opposite order then I get no segmentaton fault.
### Steps/Code to Reproduce
```
import torchvision
from sklearn.decomposition i... | 25,213 |
https://github.com/scikit-learn/scikit-learn/issues/25213 | [
"Bug",
"module:decomposition"
] | Segmentation fault occurs when KernelPCA is loaded after torchvision
### Describe the bug
If KernelPCA is loaded after torchvision, then I get a segmentation fault. If I load them in the opposite order then I get no segmentaton fault.
### Steps/Code to Reproduce
```
import torchvision
from sklearn.decomposition i... | 25,213 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
https://github.com/scikit-learn/scikit-learn/issues/25210 | [
"New Feature",
"module:ensemble"
] | ENH partial_dependece plot for HistGradientBoosting estimator fitted with `sample_weight`
### Describe the workflow you want to enable
As partial dependence of a model at a point [is defined as an expectation](https://scikit-learn.org/stable/modules/partial_dependence.html#mathematical-definition), it should respect ... | 25,210 |
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