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/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23524 | [
"Documentation",
"help wanted"
] | Reference RBFSampler in SVM docs and docstring
### Describe the issue linked to the documentation
I'm sitting in a presentation where the speaker (an academic prof in applied sciences) mentions that RBF kernels with SVMs in scikit-learn was worked best for his problem, but were too slow, so he switched back to linear... | 23,524 |
https://github.com/scikit-learn/scikit-learn/issues/23522 | [
"module:linear_model",
"module:multioutput",
"Needs Investigation"
] | Bayesian Ridge Regression
### Describe the bug
Using the Bayesian Ridge Regression with MultiOutputRegression(n_jobs=-1) consumes all the memory on the computer and the process dies. (Tested with 128G Ram + 128G Swap)
### Steps/Code to Reproduce
model = MultiOutputRegressor(sklearn.linear_model.BayesianRidge(), n_j... | 23,522 |
https://github.com/scikit-learn/scikit-learn/issues/23522 | [
"module:linear_model",
"module:multioutput",
"Needs Investigation"
] | Bayesian Ridge Regression
### Describe the bug
Using the Bayesian Ridge Regression with MultiOutputRegression(n_jobs=-1) consumes all the memory on the computer and the process dies. (Tested with 128G Ram + 128G Swap)
### Steps/Code to Reproduce
model = MultiOutputRegressor(sklearn.linear_model.BayesianRidge(), n_j... | 23,522 |
https://github.com/scikit-learn/scikit-learn/issues/23518 | [
"module:tree",
"Needs Investigation"
] | Fix resize segfault
The following code segment should be adjusted:
```python
self.capacity = node_ndarray.shape[0]
if self._resize_c(self.capacity) != 0:
```
It should instead be just `if self._resize_c(node_ndarray.shape[0]) != 0:`. Allow me to explain why:
In the `resize_c` function, the initial check is `if... | 23,518 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23512 | [
"Documentation",
"module:feature_selection"
] | RFECV regression output of cv_results_ values always of length 11 independent of n_features_
I am trying to use RFECV on a regression task, with a dataset that has a high feature to samples ratio and contains a lot of noise. As expected, the calculated value of `n_features` is quite high.
However contrary to the do... | 23,512 |
https://github.com/scikit-learn/scikit-learn/issues/23510 | [
"module:manifold"
] | Compute the gradient norm of t-SNE only when checking convergence
### Describe the workflow you want to enable
Inside the [`_gradient_descent()` function](https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/manifold/_t_sne.py#L399) in `_t_sne.py`, a gradient norm is com... | 23,510 |
https://github.com/scikit-learn/scikit-learn/issues/23500 | [
"Documentation",
"module:feature_extraction"
] | TF*IDF yields different results than TfidfTransformer
### Describe the bug
I was trying out some TF-IDF for NLP with sklearn. To double check the results, I was expecting to get the same output tfidf_matrix == TF*IDF where TF is the output of the `CountVectorizer` and IDF is the`tfidf_transformer.idf_` I was suprise ... | 23,500 |
https://github.com/scikit-learn/scikit-learn/issues/23500 | [
"Documentation",
"module:feature_extraction"
] | TF*IDF yields different results than TfidfTransformer
### Describe the bug
I was trying out some TF-IDF for NLP with sklearn. To double check the results, I was expecting to get the same output tfidf_matrix == TF*IDF where TF is the output of the `CountVectorizer` and IDF is the`tfidf_transformer.idf_` I was suprise ... | 23,500 |
https://github.com/scikit-learn/scikit-learn/issues/23500 | [
"Documentation",
"module:feature_extraction"
] | TF*IDF yields different results than TfidfTransformer
### Describe the bug
I was trying out some TF-IDF for NLP with sklearn. To double check the results, I was expecting to get the same output tfidf_matrix == TF*IDF where TF is the output of the `CountVectorizer` and IDF is the`tfidf_transformer.idf_` I was suprise ... | 23,500 |
https://github.com/scikit-learn/scikit-learn/issues/23500 | [
"Documentation",
"module:feature_extraction"
] | TF*IDF yields different results than TfidfTransformer
### Describe the bug
I was trying out some TF-IDF for NLP with sklearn. To double check the results, I was expecting to get the same output tfidf_matrix == TF*IDF where TF is the output of the `CountVectorizer` and IDF is the`tfidf_transformer.idf_` I was suprise ... | 23,500 |
https://github.com/scikit-learn/scikit-learn/issues/23500 | [
"Documentation",
"module:feature_extraction"
] | TF*IDF yields different results than TfidfTransformer
### Describe the bug
I was trying out some TF-IDF for NLP with sklearn. To double check the results, I was expecting to get the same output tfidf_matrix == TF*IDF where TF is the output of the `CountVectorizer` and IDF is the`tfidf_transformer.idf_` I was suprise ... | 23,500 |
https://github.com/scikit-learn/scikit-learn/issues/23498 | [
"Bug",
"Needs Triage"
] | Wrong order for check_sample_weights_invariance
### Describe the bug
The intent of check_sample_weights_invariance here: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/estimator_checks.py#L1016
is as it says that for kind="zeros", will produce a y1 such that a y2 with weights will give be "... | 23,498 |
https://github.com/scikit-learn/scikit-learn/issues/23498 | [
"Bug",
"Needs Triage"
] | Wrong order for check_sample_weights_invariance
### Describe the bug
The intent of check_sample_weights_invariance here: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/estimator_checks.py#L1016
is as it says that for kind="zeros", will produce a y1 such that a y2 with weights will give be "... | 23,498 |
https://github.com/scikit-learn/scikit-learn/issues/23488 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2406678618)**
COMMENT:
```
llvm-openmp-11.1.0 | | 0%
llvm-openmp-11.1.0 | | 0%
CondaHTTPError: HTTP 403 FORBIDDEN for url <https://anaconda.org/conda-forge/llvm-... | 23,488 |
https://github.com/scikit-learn/scikit-learn/issues/23488 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2406678618)**
COMMENT:
This seems like this one happened quite regularly recently, this seems the exact same error as https://github.com/scikit-learn/scikit-learn/issues/23454 and https://github.c... | 23,488 |
https://github.com/scikit-learn/scikit-learn/issues/23488 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2406678618)**
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/2412563829) | 23,488 |
https://github.com/scikit-learn/scikit-learn/issues/23482 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2399851254)**
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/2403102218) | 23,482 |
https://github.com/scikit-learn/scikit-learn/issues/23478 | [
"Bug",
"Needs Triage"
] | ConfusionMatrixDisplay.from_estimator(normalize='true') returns
### Describe the bug
When using the parameter `normalize` the plotting function cannot handle the floats generated after the normalization.
### Steps/Code to Reproduce
```
from sklearn.metrics import ConfusionMatrixDisplay
# Plot Confussion ma... | 23,478 |
https://github.com/scikit-learn/scikit-learn/issues/23474 | [
"Bug",
"Question"
] | "No module named 'sklearn.preprocessing.data'"
### Describe the bug
**Note:** Fairly new to Anaconda, Scikit-learn etc.
When I try to load a h5 file from [this zip](https://dax-cdn.cdn.appdomain.cloud/dax-oil-reservoir-simulations/1.0.0/oil-reservoir-simulations.tar.gz), with the following code:
(Importing the... | 23,474 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23462 | [
"Easy",
"good first issue",
"Meta-issue",
"Validation"
] | Make all estimators use `_validate_params`
PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.
Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it'... | 23,462 |
https://github.com/scikit-learn/scikit-learn/issues/23458 | [
"Documentation",
"Needs Triage"
] | Enable pytest to work with >=7.0
### Describe the issue linked to the documentation
pytest doesn't work with newer version
### Suggest a potential alternative/fix
_No response_
COMMENT:
duplicate of https://github.com/scikit-learn/scikit-learn/issues/22396 | 23,458 |
https://github.com/scikit-learn/scikit-learn/issues/23454 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2382037171)**
COMMENT:
Likely a temporary glitch:
```
CondaHTTPError: HTTP 403 FORBIDDEN for url <https://anaconda.org/conda-forge/llvm-openmp/11.1.0/download/osx-64/llvm-openmp-11.1.0-hda6cdc1_... | 23,454 |
https://github.com/scikit-learn/scikit-learn/issues/23454 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2382037171)**
COMMENT:
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2388554080)** | 23,454 |
https://github.com/scikit-learn/scikit-learn/issues/23454 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI Failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2382037171)**
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/2394479493) | 23,454 |
https://github.com/scikit-learn/scikit-learn/issues/23439 | [
"module:impute"
] | Default constant value in SimpleImputer with heterogeneous data
Let's consider the following example:
```python
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
X = pd.DataFrame(
{
"numerics": [1, 2, 3, np.nan],
"strings": ["A", "B", "C", np.nan],
}
... | 23,439 |
https://github.com/scikit-learn/scikit-learn/issues/23439 | [
"module:impute"
] | Default constant value in SimpleImputer with heterogeneous data
Let's consider the following example:
```python
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
X = pd.DataFrame(
{
"numerics": [1, 2, 3, np.nan],
"strings": ["A", "B", "C", np.nan],
}
... | 23,439 |
https://github.com/scikit-learn/scikit-learn/issues/23436 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Include drop='last' to OneHotEncoder
### Describe the workflow you want to enable
When using `SimpleImputer` + `OneHotEncoder`, I am able to add a new constant category for NaN values like the example below:
```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder... | 23,436 |
https://github.com/scikit-learn/scikit-learn/issues/23436 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Include drop='last' to OneHotEncoder
### Describe the workflow you want to enable
When using `SimpleImputer` + `OneHotEncoder`, I am able to add a new constant category for NaN values like the example below:
```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder... | 23,436 |
https://github.com/scikit-learn/scikit-learn/issues/23436 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Include drop='last' to OneHotEncoder
### Describe the workflow you want to enable
When using `SimpleImputer` + `OneHotEncoder`, I am able to add a new constant category for NaN values like the example below:
```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder... | 23,436 |
https://github.com/scikit-learn/scikit-learn/issues/23436 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Include drop='last' to OneHotEncoder
### Describe the workflow you want to enable
When using `SimpleImputer` + `OneHotEncoder`, I am able to add a new constant category for NaN values like the example below:
```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder... | 23,436 |
https://github.com/scikit-learn/scikit-learn/issues/23436 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | Include drop='last' to OneHotEncoder
### Describe the workflow you want to enable
When using `SimpleImputer` + `OneHotEncoder`, I am able to add a new constant category for NaN values like the example below:
```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder... | 23,436 |
https://github.com/scikit-learn/scikit-learn/issues/23430 | [
"Easy",
"Documentation",
"good first issue"
] | The location of `distance_metrics()` is wrong in this documentation snippet.
In `sklearn/metrics/pairwise.py` replacing
```
the __doc__ of the sklearn.pairwise.distance_metrics
```
by
```
:func:`sklearn.metrics.pairwise.pairwise_distances`
```
would be the best thing to do I think. This way it will be a hyp... | 23,430 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23427 | [
"Bug",
"module:neighbors",
"Needs Investigation"
] | Poor performance of KNeighborsClassifier for sklearn version >=0.24.2
### Describe the bug
Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of sklearn.
As shown in the following experiment results, this model performs best... | 23,427 |
https://github.com/scikit-learn/scikit-learn/issues/23424 | [
"Documentation",
"Needs Triage"
] | Change <= to \leq sign in tree module documentation
### Describe the issue linked to the documentation
I have found out that less or equal sign is written [here](https://scikit-learn.org/stable/modules/tree.html#mathematical-formulation) as "<=" instead of special LaTeX \leq symbol, which is inconsistent with other d... | 23,424 |
https://github.com/scikit-learn/scikit-learn/issues/23423 | [
"Bug",
"Build / CI"
] | setuptools >= 61 package discovery issue with python setup.py clean
Originally reported in https://github.com/scikit-learn/scikit-learn/discussions/23413
This seems to be a change in setuptools 61 about package discovery ... probably a related issue is https://github.com/pypa/setuptools/issues/3197
```
mamba cr... | 23,423 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
https://github.com/scikit-learn/scikit-learn/issues/23422 | [
"Bug",
"module:calibration"
] | Inconsistent numbers of samples issue with fit_params in CalibratedClassifierCV
### Describe the bug
Trying to use `fit_params` with `CalibratedClassifierCV` in v1.1 but receives fail of fit parameters when pass to classifier.
- I have 1000 rows.
- I split it into train and validation, 800 and 200 relatively.
... | 23,422 |
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