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/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 |
https://github.com/scikit-learn/scikit-learn/issues/24102 | [
"Needs Triage"
] | Pb in example for spectral coclustering
I think there is a missing final transposition missing in the example demo for spectral coclustering here :
https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html
It should be :
```
fit_data = data[np.argsort(model.row_labels_)]
fit_... | 24,102 |
https://github.com/scikit-learn/scikit-learn/issues/24086 | [
"New Feature",
"module:feature_extraction",
"Needs Decision - Include Feature"
] | Add other connectivity definitions to grid_to_graph function
### Describe the workflow you want to enable
The current grid_to_graph function only defines voxel neighbors with the 6-connectivity definition. I would like to add 18 and 26 connectivity. (https://en.wikipedia.org/wiki/Pixel_connectivity#26-connected)
###... | 24,086 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24085 | [
"Bug",
"Moderate",
"module:mixture"
] | Weights are being normalized using number of samples as opposed to sum in GaussianMixture
### Describe the bug
Weights are being normalized at Line https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/mixture/_gaussian_mixture.py#L718 using `n_samples`. It should be done using `weights.sum()` as
done in `_... | 24,085 |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 |
https://github.com/scikit-learn/scikit-learn/issues/24082 | [
"Bug",
"module:preprocessing"
] | OrdinalEncoder fails inverse_transform with np.nan as
# Describe the bug
I want to use inverse_transform on the OrdinalEncoder from and to np.nan.
Steps/Code to Reproduce
```python
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
enc = OrdinalEncoder(handle_unknown="use_encoded_value", ... | 24,082 |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 |
https://github.com/scikit-learn/scikit-learn/issues/24080 | [
"Bug"
] | Deprecation warning with scipy=1.9.0
### Describe the bug
When running a fit with a Ride regression model we get:
> DeprecationWarning: The 'sym_pos' keyword is deprecated and should be replaced by using 'assume_a = "pos"'. 'sym_pos' will be removed in SciPy 1.11.0.
Looking through the SciPy release notes, sym_... | 24,080 |
https://github.com/scikit-learn/scikit-learn/issues/24078 | [
"Documentation",
"Needs Triage"
] | Binder link does not work
### Describe the issue linked to the documentation
I have tried the binder link given here: [link](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)
but it does not work.
### Suggest a... | 24,078 |
https://github.com/scikit-learn/scikit-learn/issues/24078 | [
"Documentation",
"Needs Triage"
] | Binder link does not work
### Describe the issue linked to the documentation
I have tried the binder link given here: [link](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)
but it does not work.
### Suggest a... | 24,078 |
https://github.com/scikit-learn/scikit-learn/issues/24064 | [
"Documentation",
"Needs Triage"
] | Permutation feature importance documentation page with misleading code
### Describe the issue linked to the documentation
In the documentation page of the feature importance algorithm (https://scikit-learn.org/stable/modules/permutation_importance.html) there are misleading code snippets.
When performing the fol... | 24,064 |
https://github.com/scikit-learn/scikit-learn/issues/24064 | [
"Documentation",
"Needs Triage"
] | Permutation feature importance documentation page with misleading code
### Describe the issue linked to the documentation
In the documentation page of the feature importance algorithm (https://scikit-learn.org/stable/modules/permutation_importance.html) there are misleading code snippets.
When performing the fol... | 24,064 |
https://github.com/scikit-learn/scikit-learn/issues/24063 | [
"New Feature",
"Needs Triage"
] | CI/Pre-commit Incompatibility between black and flake8
### Describe the workflow you want to enable
While `black` *tries* to make line lengths 88 characters maximum, there are cases where it will leave the code alone. On the other hand `flake8` is a strict check whether the line is longer than 88 characters, so in ca... | 24,063 |
https://github.com/scikit-learn/scikit-learn/issues/24063 | [
"New Feature",
"Needs Triage"
] | CI/Pre-commit Incompatibility between black and flake8
### Describe the workflow you want to enable
While `black` *tries* to make line lengths 88 characters maximum, there are cases where it will leave the code alone. On the other hand `flake8` is a strict check whether the line is longer than 88 characters, so in ca... | 24,063 |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 |
https://github.com/scikit-learn/scikit-learn/issues/24061 | [
"Bug",
"Needs Triage"
] | tuning xgboost classifier hyperparams, raises a exception
### Describe the bug
I am trying to classify patients into those who have diabetes and those who do not, with xgboost and scikit-learn library. The dataset is from [kaggle](https://www.kaggle.com/datasets/cjboat/diabetes2).
**To not depend on external data ... | 24,061 |
https://github.com/scikit-learn/scikit-learn/issues/24056 | [
"Needs Triage"
] | > The Conference Paper Suggested has helped us to do detail analysis for our Research Activity
> The Conference Paper Suggested has helped us to do detail analysis for our Research Activity
Thanks for Reply. Will Surely revert you back with new article..
_Originally posted by @sujataoak799 in https://github.com/... | 24,056 |
https://github.com/scikit-learn/scikit-learn/issues/24054 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/2768157160)** (Jul 31, 2022)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/2772243666) on Aug 01, 2022 | 24,054 |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 |
https://github.com/scikit-learn/scikit-learn/issues/24050 | [
"Documentation",
"API"
] | API: Simplification of `PairwiseDistanceReductions` backend class names.
## Problem
The current backend class names are a bit confusing:
<details>
<summary>Current Names</summary>
```
# High-level diagram
# ------------------
#
# Legend:
#
# A ---⊳ B: A inherits from B
# A ---x B: A dispa... | 24,050 |
https://github.com/scikit-learn/scikit-learn/issues/24045 | [
"Documentation",
"Needs Triage"
] | URL to PCA mle papers broken in docs
### Describe the issue linked to the documentation
Some urls to papers in the [PCA docs](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA) no longer work. Here
>For n_components == ‘mle’, this class uses the method from:... | 24,045 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24037 | [
"Bug",
"module:ensemble",
"module:tree"
] | RandomForestClassifier class_weight/max_samples interaction can lead to ungraceful and nondescriptive failure
### Describe the bug
The acceptable values for `max_samples` are `(0, 1]`. One possible option for `class_weight` is `balanced_subsample`. However, for values of `max_samples` near zero and `class_weight='b... | 24,037 |
https://github.com/scikit-learn/scikit-learn/issues/24025 | [
"Bug",
"module:linear_model",
"module:utils"
] | check_estimator cannot validate SGDClassifier with log_loss
### Describe the bug
It just tried to use **SGDClassifier** with **log_loss**, but it fail some test when I try to validated it with **check_estimator** function.
### Steps/Code to Reproduce
```python
from sklearn.linear_model import SGDClassifier
... | 24,025 |
https://github.com/scikit-learn/scikit-learn/issues/24025 | [
"Bug",
"module:linear_model",
"module:utils"
] | check_estimator cannot validate SGDClassifier with log_loss
### Describe the bug
It just tried to use **SGDClassifier** with **log_loss**, but it fail some test when I try to validated it with **check_estimator** function.
### Steps/Code to Reproduce
```python
from sklearn.linear_model import SGDClassifier
... | 24,025 |
https://github.com/scikit-learn/scikit-learn/issues/24024 | [
"Performance"
] | Optimize inference time for sklearn models in real time for single example
### Describe the bug
It is a multi-class classification model with sklearn.
I am using `OneVsOneClassifier` model to train and predict `150 intents`. Its a multi-class classification problem.
**Data:**
text intents
... | 24,024 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24013 | [
"Bug"
] | ndarray is not C-contiguous error, when using KNeighborsRegressor
### Describe the bug
I came across this error when building K-nearest neighbor model for the project that I am working on. I checked the flags of the numpy array that I was passing to the predict method of **KNeighborsRegressor**, and it showed that... | 24,013 |
https://github.com/scikit-learn/scikit-learn/issues/24009 | [
"Needs Triage"
] | `_sk_visual_block` can be executed before parameter validation
I found that `_sk_visual_block` can be executed before parameter validation. It can lead to a not really informative error message:
```python
from sklearn.ensemble import StackingClassifier
StackingClassifier(estimators=[])
```
```pytb
ValueError... | 24,009 |
https://github.com/scikit-learn/scikit-learn/issues/24009 | [
"Needs Triage"
] | `_sk_visual_block` can be executed before parameter validation
I found that `_sk_visual_block` can be executed before parameter validation. It can lead to a not really informative error message:
```python
from sklearn.ensemble import StackingClassifier
StackingClassifier(estimators=[])
```
```pytb
ValueError... | 24,009 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24008 | [
"New Feature",
"Needs Decision - Include Feature"
] | Generalized Scores for Multi-Class Imbalanced Classification
### Describe the workflow you want to enable
We wish to extend Matthew coefficient and F1 to multi- class problems.
### Describe your proposed solution
We already generated a pypi package
https://pypi.org/project/matthew-Coef-MultiClass/ which sup... | 24,008 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24006 | [
"Bug",
"Needs Triage"
] | KBinsDiscretizer crashes when strategy='kmeans'
### Describe the bug
Kernel is Jupyter notebook crashes when `strategy='kmeans'` is used with `KBinsDiscretizer`.
Versions:
3.8.12 (default, Oct 12 2021, 06:23:56)
[Clang 10.0.0 ]
Python 3.8.12
numpy: 1.19.2
sklearn: 1.0.1
### Steps/Code to Reproduce
... | 24,006 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
https://github.com/scikit-learn/scikit-learn/issues/24000 | [
"New Feature",
"module:ensemble",
"module:tree",
"cython",
"Needs Decision - Include Feature",
"Refactor"
] | [RFC] Modularize the tree class in both Python and Cython to enable easy extensions
### Describe the workflow you want to enable
As we are waiting for a reviewer to review #22754 , @thomasjpfan suggested we just move forward with our goals of creating a package of more exotic tree splits. E.g. https://arxiv.org/abs/1... | 24,000 |
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