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/28959 | [
"Numerical Stability"
] | Local testing of global_random_seed is not enough
When adding ``global_random_seed`` to a test, it's not enough to check it locally, i.e. on a single machine. Numerical precision issues can come from various factors like OS, CPU, BLAS, ...
When adding ``global_random_seed``, it's important to test **all** random se... | 28,959 |
https://github.com/scikit-learn/scikit-learn/issues/28959 | [
"Numerical Stability"
] | Local testing of global_random_seed is not enough
When adding ``global_random_seed`` to a test, it's not enough to check it locally, i.e. on a single machine. Numerical precision issues can come from various factors like OS, CPU, BLAS, ...
When adding ``global_random_seed``, it's important to test **all** random se... | 28,959 |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 |
https://github.com/scikit-learn/scikit-learn/issues/28953 | [
"Bug"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil (last failure: May 06, 2024) ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=66324&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (May 06, 2024)
- test_pca_solver_equivalence[81-float32-False-T... | 28,953 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28952 | [
"New Feature",
"Moderate"
] | Add missing values and categorical features when generating datasets
### Describe the workflow you want to enable
I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:
- missing data, sometime just to test the pipeline... | 28,952 |
https://github.com/scikit-learn/scikit-learn/issues/28947 | [
"Performance"
] | Unable to allocate 24.0 GiB for an array ... But I have 64 GiB of memory
### Describe the bug
I have enough memory in my system, but I can fit my model
### Steps/Code to Reproduce
```
# X has 373 columns and 1.1 million rows
# Y has just 1 column and 1.1 million rows
def train(X,Y):
from sklearn.model... | 28,947 |
https://github.com/scikit-learn/scikit-learn/issues/28947 | [
"Performance"
] | Unable to allocate 24.0 GiB for an array ... But I have 64 GiB of memory
### Describe the bug
I have enough memory in my system, but I can fit my model
### Steps/Code to Reproduce
```
# X has 373 columns and 1.1 million rows
# Y has just 1 column and 1.1 million rows
def train(X,Y):
from sklearn.model... | 28,947 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28946 | [
"Bug"
] | Yeo-Johnson inverse_transform fails silently on extreme skew data
### Describe the bug
The Yeo-Johnson is not a surjective transformation for negative lambdas. Therefore, the inverse transformation returns `np.nan` when inverse transforming values outside the range of the transform. This failure is silent, so it to... | 28,946 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28944 | [
"New Feature",
"Documentation"
] | DOC add an example on how to optimize a metric with a constraint in TunedThresholdClassifierCV
We merged `TunedThresholdClassifierCV` in #26120.
However, we don't expose any way to optimize a metric that is constrained by another as one would do when choosing a point on the ROC or PR curves.
We should have an exam... | 28,944 |
https://github.com/scikit-learn/scikit-learn/issues/28943 | [
"Bug",
"Needs Triage"
] | MAPE approaching infinity with RandomForestRegressor
### Describe the bug
When using the current version of scikit-learn for learning a Random Forest Regressor (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn-ensemble-randomforestregressor) on the same dataset o... | 28,943 |
https://github.com/scikit-learn/scikit-learn/issues/28943 | [
"Bug",
"Needs Triage"
] | MAPE approaching infinity with RandomForestRegressor
### Describe the bug
When using the current version of scikit-learn for learning a Random Forest Regressor (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn-ensemble-randomforestregressor) on the same dataset o... | 28,943 |
https://github.com/scikit-learn/scikit-learn/issues/28939 | [
"Documentation",
"Needs Triage"
] | Rolling your own estimator
### Describe the issue linked to the documentation
The details on the Scikit-learn documentation page are at odds with the linked template.
According to the documentation, it suggests:
```class TemplateClassifier(BaseEstimator, ClassifierMixin)```
https://scikit-learn.org/stable... | 28,939 |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 |
https://github.com/scikit-learn/scikit-learn/issues/28937 | [
"New Feature"
] | Allow for multiple scoring metrics in `RFECV`
### Workflow
In its current state, `RFECV` only allows for a single scoring metric. In my opinion, calculating multiple scores on each model using *k <= K* features would be extremely valuable.
For example, if I wanted to study how the precision and recall metrics of... | 28,937 |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 |
https://github.com/scikit-learn/scikit-learn/issues/28935 | [
"Bug",
"Needs Triage"
] | VotingClassifier Doesn't work when use CatboostClassifier among estimators
### Describe the bug
VotingClassifier Doesn't work when using CatboostClassifier among estimators
### Steps/Code to Reproduce
here is my test case
```python
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble impor... | 28,935 |
https://github.com/scikit-learn/scikit-learn/issues/28933 | [
"Documentation"
] | DOC D2_log_loss_score is in wrong section
``D2_log_loss_score`` was added in https://github.com/scikit-learn/scikit-learn/pull/28351, but the function is documented in regression metrics with other D2 scores, while this one is a classification metric.
Ping @OmarManzoor for a follow-up PR maybe ?
COMMENT:
Sure than... | 28,933 |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 |
https://github.com/scikit-learn/scikit-learn/issues/28931 | [
"Bug",
"Pandas compatibility"
] | BUG internal indexing tools trigger error with pandas < 2.0.0
[#28375](https://github.com/scikit-learn/scikit-learn/pull/28375#issuecomment-2088926826) triggers errors for pandas < 2.0.0, despite just using scikit-learn internal functionalities.
As documented in https://scikit-learn.org/dev/install.html, we have pa... | 28,931 |
https://github.com/scikit-learn/scikit-learn/issues/28930 | [
"Documentation",
"Moderate",
"help wanted",
"Pandas compatibility"
] | Update FAQ about pandas
Our FAQ is not up to date when it comes to pandas,
> [Why does scikit-learn not directly work with, for example, ](https://scikit-learn.org/1.4/faq.html#id13)[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)?
>
>The homogene... | 28,930 |
https://github.com/scikit-learn/scikit-learn/issues/28930 | [
"Documentation",
"Moderate",
"help wanted",
"Pandas compatibility"
] | Update FAQ about pandas
Our FAQ is not up to date when it comes to pandas,
> [Why does scikit-learn not directly work with, for example, ](https://scikit-learn.org/1.4/faq.html#id13)[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)?
>
>The homogene... | 28,930 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28928 | [
"Enhancement"
] | Allow to use prefitted SelectFromModel in ColumnTransformer
```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectFromModel
iris = load_iris()
X = pd.Dat... | 28,928 |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 |
https://github.com/scikit-learn/scikit-learn/issues/28926 | [
"Bug"
] | Performance Degradation in MeanShift When Data Has No Variance
### Describe the bug
When data provided to `MeanShift` consists of values with no variance (for example, two clusters of 0 and 1), the performance becomes extremely slow.
I am unsure whether this is a bug or an unavoidable aspect of the algorithm's d... | 28,926 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28921 | [
"Documentation",
"Moderate",
"help wanted",
"module:tree"
] | Undocumented change in tree_.value example for DecisionTreeClassifier between versions 1.3.2 and 1.4.2
### Describe the issue linked to the documentation
In the the 1.4.2 docs the [Understanding the decision tree structure page](https://scikit-learn.org/1.3/auto_examples/tree/plot_unveil_tree_structure.html#understan... | 28,921 |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 |
https://github.com/scikit-learn/scikit-learn/issues/28920 | [
"Needs Reproducible Code",
"Needs Investigation"
] | Random Forest predict() does not produce reproducible results. random_state=42
### Describe the bug
If I load my pre trained model and set of samples and call predict() multiple times I get different predicted classes. Here are some sample results. I am using a juypter notebook. I have tried restarting the kernal ... | 28,920 |
https://github.com/scikit-learn/scikit-learn/issues/28913 | [
"New Feature",
"Needs Triage"
] | mypy errors when depending on sklearn
### Describe the workflow you want to enable
less errors when analyzing python code relying on sklearn using mypy
### Describe your proposed solution
Better code?
Typing annotations in the right places?
### Describe alternatives you've considered, if relevant
N/A
### Addi... | 28,913 |
https://github.com/scikit-learn/scikit-learn/issues/28911 | [
"Documentation"
] | DOC Add Tidelift to sponsor list
### Describe the issue linked to the documentation
Add Tidelift to sponsor list https://scikit-learn.org/stable/about.html#funding
### Suggest a potential alternative/fix
_No response_
COMMENT:
Indeed.
@adrinjalali @thomasjpfan any suggestion on the phrasing?
Shall we link to... | 28,911 |
https://github.com/scikit-learn/scikit-learn/issues/28911 | [
"Documentation"
] | DOC Add Tidelift to sponsor list
### Describe the issue linked to the documentation
Add Tidelift to sponsor list https://scikit-learn.org/stable/about.html#funding
### Suggest a potential alternative/fix
_No response_
COMMENT:
I don't mind adding Tidelift. And yes it seems from February the money is halved! I don'... | 28,911 |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 |
https://github.com/scikit-learn/scikit-learn/issues/28910 | [
"API",
"RFC",
"Developer API"
] | RFC Move `_more_tags` to "developer API" via `__sklearn_tags__`
As a part of making it easier and more "standard" to write scikit-learn estimators by third party developers, we have been slowly developing a "developer API" kind of thing, which are useful for third party developers, but not end users of the estimators.... | 28,910 |
https://github.com/scikit-learn/scikit-learn/issues/28903 | [
"Documentation"
] | Parameter Validation Documentation?
While implementing a custom estimator, I noticed that the BaseEstimator class brings in a `_validate_params` method. Looking through this repo's history, it looks like it came in back during 2022 as part of PR https://github.com/scikit-learn/scikit-learn/pull/22722
```python
... | 28,903 |
https://github.com/scikit-learn/scikit-learn/issues/28903 | [
"Documentation"
] | Parameter Validation Documentation?
While implementing a custom estimator, I noticed that the BaseEstimator class brings in a `_validate_params` method. Looking through this repo's history, it looks like it came in back during 2022 as part of PR https://github.com/scikit-learn/scikit-learn/pull/22722
```python
... | 28,903 |
https://github.com/scikit-learn/scikit-learn/issues/28899 | [
"Bug"
] | Validation step fails when using shared memory with `multiprocessing.managers.BaseManager`
### Describe the bug
Original issue: https://github.com/kedro-org/kedro/issues/3674
Relates to https://github.com/scikit-learn/scikit-learn/issues/28781
We use multiprocessing managers to work with shared memory for pip... | 28,899 |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 |
https://github.com/scikit-learn/scikit-learn/issues/28898 | [
"Bug"
] | HistGradientBoostingClassifier raise error with monotonic constraints and categorical features
### Describe the bug
Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in ... | 28,898 |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 |
https://github.com/scikit-learn/scikit-learn/issues/28892 | [
"New Feature",
"API",
"Needs Decision",
"module:preprocessing"
] | Automatically handle missing values in OrdinalEncoder
### Describe the workflow you want to enable
Currently, NaN values in OrdinalEncoder are either passed through as NaN, or encoded into user-specified value.
It would be nice to have a third option: consider NaN values as another category and map them into `num_... | 28,892 |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 |
https://github.com/scikit-learn/scikit-learn/issues/28891 | [
"New Feature",
"API",
"Needs Decision"
] | Easily retrieve mapping from OrdinalEncoder
### Describe the workflow you want to enable
It would be nice to be able to easily retrieve mapping in the form of a dictionary
```
"category_a": 0,
"category_b": 1,
"category_infrequent": 2,
...
```
Currently .categories_ attribute only retrieves list of seen cate... | 28,891 |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 |
https://github.com/scikit-learn/scikit-learn/issues/28887 | [
"New Feature"
] | Add missing value support to ExtraTreesRegressor
### Describe the workflow you want to enable
It wasn't very clear to me from the version 1.4 release notes and I inferred that missing value support was added for all DecisionTreeRegressor based regressors. I've noticed though that the `ExtraTreesRegressor` does not su... | 28,887 |
https://github.com/scikit-learn/scikit-learn/issues/28884 | [
"Bug",
"Build / CI"
] | ⚠️ CI failed on Wheel builder (last failure: Apr 26, 2024) ⚠️
**CI is still failing on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/8842793782)** (Apr 26, 2024)
COMMENT:
`conda` command not found in the osx jobs | 28,884 |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 |
https://github.com/scikit-learn/scikit-learn/issues/28883 | [
"Performance"
] | Configure OpenBLAS to use scikit-learn's OpenMP threadpool
OpenBLAS v0.3.28 will have a new feature allowing OpenBLAS to use the threadpool chosen by the user, (see https://github.com/OpenMathLib/OpenBLAS/pull/4577).
This is very interesting because it would solve a performance issue happening when there's a quick ... | 28,883 |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 |
https://github.com/scikit-learn/scikit-learn/issues/28881 | [
"New Feature"
] | `TargetEncoder` should respect `sample_weights`
### Describe the workflow you want to enable
The current implementation of `TargetEncoder` seems to calculate (shrinked) averages of `y`. In cases with `sample_weights`, it would be more natural to work with (shrinked) weighted averages.
### Describe your proposed ... | 28,881 |
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