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- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train]
- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True)]
- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True,X_dtype=float32)] | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33469 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33469/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33469/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33469/events | https://github.com/scikit-learn/scikit-learn/pull/33469 | 4,031,718,646 | PR_kwDOAAzd1s7IX8dM | 33,469 | [Callbacks] ENH Add fit-level callback support to Pipeline | {
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Towards #27676
#### What does this implement/fix? Explain your changes.
- add callback support to `Pipeline.fit` by emitting one callback task per pipeline step
- keep the `Pipeline` step callbacks observational only: callback stop values are ignored
- support step callbacks for regular, `None` / `"passthrough"`, and cached steps
- add tests covering step payloads, passthrough steps, and `memory=` cache hits
#### Any other comments?
This is an intentionally narrow first `Pipeline` integration. It covers fit-level step callbacks only and does not propagate callbacks into sub-estimators.
This pull request includes code written with the assistance of AI.
The code has been reviewed by a human. | {
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Towards #27676
#### What does this implement/fix? Explain your changes.
- add callback support to `KMeans` by emitting nested `init` and `iter` tasks during `fit`
- honor callback stop signals at both the iteration and initialization levels in `KMeans`
- add callback support to `MiniBatchKMeans` by emitting one callback task per minibatch step during `fit`
- honor callback stop signals to terminate `MiniBatchKMeans.fit` early
- emit a callback task from `MiniBatchKMeans.partial_fit` for consistency with `fit`
- add tests covering nested hook calls and callback-driven stopping
#### Any other comments?
This is intended as a first production estimator integration on top of the `callbacks` branch.
This pull request includes code written with the assistance of AI.
The code has been reviewed by a human. | {
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None
#### What does this implement/fix? Explain your changes.
#### AI usage disclosure
None
#### Any other comments?
| null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33466 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33466/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33466/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33466/events | https://github.com/scikit-learn/scikit-learn/pull/33466 | 4,028,211,277 | PR_kwDOAAzd1s7IMxJ6 | 33,466 | DOC better marketing in user guid for Array API and set_output pandas/polars | {
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None
#### What does this implement/fix? Explain your changes.
The current user guide makes it hard to find "array API" or "pandas/polars output transform" unless one searches for it.
#### AI usage disclosure
None
#### Any other comments? | null | {
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**Converted tests:**
- `test_prediction_bayesian_ridge_ard_with_constant_input`: `check_random_state(42)` → `check_random_state(global_random_seed)`
- `test_std_bayesian_ridge_ard_with_constant_input`: `check_random_state(42)` → `check_random_state(global_random_seed)`
- `test_return_std`: bare `np.random.random()`/`np.random.randn()` → `rng = np.random.RandomState(global_random_seed)`
**Verification:**
- All 400 runs (4 test variants × 100 seeds) pass with `SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"`
Ref #22827 | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33459 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33459/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33459/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33459/events | https://github.com/scikit-learn/scikit-learn/pull/33459 | 4,025,042,837 | PR_kwDOAAzd1s7ICOSU | 33,459 | FIX account for sample_weight in SAGA step size computation | {
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] | closed | false | [] | null | 1 | 2026-03-05T01:05:04 | 2026-03-05T14:25:45 | 2026-03-05T08:44:49 | null | NONE | null | null | null | null | The step size for SAGA was computed using max(||x_i||^2) without accounting for sample weights. When sample_weight[i] > 1, the effective Lipschitz constant of sample i is w_i * L_i, so the global L must use max(w_i * ||x_i||^2) as an upper bound.
Add a regression test comparing SAGA with non-uniform sample_weight against lbfgs as a reference solver.
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] | open | false | [] | null | 0 | 2026-03-04T20:50:29 | 2026-03-05T08:00:44 | null | null | MEMBER | null | null | {
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} | The goal of this issue is to track the support of `estimator.fit(X, y, sample_weight=..)` throughout the library. As of scikit-learn version 1.8:
- [ ] `sklearn.cluster.AffinityPropagation`
- [ ] `sklearn.cluster.AgglomerativeClustering`
- [ ] `sklearn.cluster.Birch`
- [ ] `sklearn.cluster.FeatureAgglomeration`
- [ ] `sklearn.cluster.HDBSCAN`
- [ ] `sklearn.cluster.MeanShift`
- [ ] `sklearn.cluster.OPTICS`
- [ ] `sklearn.cluster.SpectralClustering`
- [ ] `sklearn.compose.TransformedTargetRegressor`
- [ ] `sklearn.cross_decomposition.CCA`
- [ ] `sklearn.cross_decomposition.PLSCanonical`
- [ ] `sklearn.cross_decomposition.PLSRegression`
- [ ] `sklearn.discriminant_analysis.LinearDiscriminantAnalysis`
- [ ] `sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`
- [x] `sklearn.ensemble.HistGradientBoostingClassifier` and `HistGradientBoostingRegressor` binning (added manually)
#29641
- [ ] `sklearn.ensemble.StackingClassifier`
- [ ] `sklearn.ensemble.StackingRegressor`
- [ ] `sklearn.ensemble.VotingClassifier`
- [ ] `sklearn.ensemble.VotingRegressor`
- [ ] `sklearn.gaussian_process.GaussianProcessClassifier`
- [ ] `sklearn.gaussian_process.GaussianProcessRegressor`
- [ ] `sklearn.linear_model.ARDRegression`
- [ ] `sklearn.linear_model.Lars`
- [ ] `sklearn.linear_model.LarsCV`
- [ ] `sklearn.linear_model.LassoLars`
- [ ] `sklearn.linear_model.LassoLarsCV`
- [ ] `sklearn.linear_model.LassoLarsIC`
- [ ] `sklearn.linear_model.MultiTaskElasticNet`
#33440
- [ ] `sklearn.linear_model.MultiTaskElasticNetCV`
#33440
- [ ] `sklearn.linear_model.MultiTaskLasso`
#33440
- [ ] `sklearn.linear_model.MultiTaskLassoCV`
#33440
- [ ] `sklearn.linear_model.OrthogonalMatchingPursuit`
- [ ] `sklearn.linear_model.OrthogonalMatchingPursuitCV`
- [ ] `sklearn.linear_model.PassiveAggressiveClassifier`
deprecated
- [ ] `sklearn.linear_model.PassiveAggressiveRegressor`
deprecated
- [ ] `sklearn.linear_model.TheilSenRegressor`
- [ ] `sklearn.model_selection.FixedThresholdClassifier`
- [ ] `sklearn.model_selection.TunedThresholdClassifierCV`
- [ ] `sklearn.multiclass.OneVsOneClassifier`
- [ ] `sklearn.multiclass.OneVsRestClassifier`
- [ ] `sklearn.multioutput.ClassifierChain`
- [ ] `sklearn.multiclass.OutputCodeClassifier`
- [ ] `sklearn.multioutput.RegressorChain`
- [ ] `sklearn.neighbors.KNeighborsClassifier`
- [ ] `sklearn.neighbors.KNeighborsRegressor`
- [ ] `sklearn.neighbors.NearestCentroid`
- [ ] `sklearn.neighbors.RadiusNeighborsClassifier`
- [ ] `sklearn.neighbors.RadiusNeighborsRegressor`
- [ ] `sklearn.semi_supervised.LabelPropagation`
- [ ] `sklearn.semi_supervised.LabelSpreading`
- [ ] `sklearn.semi_supervised.SelfTrainingClassifier`
Code to generate that list
```py
from inspect import signature
import sklearn
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instances
[est for name, est in all_estimators(type_filter=["classifier", "regressor", "cluster"]) if "sample_weig
⋮ ht" not in signature(next(_construct_instances(est)).fit).parameters]
``` | null | {
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"node_id":... | closed | false | [] | null | 2 | 2026-03-04T16:20:45 | 2026-03-05T05:06:48 | 2026-03-05T05:06:48 | null | NONE | null | null | null | null | ### **Reference Issues/PRs**
Addresses a common need for fixed-window cross-validation (rolling origin) in time series, distinct from the expanding window strategy of `TimeSeriesSplit`.
### **What does this implement/fix? Explain your changes.**
This PR introduces a new cross-validator: `RollingTimeSeriesSplit`.
### **Motivation:**
The current `TimeSeriesSplit` implements an expanding window strategy (the training set grows over time). However, in many time-series applications (finance, physics, demand forecasting), it is standard to use a **fixed sliding window** (Walk-Forward Validation) to detect concept drift and validate model performance on strictly recent history.
While `TimeSeriesSplit` accepts a `max_train_size`, forcing it to behave like a strict rolling window with a specific step size is currently unintuitive and requires complex calculations of n_splits based on the dataset length.
### **Proposed Solution:**
`RollingTimeSeriesSplit` allows defining:
- `train_size` (fixed)
- `test_size` (fixed)
- `step_size` (optional, defaults to train_size for disjoint windows)
- `gap` (optional, for embargo/purge)
### **Key Feature - Right Alignment:**
Unlike standard implementations that might drop recent data if the division isn't perfect, this implementation is **right-aligned**. It anchors the splits to the end of the dataset (most recent data). Any historical "remainder" (indices at the start that don't fit a full fold) is added to the first training set. This ensures the model is always validated on the latest available data.
### **AI usage disclosure**
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
### **Any other comments?**
I have added unit tests in `sklearn/model_selection/tests/test_split.py` covering:
- Disjoint windows (`step_size` = `train_size`)
- Overlapping windows (`step_size` < `train_size`)
- The `gap` parameter
- The right-alignment logic (remainder handling)
### **Example usage:**
```
import numpy as np
from sklearn.model_selection import RollingTimeSeriesSplit
X = np.arange(14)
# Scenario: Train on 4 samples, Test on 1 sample.
# Default step=4 (disjoint).
# Remainder (1 sample) goes to the first train set.
cv = RollingTimeSeriesSplit(train_size=4, test_size=1)
for i, (train, test) in enumerate(cv.split(X)):
print(f"Split {i}: Train={train}, Test={test}")
# Output:
# Split 0: Train=[0 1 2 3 4], Test=[5] <-- Absorbs index 0 (remainder)
# Split 1: Train=[5 6 7 8], Test=[9]
# Split 2: Train=[9 10 11 12], Test=[13] <-- Perfectly aligned with end
```
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"node_id": "MDU6TGFiZWwyOT... | closed | false | [] | null | 0 | 2026-03-04T15:15:57 | 2026-03-05T08:20:37 | 2026-03-05T08:20:37 | null | NONE | null | null | null | null | closes #33219
the `traverse_upwards` function in `_tree.pyx` does boolean-indexed lookups on the condensed tree structured array:
```python
parent = cluster_tree[cluster_tree['child'] == leaf]['parent']
parent_eps = 1 / cluster_tree[cluster_tree['child'] == parent]['value']
```
these always return shape-(1,) arrays, not scalars. numpy < 2.0 silently coerced shape-(1,) to scalar during cython typed assignment, but numpy 2.0 made this strict and raises `TypeError: only 0-dimensional arrays can be converted to Python scalars`.
the fix adds `[0]` to both lookups to explicitly extract the scalar. this is always safe since each child node has exactly one parent in the condensed tree.
added a non-regression test using the exact distance matrix from the issue.
## AI usage disclosure
I used AI assistance for:
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- [x] Test/benchmark generation
- [ ] Documentation (including examples)
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} | ### Describe the bug and give evidence about its user-facing impact
I noticed new behaviour in `sklearn.decomposition.DictioanryLearning`. I have noticed an error that I have not seen before. Learner `transform_n_nonzero_coefs` > `n_components` and only when `transform_algorithm = "omp"`.
Example below is similar to my current use-case. Situation occurs due to large hyperparameter grid search.
Using newest version, `sklearn==1.8.0`.
Documentation should be updated to reflect the difference in outputs and limitations.
### Steps/Code to Reproduce
```python
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler
from sklearn.compose import ColumnTransformer
if __name__ == '__main__':
column_scaler = ColumnTransformer(
transformers=[
('vert_encoder', OneHotEncoder(), [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]),
],
remainder='passthrough',
sparse_threshold=0,
)
N = 150
x_main = np.zeros((N, 26))
x_main[:, :6] = np.random.rand(N, 6)
x_main[:, 6] = np.random.randint(1, 25, size=N)
x_main[:, 7] = np.random.randint(1, 3, size=N)
x_main[:, 8] = np.random.randint(0, 5, size=N)
x_main[:, 9] = np.random.randint(1, 17, size=N)
x_main[:, 10] = np.random.randint(1, 3, size=N)
x_main[:, 11] = np.random.randint(1, 3, size=N)
x_main[:, 12] = np.random.randint(1, 3, size=N)
x_main[:, 13] = np.random.randint(0, 7, size=N)
x_main[:, 14] = np.random.randint(1, 4, size=N)
x_main[:, 15] = np.random.randint(0, 4, size=N)
x_main[:, 16] = np.random.randint(0, 4, size=N)
x_main[:, 17] = np.random.randint(0, 4, size=N)
x_main[:, 18] = np.random.randint(0, 19, size=N)
x_main[:, 19:] = np.random.rand(N, 7)
column_scaler.fit(x_main)
x_scaled = column_scaler.transform(x_main)
x_val_scaled = column_scaler.transform(x_main)
dict_params = {'n_components': 18,
'max_iter': 1000,
'transform_algorithm': 'lars',
'transform_n_nonzero_coefs': 19,
}
dl_lars = sklearn.decomposition.DictionaryLearning(random_state=0,
**dict_params)
dl_lars.fit(x_scaled)
x_scaled_transformed = dl_lars.transform(x_scaled)
print('passed (fake) training data')
x_val_scaled_transformed = dl_lars.transform(x_val_scaled)
print('passed (fake) validation data')
print('Changing to "omp"')
dict_params = {'n_components': 18,
'max_iter': 1000,
'transform_algorithm': 'omp',
'transform_n_nonzero_coefs': 19,
}
dl_omp = sklearn.decomposition.DictionaryLearning(random_state=0,
**dict_params)
dl_omp.fit(x_scaled)
x_scaled_transformed = dl_omp.transform(x_scaled)
print('passed (fake) training data')
x_val_scaled_transformed = dl_omp.transform(x_val_scaled)
print('passed (fake) validation data')
```
### Expected Results
No errors.
### Actual Results
```python
Connected to pydev debugger (build 253.28294.336)
passed (fake) training data
passed (fake) validation data
Changing to "omp"
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm\plugins\python-ce\helpers\pydev\pydevd.py", line 1647, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\JetBrains\PyCharm\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:\SpineFracturePrediction\test_sklearn_dl.py", line 69, in <module>
x_scaled_transformed = dl_omp.transform(x_scaled)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\utils\_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\decomposition\_dict_learning.py", line 1141, in transform
return self._transform(X, self.components_)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\decomposition\_dict_learning.py", line 1102, in _transform
code = sparse_encode(
^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\utils\_param_validation.py", line 218, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\decomposition\_dict_learning.py", line 374, in sparse_encode
return _sparse_encode(
^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\decomposition\_dict_learning.py", line 429, in _sparse_encode
code = _sparse_encode_precomputed(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\decomposition\_dict_learning.py", line 192, in _sparse_encode_precomputed
new_code = orthogonal_mp_gram(
^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\utils\_param_validation.py", line 191, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Miniforge3\envs\spine_frac_312\Lib\site-packages\sklearn\linear_model\_omp.py", line 605, in orthogonal_mp_gram
raise ValueError(
ValueError: The number of atoms cannot be more than the number of features
```
### Versions
```shell
sklearn.show_versions()
System:
python: 3.12.12 | packaged by conda-forge | (main, Jan 26 2026, 23:38:32) [MSC v.1944 64 bit (AMD64)]
executable: C:\Miniforge3\envs\spine_frac_312\python.exe
machine: Windows-11-10.0.26100-SP0
Python dependencies:
sklearn: 1.8.0
pip: 26.0.1
setuptools: 82.0.0
numpy: 2.4.2
scipy: 1.17.1
Cython: None
pandas: 3.0.1
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: mkl
num_threads: 8
prefix: libblas
filepath: C:\Miniforge3\envs\spine_frac_312\Library\bin\libblas.dll
version: 2020.0.4
threading_layer: intel
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libiomp
filepath: C:\Miniforge3\envs\spine_frac_312\Library\bin\libiomp5md.dll
version: None
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: vcomp
filepath: C:\Miniforge3\envs\spine_frac_312\vcomp140.dll
version: None
```
### Interest in fixing the bug
No | null | {
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] | open | false | [] | null | 2 | 2026-03-04T14:20:25 | 2026-03-06T12:23:31 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
https://github.com/scikit-learn/scikit-learn/pull/33089#discussion_r2704525722
#### What does this implement/fix? Explain your changes.
This PR deprecates the `shuffle` and `random_state` params from `TargetEncoder`. They are now redundant and had only been there in the first place because before #33089 only ints could be passed as `cv` arguments.
#### AI usage disclosure
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I used AI assistance for:
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] | closed | false | [] | null | 0 | 2026-03-04T09:20:55 | 2026-03-05T08:23:23 | 2026-03-05T08:23:23 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #33383
#### What does this implement/fix? Explain your changes.
`check_array` has a block that normalizes the `dtype` argument by calling `np.dtype(dtype)` (line ~952 in `validation.py`). This works fine for numpy dtypes and string aliases like `"float32"`, but blows up with a `TypeError` for any pandas extension dtype that has no numpy equivalent — `pd.StringDtype` being the one that hits users in practice because it became the default dtype for string columns in pandas 3.
The other nullable extension dtypes (`Int64Dtype`, `Float64Dtype`, `BooleanDtype`) are handled earlier by `_pandas_dtype_needs_early_conversion()`, which converts the array upfront and sets `dtype=None` before reaching that line. `StringDtype` isn't handled there (it's not numeric/bool), so it falls through and crashes.
Fix is a small `try/except TypeError` around the `np.dtype()` call. If the dtype can't be represented in numpy, we reset it to `None` and let numpy pick the natural dtype for the array — which is `object` for string data, exactly what you'd get from `np.asarray(pd_string_series)` anyway. No data loss, fully backward-compatible, and it also handles `CategoricalDtype` and any future pandas extension dtype that hits the same path.
**Changed files:**
- `sklearn/utils/validation.py` — wrapped `np.dtype(dtype)` in try/except TypeError (6 lines)
- `sklearn/utils/tests/test_validation.py` — added 2 regression tests for `column_or_1d` and `check_array` with `StringDtype`
- `doc/whats_new/upcoming_changes/sklearn.utils/33383.fix.rst` — changelog entry
**Test results:**
- `sklearn/utils/tests/test_validation.py`: 306 passed, 115 skipped
- `sklearn/preprocessing/tests/test_label.py`: 55 passed, 99 skipped
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
The changelog file is named `33383.fix.rst` — happy to rename it to the PR number once this is open if that's the convention (the README in that directory mentions using the PR number). | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33450 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33450/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33450/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33450/events | https://github.com/scikit-learn/scikit-learn/pull/33450 | 4,018,794,981 | PR_kwDOAAzd1s7Ht3ID | 33,450 | DOC: clarify precisions_cholesky_ stores upper-triangular Cholesky factor | {
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"node_id": "MDU6TGFiZWwxODE1... | open | false | [] | null | 0 | 2026-03-03T21:30:07 | 2026-03-03T21:30:29 | null | null | NONE | null | null | null | null | ## What does this implement/fix? Explain your changes.
Closes #10141
The `precisions_cholesky_` attribute in `GaussianMixture` was described as:
> The Cholesky decomposition of the precision matrices of each mixture component.
This phrasing is ambiguous — it could mean the full factorization object, or the lower-triangular factor `L` such that `L @ L.T = Λ`.
In reality the code stores the **upper-triangular** factor `U = L^{-T}` (where `L = chol(Σ)`) such that `U @ U.T = Λ = Σ^{-1}`. This is documented precisely in the private helper `_compute_precision_cholesky_from_precisions` but was not reflected in the public attribute docstring or the `_compute_precision_cholesky` function docstring.
## Changes
- Updated `precisions_cholesky_` attribute docstring to explicitly state "upper-triangular Cholesky factor `U` such that `U @ U.T` equals the precision matrix `Λ`".
- Updated the `_compute_precision_cholesky` function docstring to describe the mathematical relationship `U = L^{-T}` (for full/tied covariances) and element-wise square root of precisions (for diag/spherical).
## Did you add a test? If not, why?
Documentation-only change. No new tests needed.
Made with [Cursor](https://cursor.com) | null | {
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] | closed | false | [] | null | 0 | 2026-03-03T20:43:38 | 2026-03-05T08:22:54 | 2026-03-05T08:22:54 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Closes #33436.
#### What does this implement/fix? Explain your changes.
`SGDClassifier.partial_fit`, `SGDRegressor.partial_fit`, and `SGDOneClassSVM.partial_fit` currently raise `ValueError` when `sample_weight` is all zeros (behavior introduced in PR #32212). That's fine for `fit()` — fitting a fresh model on a zero-weight dataset is almost certainly a bug — but for `partial_fit` it's disruptive.
The use case is incremental/streaming workflows where zero-weight batches are used as a skip signal: you want to pass a batch through the pipeline unchanged without adding try/except blocks or pre-checks around every `partial_fit` call.
**The fix:** add an early-return in `_partial_fit` for all three estimators when all sample weights are zero. The guard fires *after* model state is initialized (so `coef_`, `intercept_`, `t_` are allocated on the first call) but *before* the SGD loop runs. This means `coef_`, `intercept_`, and `t_` are left completely unchanged.
`fit()` behavior is untouched — it still raises `ValueError` on all-zero weights.
Changes:
- `_stochastic_gradient.py`: added `_skip_on_zero_weights` parameter to `_partial_fit` in `BaseSGDClassifier`, `BaseSGDRegressor`, and `SGDOneClassSVM`. When called via `partial_fit`, this is `True`, which allows the zero-weight check to pass through `_check_sample_weight` and then triggers the early-return guard.
- `tests/test_sgd.py`: two new non-regression tests — one parametrized over `SGDClassifier`/`SGDRegressor`, one for `SGDOneClassSVM` — verifying that `coef_`, `intercept_`/`offset_`, and `t_` are unchanged after a zero-weight `partial_fit` call.
- `doc/whats_new/upcoming_changes/sklearn.linear_model/33436.fix.rst`: changelog entry.
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
I verified that all 270 SGD tests pass and the 222 common estimator checks pass (including `check_all_zero_sample_weights_error`, which only tests `fit()` and is unaffected by this change). | {
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] | closed | false | [] | null | 0 | 2026-03-03T17:59:21 | 2026-03-03T23:55:33 | 2026-03-03T23:55:33 | null | MEMBER | null | null | null | null | I made the mistake of not re-checking the diff tab before clicking the merge button of #33437...
Sorry for that. | {
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} | ### Describe the workflow you want to enable
When working with high-cardinality categorical columns (e.g. city names,
product IDs, user IDs), users need a simple, leak-proof way to encode
categories as their frequency in the training set.
Currently users must hand-roll this inside a pipeline or install external
libraries like category_encoders or feature-engine, which is error-prone
and adds unnecessary dependencies.
Example:
enc = FrequencyEncoder(normalize=True)
enc.fit_transform(X[['city']])
# London (50% of data) → 0.50
# Paris (33% of data) → 0.33
# Tokyo (17% of data) → 0.17
### Describe your proposed solution
Add a FrequencyEncoder transformer to sklearn.preprocessing with the
following API:
FrequencyEncoder(
normalize=True, # True = relative freq, False = raw count
handle_unknown=0.0, # value for unseen categories at transform time
min_frequency=None, # collapse rare labels below this threshold
)
Key design decisions:
- Works inside Pipeline and ColumnTransformer (no data leakage)
- Implements get_feature_names_out()
- Supports set_output(transform="pandas")
- Mirrors OrdinalEncoder conventions for consistency
- Multiple columns encoded independently in one transformer
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
This encoding technique is widely used in tabular ML competitions (Kaggle)
and production pipelines. It is simple, target-free, and produces a single
compact column regardless of cardinality — making it more scalable than
OneHotEncoder for large categorical vocabularies.
I am willing to implement this if maintainers agree it belongs in core. | {
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"node_id": "LA_kwDOAAzd1s8AAAA... | closed | false | [] | null | 3 | 2026-03-03T15:29:54 | 2026-03-05T14:00:23 | 2026-03-05T13:22:41 | null | MEMBER | null | null | null | null | Since the number of array API related tests is growing quite quickly and since we already run all tests for non-CUDA devices on other CI hosts, I figured we could change the pytest filter for this config.
This will also allow use to have more freedom in the naming of test functions parametrized for array API namespace testing and as a result have more freedom to evolve our array API testing policy:
https://github.com/scikit-learn/scikit-learn/pull/33345/changes#r2845855747 | {
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} | Now that we have added array API support to several important models and tools (including classifiers, regressors, transformers, meta estimators, pipelines, evaluation and model selection tools...), it might be important to discuss what are our plans and conditions to remove the "experimental" marker for that set of features.
To me, moving out of experimental means the following things in particular:
- a. Setting `array_api_dispatch=True` as the default value in the config (or maybe even deprecate this configuration parameter to always have it enabled and simplify the code as a result);
- b. Enforcing backward compatibility guarantees with array API related behavior change (the requirement for 2 release deprecation cycle and the introduction of `FutureWarning`).
For a., I think we would need to:
- [ ] ensure that all estimators and public functions/classes that have not yet been updated to support array API inputs do not raise confusing error messages on non-(NumPy + CPU) inputs when array API dispatch is enabled. I have no idea if this is the case or not. We should probably extend the common test to check that.
Furthermore, for a. to make sense we might need to wait for SciPy to also enable array API support without having the user to manually set `SCIPY_ARRAY_API=1`, and therefore:
- [ ] coordinate with upstream on their own plans to move out of experimental.
I think that for point b. we at least need to implement the following:
- [x] finalize https://github.com/scikit-learn/scikit-learn/pull/32755 to ensure consistency in mixed namespace inputs for metric functions;
- [ ] at least the common testing infrastructure for https://github.com/scikit-learn/scikit-learn/issues/28668 (to make sure that we agree on the semantics) and possibly also upgrade all our existing estimators to ensure that they pass this new common test.
- [ ] https://github.com/scikit-learn/scikit-learn/pull/33076
- [ ] https://github.com/scikit-learn/scikit-learn/pull/32873
Once this is done, it means that we would also be able to consider an extra condition:
- [ ] at least one runnable example in our doc that shows how to use scikit-learn estimators in a non-trivial pipeline with on a non-CPU device (when available).
The purpose of this issue is to agree with a shared plan and maybe milestone this as a goal for 1.9?
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"node_id": "MDU6TGFiZWwxODE1... | open | false | [] | null | 2 | 2026-03-03T12:16:07 | 2026-03-05T12:51:51 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
As discussed in https://github.com/scikit-learn/scikit-learn/pull/32732#discussion_r2866729326
#### What does this implement/fix? Explain your changes.
Improve description of values expected by `y_score` param, which listed three possibilities since #6336 of which two were meant the same.
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#### Any other comments?
The current one was a simple internal link. Adding a brief one-sentence explanation provides immediate context to the user before they click through to the more technical "Adding graphical models" section. This aligns with making the documentation more accessible to beginners.
Changes:
Modified doc/faq.rst to include a summary of why HMMs are out of scope.
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} | 0 | 2026-03-03T04:27:50 | 2026-03-03T06:01:33 | null | null | MEMBER | null | null | null | null | <!--
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Fixes #33390
#### What does this implement/fix? Explain your changes.
This PR fixes an overflow issue when [<code>RidgeClassifier</code>](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html) is fitted with unsigned integer labels.
#### AI usage disclosure
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I used AI assistance for:
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- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
N / A
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33440 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33440/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33440/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33440/events | https://github.com/scikit-learn/scikit-learn/pull/33440 | 4,012,615,017 | PR_kwDOAAzd1s7HZsou | 33,440 | ENH add sparse and sample_weight support to MultiTaskElasticNet | {
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"node_id": "MDU6... | open | false | [] | null | 0 | 2026-03-02T18:12:04 | 2026-03-05T10:50:23 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Closes #3702, finally.
Contributes to #33457
#### What does this implement/fix? Explain your changes.
This add support to fit
- with `sample_weight`
- on sparse `X`
for
- `MultiTaskElasticNet`
- `MultiTaskLasso`
- `MultiTaskElasticNetCV`
- `MultiTaskLassoCV`
#### AI usage disclosure
None
#### Any other comments?
https://github.com/scikit-learn/scikit-learn/pull/15436#issuecomment-586288621:
> It's just that I hear something that we are consistent in linear models
with certain options implemented
only in certain estimators without clear reason. So it suggests we should
have sample_weights added to CV
classes very soon and ideally in the same release cycle
*Only* 6 years later and not in the same release 😄 (but note that the comment did not apply to multitask estimators of this PR).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33439 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33439/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33439/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33439/events | https://github.com/scikit-learn/scikit-learn/pull/33439 | 4,012,344,801 | PR_kwDOAAzd1s7HYyiP | 33,439 | Fix standard scaler negative weights | {
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"node_id": "MD... | closed | false | [] | null | 1 | 2026-03-02T17:12:22 | 2026-03-03T04:16:52 | 2026-03-03T04:16:48 | null | NONE | null | null | null | null | #### What does this implement/fix?
`StandardScaler.fit` and `.partial_fit` now reject negative `sample_weight`.
They now call `_check_sample_weight(..., ensure_non_negative=True)`
This prevents negative weights from producing impossible negative variance, a RuntimeWarning, and silent `scale_=1` fallback.
#### Any other comments?
- Validation hardening: zero behavior change for valid (non-negative) weights.
- Added minimal test `test_standard_scaler_negative_sample_weight` covering both `fit` and `partial_fit`.
#### Notes
This is a gap in the preprocessing module (`sklearn.preprocessing`).
Most estimators are solid with the `ensure_non_negative` flag. Preprocessing has not this flag.
**AI usage disclosure**
I used AI assistance for:
- [x] Code generation
- [x] Test generation
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33438 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33438/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33438/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33438/events | https://github.com/scikit-learn/scikit-learn/issues/33438 | 4,011,805,560 | I_kwDOAAzd1s7vH0t4 | 33,438 | CI Add aggregating job in GHA to summarize statuses of all unit test jobs and be used a required check | {
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} | Follow-up on https://github.com/scikit-learn/scikit-learn/issues/32434, the idea would be to have a single ggregating job (green if all unit test jobs are green, red if one of the unit test is red) to use it as required checks. Azure had a built-in aggregated job which we were using for our required check and right now our GHA required checks are based on a number of exact job names which is a bit brittle.
From a quick look some time ago https://github.com/scikit-learn/scikit-learn/pull/31832#issuecomment-3116678817:
- [github.com/orgs/community/discussions/4324#discussioncomment-13335158](https://github.com/orgs/community/discussions/4324#discussioncomment-13335158)
- third-party GitHub action [re-actors/alls-green](https://github.com/re-actors/alls-green?rgh-link-date=2025-07-25T07%3A08%3A55Z) that seems to handle this use case
I am wondering whether if this is doable/easy to take into account the status "optional" jobs, for example free-threaded or scipy-dev in a PR.
----------------
More details, GitHub required checks are managed centrally via the web UI on a job level and are based on exact job names. Setting is [here](https://github.com/scikit-learn/scikit-learn/settings/branch_protection_rules/23621449)
<img width="794" height="772" alt="Image" src="https://github.com/user-attachments/assets/2a29e0fd-2364-4eff-8037-f1374c395218" />
That means that it you change a job name in `main` and then update the required checks, all the PRs will have a pending job that never complete and the PR can not be merged. The work-around is to merge `main` into your PR branch. Having a single aggregating job used a
I think @FrancoisPgm had an initial look at one point, feel free to add more relevant context if you have some. | null | {
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This PR should fix the failures discovered in the related lockfile update PRS: #33434 and #33432. | {
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} | ### Describe the bug and give evidence about its user-facing impact
`SGDClassifier.partial_fit` changes the model state even when the new batch has `sample_weight=np.zeros(n_samples)`. In the code below, an all-zero-weight batch changes `coef_`, which affects subsequent predictions, and advances `t_`, which changes the learning-rate schedule for later partial_fit calls. I expect such a batch to behave like skipping it entirely. This matters to me because in weighted incremental-learning workflows, zero sample weights are a natural way to ignore samples or whole batches without changing the surrounding batching code. And the current behavior means that batches kept with zero weight are not equivalent to skipped batches, which silently changes the optimization path.
Related to #16298
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import SGDClassifier
def main():
X, y = make_classification(n_samples=40, random_state=1)
clf = SGDClassifier(random_state=0)
clf.partial_fit(X[:10], y[:10], classes=np.array([0, 1]))
coef_before = clf.coef_.copy()
t_before = clf.t_
clf.partial_fit(X[10:20], y[10:20], sample_weight=np.zeros(10))
coef_changed = bool(np.max(np.abs(clf.coef_ - coef_before)) > 0)
print("coef_changed:", coef_changed)
print("t_before:", t_before)
print("t_after:", clf.t_)
if __name__ == "__main__":
main()
```
### Expected Results
coef_changed: False
t_before: 11.0
t_after: 11.0
### Actual Results
coef_changed: True
t_before: 11.0
t_after: 21.0
### Versions
```shell
System:
python: 3.10.12 (main, Jan 26 2026, 14:55:28) [GCC 11.4.0]
executable: /usr/bin/python3.10
machine: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35
Python dependencies:
sklearn: 1.7.2
pip: 26.0.1
setuptools: 59.6.0
numpy: 2.2.6
scipy: 1.15.3
Cython: None
pandas: None
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libscipy_openblas
filepath: /home/yang/.local/lib/python3.10/site-packages/numpy.libs/libscipy_openblas64_-56d6093b.so
version: 0.3.29
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libscipy_openblas
filepath: /home/yang/.local/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-68440149.so
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libgomp
filepath: /home/yang/.local/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
version: None
```
### Interest in fixing the bug
Yes, I would be interested in working on a PR if this is triaged as a bug. The issue seems to be that zero-weight samples are only zeroed out at the gradient-update stage, but are not skipped earlier in the optimization loop. The coefficient change disappears with `penalty=None`, which suggests the current behavior is tied to regularization being applied before zero-weight samples are fully skipped. My plan is to skip zero-weight samples earlier in the low-level SGD loop and add a test for an all-zero-weight `partial_fit` batch. Does that sound like the right direction? | null | {
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N/A
#### What does this implement/fix? Explain your changes.
This PR enables `clang-cl` support for Windows ARM64 builds in the CI pipeline.
Majority of benchmarks show performance improvement with `clang-cl` (about ~50%).
Minor to low regressions observed in a few cases (upto ~3%).
Changes include:
- clang-cl based build support
- Fix for uint32_t redefinition on Windows with clang
- Updated libomp.dll handling in vendor.py
- Update `test_sizeof_LARGEST_INT_t` test to handle `clang-cl`
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
### Benchmark Results
#### 1. cluster.KMeansBenchmark.time_predict
| Representation | Algorithm | MSVC (init_random) | Clang-CL (init_random) | Δ % | MSVC (init_kmeans++) | Clang-CL (init_kmeans++) | Δ % |
|----------------|-----------|-------------------|----------------------|-----|---------------------|------------------------|-----|
| dense | lloyd | 2.61 ms | 2.00 ms | +23.37% | 2.85 ms | 2.01 ms | +29.47% |
| dense | elkan | 2.84 ms | 2.03 ms | +28.52% | 2.76 ms | 2.04 ms | +26.09% |
| sparse | lloyd | 5.81 ms | 4.89 ms | +15.83% | 5.29 ms | 4.63 ms | +12.48% |
| sparse | elkan | 6.30 ms | 5.44 ms | +13.65% | 6.28 ms | 5.47 ms | +12.90% |
---
#### 2. cluster.KMeansBenchmark.time_transform
| Representation | Algorithm | MSVC (init_random) | Clang-CL (init_random) | Δ % | MSVC (init_kmeans++) | Clang-CL (init_kmeans++) | Δ % |
|----------------|-----------|-------------------|----------------------|-----|---------------------|------------------------|-----|
| dense | lloyd | 38.5 ms | 34.5 ms | +10.39% | 37.0 ms | 32.8 ms | +11.35% |
| dense | elkan | 33.6 ms | 32.7 ms | +2.68% | 31.6 ms | 32.1 ms | -1.58% |
| sparse | lloyd | 4890 ms | 5060 ms | -3.48% | 5030 ms | 5050 ms | -0.40% |
| sparse | elkan | 4950 ms | 5060 ms | -2.22% | 4980 ms | 5160 ms | -3.61% |
---
#### 3. linear_model.RidgeBenchmark.time_predict
| Representation | Solver | MSVC | Clang-CL | Δ % |
|----------------|--------|------|----------|-----|
| dense | auto | 14.6 ms | 10.3 ms | +29.45% |
| dense | svd | 10.4 ms | 10.0 ms | +3.85% |
| dense | cholesky | 9.96 ms | 10.1 ms | -1.41% |
| dense | lsqr | 18.4 ms | 10.0 ms | +45.65% |
| dense | sparse_cg | 10.2 ms | 10.2 ms | 0% |
| dense | sag | 14.8 ms | 10.2 ms | +31.08% |
| dense | saga | 10.1 ms | 10.3 ms | -1.98% |
| sparse | auto | 3.22 ms | 3.24 ms | -0.62% |
| sparse | cholesky | 3.27 ms | 3.25 ms | +0.61% |
| sparse | lsqr | 3.32 ms | 3.28 ms | +1.20% |
| sparse | sparse_cg | 3.22 ms | 3.24 ms | -0.62% |
| sparse | sag | 3.19 ms | 3.26 ms | -2.19% |
| sparse | saga | 3.21 ms | 3.22 ms | -0.31% |
---
#### 4. KNeighborsClassifierBenchmark.time_predict
| Algorithm | MSVC (low) | Clang-CL (low) | Δ % | MSVC (high) | Clang-CL (high) | Δ % |
|-----------|-----------|---------------|-----|------------|----------------|-----|
| brute | 89.0 ms | 89.5 ms | -0.56% | 4889 ms | 4678 ms | +4.32% |
| kd_tree | 308 ms | 308 ms | 0% | 5381 ms | 3152 ms | +41.42% |
| ball_tree | 761 ms | 535 ms | +29.70% | 5291 ms | 2622 ms | +50.44% |
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### Note
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### Note
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Clarified why double underscore (`__`) is used instead of dot (`.`) for nested parameters in GridSearchCV.
Changes applied to:
- Glossary
- User Guide
- GridSearchCV docstring
### Why
The previous wording was unclear and could confuse users when setting parameters in a Pipeline.
This documentation update helps beginners understand the correct syntax.
### Notes
No code behavior is changed; this is purely documentation. | {
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