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.. \_installation-instructions: ======================= Installing scikit-learn ======================= There are different ways to install scikit-learn: \* :ref:`Install the latest official release `. This is the best approach for most users. It will provide a stable version and pre-built packages are available for mo... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/install.rst | main | scikit-learn | [
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in order to avoid potential conflicts with other packages. .. prompt:: bash python3 -m venv sklearn-env source sklearn-env/bin/activate # activate pip3 install -U scikit-learn In order to check your installation, you can use: .. prompt:: bash python3 -m pip show scikit-learn # show scikit-learn version and location pyt... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/install.rst | main | scikit-learn | [
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Mac OSX -------------------- The MacPorts package is named ``py-scikit-learn``, where ``XY`` denotes the Python version. It can be installed by typing the following command: .. prompt:: bash sudo port install py312-scikit-learn Anaconda and Enthought Deployment Manager for all supported platforms ----------------------... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/install.rst | main | scikit-learn | [
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Getting Started =============== ``Scikit-learn`` is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities. The purpose of this guide is to illustrate so... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/getting_started.rst | main | scikit-learn | [
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we :ref:`load the Iris dataset `, split it into train and test sets, and compute the accuracy score of a pipeline on the test data:: >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.linear\_model import LogisticRegression >>> from sklearn.pipeline import make\_pipeline >>> from sklearn.datasets imp... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/getting_started.rst | main | scikit-learn | [
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like a normal random forest estimator >>> # with max\_depth=9 and n\_estimators=4 >>> search.score(X\_test, y\_test) 0.84... .. note:: In practice, you almost always want to :ref:`search over a pipeline `, instead of a single estimator. One of the main reasons is that if you apply a pre-processing step to the whole dat... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/getting_started.rst | main | scikit-learn | [
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===================== Data Interoperability ===================== .. currentmodule:: sklearn Scikit-learn handles four kinds of data for :term:`X` as used in `fit(X, y)`, `fit(X)`, `fit\_transform(X)` and `transform(X)` as well as :term:`Xt` as returned by `transform(X)` and `fit\_transform(X)`: - :term:`array-like` ob... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/data_interoperability.rst | main | scikit-learn | [
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.. |ss| raw:: html .. |se| raw:: html .. \_roadmap: Roadmap ======= Purpose of this document ------------------------ This document lists general directions that core contributors are interested to see developed in scikit-learn. The fact that an item is listed here is in no way a promise that it will happen, as resourc... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/roadmap.rst | main | scikit-learn | [
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should also not need to be provided to estimator constructors, ideally, but should be available as metadata alongside X. :issue:`8480` #. Passing around information that is not (X, y): Target information \* We have problems getting the full set of classes to all components when the data is split/sampled. :issue:`6231` ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/roadmap.rst | main | scikit-learn | [
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to our API contract. We are still in the process of making decisions on some of these related issues. \* `Pipeline ` and `FeatureUnion` modify their input parameters in fit. Fixing this requires making sure we have a good grasp of their use cases to make sure all current functionality is maintained. :issue:`8157` :issu... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/roadmap.rst | main | scikit-learn | [
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:html\_theme.sidebar\_secondary.remove: .. \_ml\_map: Choosing the right estimator ============================ Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flow... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/machine_learning_map.rst | main | scikit-learn | [
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.. \_inspection: Inspection ---------- Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metric and test dataset perfectly reflect the target domain, which is rarely true. In ce... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/inspection.rst | main | scikit-learn | [
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======= Support ======= There are several channels to connect with scikit-learn developers for assistance, feedback, or contributions. \*\*Note\*\*: Communications on all channels should respect our `Code of Conduct `\_. .. \_announcements\_and\_notification: Mailing Lists ============= - \*\*Main Mailing List\*\*: Joi... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/support.rst | main | scikit-learn | [
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.. \_model\_persistence: ================= Model persistence ================= .. list-table:: Summary of model persistence methods :widths: 25 50 50 :header-rows: 1 \* - Persistence method - Pros - Risks / Cons \* - :ref:`ONNX ` - \* Serve models without a Python environment \* Serving and training environments indepe... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst | main | scikit-learn | [
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Did you try :mod:`pickle` or :mod:`joblib` and found that the model cannot be persisted? It can happen for instance when you have user defined functions in your model. If yes, then you can use `cloudpickle`\_ which can serialize certain objects which cannot be serialized by :mod:`pickle` or :mod:`joblib`. Workflow Over... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst | main | scikit-learn | [
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information about the input as well, about which you can read more `here `\_\_:: from skl2onnx import to\_onnx onx = to\_onnx(clf, X[:1].astype(numpy.float32), target\_opset=12) with open("filename.onnx", "wb") as f: f.write(onx.SerializeToString()) You can load the model in Python and use the `ONNX` runtime to get pre... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst | main | scikit-learn | [
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therefore recommended to serve models using `ONNX` in a sandboxed environment to safeguard against computational and memory exploits. Also note that there are no supported ways to load a model trained with a different version of scikit-learn. While using :mod:`skops.io`, :mod:`joblib`, :mod:`pickle`, or `cloudpickle`\_... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst | main | scikit-learn | [
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on the different approaches for model persistence, the key points for each approach can be summarized as follows: \* `ONNX`: It provides a uniform format for persisting any machine learning or deep learning model (other than scikit-learn) and is useful for model inference (predictions). It can however, result in compat... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/model_persistence.rst | main | scikit-learn | [
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.. currentmodule:: sklearn.callback .. \_callbacks\_user: Callbacks ========= .. note:: The callback API is experimental, and is not yet implemented for all estimators. Please refer to the :ref:`list of callback-compatible estimators ` for more information. It may change without the usual deprecation cycle. This guide ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/callbacks.rst | main | scikit-learn | [
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the learning process of that estimator. :class:`~ScoringMonitor`, for example, records the scores at each iteration of a model. A regular callback can be registered on an estimator at any level of a composition. If a regular callback is registered on an estimator that is :term:`cloned` by a meta-estimator, possibly mul... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/callbacks.rst | main | scikit-learn | [
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0:00:00 GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 GridSearchCV - candidate-split-evaluation | LogisticRegressi... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/callbacks.rst | main | scikit-learn | [
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.. \_governance: =========================================== Scikit-learn governance and decision-making =========================================== The purpose of this document is to formalize the governance process used by the scikit-learn project, to clarify how decisions are made and how the various elements of our... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/governance.rst | main | scikit-learn | [
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accounts on various social networks and produce materials. They also have the required rights to our blog repository and other relevant accounts and platforms. \* \*\*Documentation Team\*\* Members of the documentation team engage with the documentation of the project among other things. They might also be involved in ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/governance.rst | main | scikit-learn | [
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process\*\*". Decisions (in addition to adding core contributors and TC membership as above) are made according to the following rules: \* \*\*Minor code and documentation changes\*\*, such as small maintenance changes without modification of code logic, typo fixes, or addition / correction of a sentence, but no change... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/governance.rst | main | scikit-learn | [
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.. \_visualizations: ============== Visualizations ============== Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. We provide `Display` classes that expose two methods for creating... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/visualizations.rst | main | scikit-learn | [
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.. \_related\_projects: ===================================== Related Projects ===================================== Projects implementing the scikit-learn estimator API are encouraged to use the `scikit-learn-contrib template `\_ which facilitates best practices for testing and documenting estimators. The `scikit-lear... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/related_projects.rst | main | scikit-learn | [
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and estimators under ``scikit-learn-intelex`` would give different results than ``scikit-learn`` itself. If you encounter issues while using this project, make sure you report potential issues in their respective repositories. \*\*Interface to R with genomic applications\*\* - `BiocSklearn `\_ Exposes a small number of... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/related_projects.rst | main | scikit-learn | [
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`hdbscan `\_ HDBSCAN and Robust Single Linkage clustering algorithms for robust variable density clustering. As of scikit-learn version 1.3.0, there is :class:`~sklearn.cluster.HDBSCAN`. \*\*Pre-processing\*\* - `categorical-encoding `\_ A library of sklearn compatible categorical variable encoders. As of scikit-learn ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/related_projects.rst | main | scikit-learn | [
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.. \_funding: Institutional support ===================== Scikit-learn is a community driven project. However, a number of public institutions and private entities have contributed and keep on contributing to its success and sustainability. .. div:: sk-text-image-grid-small .. div:: image-box .. image:: images/inria-lo... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/institutional_support.rst | main | scikit-learn | [
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2021 to 2023 via the NASA ROSES grant 80NSSC22K0405: "Reinforcing the Foundations of Scientific Python". `Columbia University `\_ funded Andreas Müller (2016-2020). `The University of Sydney `\_ funded Joel Nothman (2017-2021). Andreas Müller received a grant to improve scikit-learn from the `Alfred P. Sloan Foundation... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/institutional_support.rst | main | scikit-learn | [
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--- | | [GitHub](https://www.github.com) | CPU time on their Continuous Integration servers + Teams account and web hosting. | | [CircleCI](https://circleci.com/) | CPU time on their Continuous Integration servers | | [Anaconda Inc](https://www.anaconda.com) | Storage for our staging and nightly builds | | https://github.com/scikit-learn/scikit-learn/blob/main/doc/institutional_support.rst | main | scikit-learn | [
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.. currentmodule:: sklearn .. \_metadata\_routing: Metadata Routing ================ .. note:: The Metadata Routing API is experimental, and is not yet implemented for all estimators. Please refer to the :ref:`list of supported and unsupported models ` for more information. It may change without the usual deprecation c... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/metadata_routing.rst | main | scikit-learn | [
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`sample\_weight` for it and for our custom scorer by specifying `sample\_weight=True` in :class:`~linear\_model.LogisticRegressionCV`'s `set\_fit\_request()` method and in :func:`~metrics.make\_scorer`'s `set\_score\_request()` method. Both :term:`consumers ` know how to use ``sample\_weight`` in their `fit()` or `scor... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/metadata_routing.rst | main | scikit-learn | [
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(Meta-)Estimators which route metadata to other objects are :term:`routers `. A(n) (meta-)estimator can be a :term:`consumer` and a :term:`router` at the same time. (Meta-)Estimators and splitters expose a `set\_{method}\_request` method for each method which accepts at least one metadata. For instance, if an estimator... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/metadata_routing.rst | main | scikit-learn | [
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tools not supporting metadata routing yet: - :class:`sklearn.ensemble.AdaBoostClassifier` - :class:`sklearn.ensemble.AdaBoostRegressor` | https://github.com/scikit-learn/scikit-learn/blob/main/doc/metadata_routing.rst | main | scikit-learn | [
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- Mathieu Blondel - Joris Van den Bossche - Matthieu Brucher - Lars Buitinck - David Cournapeau - Noel Dawe - Vincent Dubourg - Edouard Duchesnay - Alexander Fabisch - Virgile Fritsch - Satrajit Ghosh - Angel Soler Gollonet - Chris Gorgolewski - Jaques Grobler - Yaroslav Halchenko - Brian Holt - Nicolas Hug - Arnaud Jo... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/maintainers_emeritus.rst | main | scikit-learn | [
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.. \_external\_resources: =========================================== External Resources, Videos and Talks =========================================== The scikit-learn MOOC ===================== If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the \*\*scikit-learn MOOC (M... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/presentations.rst | main | scikit-learn | [
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.. \_data-transforms: Dataset transformations ----------------------- scikit-learn provides a library of transformers, which may clean (see :ref:`preprocessing`), reduce (see :ref:`data\_reduction`), expand (see :ref:`kernel\_approximation`) or generate (see :ref:`feature\_extraction`) feature representations. Like oth... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/data_transforms.rst | main | scikit-learn | [
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.. \_datasets: ========================= Dataset loading utilities ========================= .. currentmodule:: sklearn.datasets The ``sklearn.datasets`` package embeds some small toy datasets and provides helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data t... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/datasets.rst | main | scikit-learn | [
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.. currentmodule:: sklearn .. include:: whats\_new/\_contributors.rst Release History =============== Changelogs and release notes for all scikit-learn releases are linked in this page. .. tip:: `Subscribe to scikit-learn releases `\_\_ on libraries.io to be notified when new versions are released. .. toctree:: :maxdep... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new.rst | main | scikit-learn | [
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.. raw:: html /\* h3 headings on this page are the questions; make them rubric-like \*/ h3 { font-size: 1rem; font-weight: bold; padding-bottom: 0.2rem; margin: 2rem 0 1.15rem 0; border-bottom: 1px solid var(--pst-color-border); } /\* Increase top margin for first question in each section \*/ h2 + section > h3 { margin... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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approximate inference; defines the notion of sample as an instance of the graph structure). \* `seqlearn `\_ handles sequences only (focuses on exact inference; has HMMs, but mostly for the sake of completeness; treats a feature vector as a sample and uses an offset encoding for the dependencies between feature vectors... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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homogeneous subsets of dataframe columns selected by name or dtype to dedicated scikit-learn transformers. Therefore :class:`~sklearn.compose.ColumnTransformer` are often used in the first step of scikit-learn pipelines when dealing with heterogeneous dataframes (see :ref:`pipeline` for more details). See also :ref:`sp... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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The scikit-learn review process takes a significant amount of time, and contributors should not be discouraged by a lack of activity or review on their pull request. We care a lot about getting things right the first time, as maintenance and later change comes at a high cost. We rarely release any "experimental" code, ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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sparse matrix, are accepted. The contributor should support the importance of the proposed addition with research papers and/or implementations in other similar packages, demonstrate its usefulness via common use-cases/applications and corroborate performance improvements, if any, with benchmarks and/or plots. It is ex... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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of the `issue tracker on GitHub `\_. .. warning:: Please do not email any authors directly to ask for assistance, report bugs, or for any other issue related to scikit-learn. How should I save, export or deploy estimators for production? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ See :ref:`model\_pe... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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reasons. Many libraries like (some versions of) Accelerate or vecLib under OSX, (some versions of) MKL, the OpenMP runtime of GCC, nvidia's Cuda (and probably many others), manage their own internal thread pool. Upon a call to `fork`, the thread pool state in the child process is corrupted: the thread pool believes it ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/faq.rst | main | scikit-learn | [
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.. \_common\_pitfalls: ========================================= Common pitfalls and recommended practices ========================================= The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what \*\*not\*\* to do, alo... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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the `transform` method should be used on both train and test subsets as the same preprocessing should be applied to all the data. This can be achieved by using `fit\_transform` on the train subset and `transform` on the test subset. \* The scikit-learn :ref:`pipeline ` is a great way to prevent data leakage as it ensur... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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SelectKBest(k=25)), ('histgradientboostingclassifier', HistGradientBoostingClassifier(random\_state=1))]) >>> y\_pred = pipeline.predict(X\_test) >>> accuracy\_score(y\_test, y\_pred) 0.5 The pipeline can also be fed into a cross-validation function such as :func:`~sklearn.model\_selection.cross\_val\_score`. Again, th... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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we would have obtained the same models, and thus the same scores each time. When we pass an integer, the same RNG is used across all calls to `fit`. What internally happens is that even though the RNG is consumed when `fit` is called, it is always reset to its original state at the beginning of `fit`. CV splitters ....... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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we pass an integer or an instance to :func:`~sklearn.datasets.make\_classification` isn't relevant for our illustration purpose: what matters is what we pass to the :class:`~sklearn.ensemble.RandomForestClassifier` estimator. .. dropdown:: Cloning Another subtle side effect of passing `RandomState` instances is how :fu... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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`random\_state=None`, which is the default. The recommended way is to declare a `rng` variable at the top of the program, and pass it down to any object that accepts a `random\_state` parameter:: >>> from sklearn.ensemble import RandomForestClassifier >>> from sklearn.datasets import make\_classification >>> from sklea... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/common_pitfalls.rst | main | scikit-learn | [
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.. currentmodule:: sklearn .. \_glossary: ========================================= Glossary of Common Terms and API Elements ========================================= This glossary hopes to definitively represent the tacit and explicit conventions applied in Scikit-learn and its API, while providing a reference for us... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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be set when fitting. These begin with a single underscore and are not assured to be stable for public access. A public attribute on an estimator instance that does not end in an underscore should be the stored, unmodified value of an ``\_\_init\_\_`` :term:`parameter` of the same name. Because of this equivalence, thes... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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the estimator's `random\_state` parameter is an integer (or if the estimator doesn't have a `random\_state` parameter), an \*exact clone\* is returned: the clone and the original estimator will give the exact same results. Otherwise, \*statistical clone\* is returned: the clone might yield different results from the or... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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arrays before encoding or vectorizing. Our estimators do not work with struct arrays, for instance. Our documentation can sometimes give information about the dtype precision, e.g. `np.int32`, `np.int64`, etc. When the precision is provided, it refers to the NumPy dtype. If an arbitrary precision is used, the documenta... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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parameter ``early\_stopping`` or by setting a positive :term:`n\_iter\_no\_change`. estimator instance We sometimes use this terminology to distinguish an :term:`estimator` class from a constructed instance. For example, in the following, ``cls`` is an estimator class, while ``est1`` and ``est2`` are instances:: cls = ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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is used internally for this purpose.) \* should not have any :term:`attributes` beginning with an alphabetic character and ending with an underscore. (Note that a descriptor for the attribute may still be present on the class, but hasattr should return False) function We provide ad hoc function interfaces for many algo... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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learning can be performed in integer space. :term:`Unlabeled data ` is a special case of missing values in the :term:`target`. ``n\_features`` The number of :term:`features`. ``n\_outputs`` The number of :term:`outputs` in the :term:`target`. ``n\_samples`` The number of :term:`samples`. ``n\_targets`` Synonym for :ter... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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and fitting, parameters may be modified using :term:`set\_params`. To enable this, parameters are not ordinarily validated or altered when the estimator is constructed, or when each parameter is set. Parameter validation is performed when :term:`fit` is called. Common parameters are listed :ref:`below `. pairwise metri... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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follows the decision-making process outlined in :ref:`governance`. For all votes, a proposal must have been made public and discussed before the vote. Such a proposal must be a consolidated document, in the form of a "Scikit-Learn Enhancement Proposal" (SLEP), rather than a long discussion on an issue. A SLEP must be s... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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machine learning method is designed to model a specific dataset, but not to apply that model to unseen data. Examples include :class:`manifold.TSNE`, :class:`cluster.AgglomerativeClustering` and :class:`neighbors.LocalOutlierFactor`. unlabeled unlabeled data Samples with an unknown ground truth when fitting; equivalent... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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lose its model if the meta-estimator is cloned. A meta-estimator should have ``fit`` called before prediction, even if all contained estimators are pre-fitted. In cases where a meta-estimator's primary behaviors (e.g. :term:`predict` or :term:`transform` implementation) are functions of prediction/transformation method... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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is usually an :term:`estimator`, a :term:`scorer`, or a :term:`CV splitter`. Consuming metadata means using it in calculations, e.g. using :term:`sample\_weight` to calculate a certain type of score. Being a consumer doesn't mean that the object always receives a certain metadata, rather it means it can use it if it is... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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that a dataset sampled from a multiclass ``y`` or a continuous ``y`` may appear to be binary. :func:`~utils.multiclass.type\_of\_target` will return 'binary' for binary input, or a similar array with only a single class present. continuous A regression problem where each sample's target is a finite floating point numbe... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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a multilabel problem. :func:`~utils.multiclass.type\_of\_target` will return 'multilabel-indicator' for multilabel input, whether sparse or dense. multioutput multi-output A target where each sample has multiple classification/regression labels. See :term:`multiclass multioutput` and :term:`continuous multioutput`. We ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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apply ``fit\_transform`` to the entirety of a dataset (i.e. training and test data together) before further modelling, as this results in :term:`data leakage`. ``get\_feature\_names\_out`` Primarily for :term:`feature extractors`, but also used for other transformers to provide string names for each column in the outpu... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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of the values in the classifier's :term:`classes\_` attribute. clusterer An array of shape ``(n\_samples,)`` where each value is from 0 to ``n\_clusters - 1`` if the corresponding sample is clustered, and -1 if the sample is not clustered, as in :func:`cluster.dbscan`. outlier detector An array of shape ``(n\_samples,)... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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:term:`fitting`. If the estimator was not already :term:`fitted`, calling this method should raise a :class:`exceptions.NotFittedError`. .. \_glossary\_parameters: Parameters ========== These common parameter names, specifically used in estimator construction (see concept :term:`parameter`), sometimes also appear as pa... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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estimators: some, but not all, use it to mean a single epoch (i.e. a pass over every sample in the data). .. FIXME: perhaps we should have some common tests about the relationship between ConvergenceWarning and max\_iter. ``memory`` Some estimators make use of :class:`joblib.Memory` to store partial solutions during fi... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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random seeds. Popular integer random seeds are 0 and `42 `\_. Integer values must be in the range `[0, 2\*\*32 - 1]`. A :class:`numpy.random.RandomState` instance Use the provided random state, only affecting other users of that same random state instance. Calling the function multiple times will reuse the same instanc... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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the state of the estimator since the initialization. :term:`partial\_fit` also retains the model between calls, but differs: with ``warm\_start`` the parameters change and the data is (more-or-less) constant across calls to ``fit``; with ``partial\_fit``, the mini-batch of data changes and model parameters stay fixed. ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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weights. `sample\_weight` can be both an argument of the estimator's :term:`fit` method for model training or a parameter of a :term:`scorer` for model evaluation. These callables are said to \*consume\* the sample weights while other components of scikit-learn can \*route\* the weights to the underlying estimators or ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/glossary.rst | main | scikit-learn | [
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.. \_about: ======== About us ======== History ======= This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started working on this project as part of his thesis. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRI... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/about.rst | main | scikit-learn | [
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Pedregosa and Andreas Mueller and Olivier Grisel and Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort and Jaques Grobler and Robert Layton and Jake VanderPlas and Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux}, title = {{API} design for machine learning software: experiences from the scikit-learn projec... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/about.rst | main | scikit-learn | [
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.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_10: ============ Version 1.10 ============ .. -- UNCOMMENT WHEN 1.10.0 IS RELEASED -- For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_hig... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.10.rst | main | scikit-learn | [
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.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_5: =========== Version 1.5 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_5\_0.py`. .. include:: changelog\_l... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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but also too rare tokens (below `min\_df`). This fixes a potential security issue (data leak) if the discarded rare tokens hold sensitive information from the training set without the model developer's knowledge. Note: users of those classes are encouraged to either retrain their pipelines with the new scikit-learn ver... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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``predict`` methods. :pr:`28261` by :user:`Stefanie Senger `. - |Feature| :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` now support metadata routing and pass ``\*\*fit\_params`` to the underlying estimators via their `fit` methods. :pr:`27584` by :user:`Stefanie Senger `. - |Feature| :class:`... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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<< n\_features`, this can save some memory and, more importantly, help speed-up subsequent calls to the `transform` method by more than an order of magnitude by leveraging cache locality of BLAS GEMM on contiguous arrays. :pr:`27491` by :user:`Olivier Grisel `. - |Enhancement| :class:`~decomposition.PCA` now automatica... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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Boisberranger `. - |API| Parameter `multi\_class` was deprecated in :class:`linear\_model.LogisticRegression` and :class:`linear\_model.LogisticRegressionCV`. `multi\_class` will be removed in 1.8, and internally, for 3 and more classes, it will always use multinomial. If you still want to use the one-vs-rest scheme, y... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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feature names. :pr:`28306` by :user:`Brendan Lu `. :mod:`sklearn.pipeline` ....................... - |Feature| :class:`pipeline.FeatureUnion` can now use the `verbose\_feature\_names\_out` attribute. If `True`, `get\_feature\_names\_out` will prefix all feature names with the name of the transformer that generated that... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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Senger, Steffen Schneider, Suha Siddiqui, Thanh Lam DANG, thebabush, Thomas, Thomas J. Fan, Thomas Lazarus, Tialo, Tim Head, Tuhin Sharma, Tushar Parimi, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will Dean, Xavier Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao, yareyaredesuyo, Ziad Amerr, Štěpán Sršeň | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.5.rst | main | scikit-learn | [
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.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_4: =========== Version 1.4 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_4\_0.py`. .. include:: changelog\_l... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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lenient and overwrite output column names with the `get\_feature\_names\_out` in the following cases: (i) the input and output column names remain the same (happen when using NumPy `ufunc`); (ii) the input column names are numbers; (iii) the output will be set to Pandas or Polars dataframe. :pr:`28241` by :user:`Guilla... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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for more details. - |Feature| :class:`LarsCV` and :class:`LassoLarsCV` now support metadata routing in their `fit` method and route metadata to the CV splitter. :pr:`27538` by :user:`Omar Salman `. - |Feature| :class:`multiclass.OneVsRestClassifier`, :class:`multiclass.OneVsOneClassifier` and :class:`multiclass.OutputC... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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in :pr:`27277` by :user:`Yao Xiao `; - :class:`impute.IterativeImputer` in :pr:`27277` by :user:`Yao Xiao `; - :class:`impute.KNNImputer` in :pr:`27277` by :user:`Yao Xiao `; - :class:`kernel\_approximation.PolynomialCountSketch` in :pr:`27301` by :user:`Lohit SundaramahaLingam `; - :class:`neural\_network.BernoulliRBM... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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- |Fix| Create copy of precomputed sparse matrix within the `fit` method of :class:`cluster.DBSCAN` to avoid in-place modification of the sparse matrix. :pr:`27651` by :user:`Ganesh Tata `. - |Fix| Raises a proper `ValueError` when `metric="precomputed"` and requested storing centers via the parameter `store\_centers`.... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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is deprecated and will be removed in version 1.6. Use the default value instead. :pr:`27834` by :user:`Guillaume Lemaitre `. :mod:`sklearn.ensemble` ....................... - |MajorFeature| :class:`ensemble.RandomForestClassifier` and :class:`ensemble.RandomForestRegressor` support missing values when the criterion is ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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by providing a `score\_func` taking `X` and `y=None`. :pr:`27721` by :user:`Guillaume Lemaitre `. - |Enhancement| :class:`feature\_selection.SelectKBest` and :class:`feature\_selection.GenericUnivariateSelect` with `mode='k\_best'` now shows a warning when `k` is greater than the number of features. :pr:`27841` by `Tho... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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`X\_norm\_squared` as a `float32` array. :pr:`27624` by :user:`Jérôme Dockès `. - |Fix| :func:`f1\_score` now provides correct values when handling various cases in which division by zero occurs by using a formulation that does not depend on the precision and recall values. :pr:`27577` by :user:`Omar Salman ` and :user... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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now support monotonic constraints, useful when features are supposed to have a positive/negative effect on the target. Missing values in the train data and multi-output targets are not supported. :pr:`13649` by :user:`Samuel Ronsin `, initiated by :user:`Patrick O'Reilly `. :mod:`sklearn.utils` .................... - |... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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Marek Hanuš, Maren Westermann, Mark Elliot, Martin Larralde, Mateusz Sokół, mathurinm, mecopur, Meekail Zain, Michael Higgins, Miki Watanabe, Milton Gomez, MN193, Mohammed Hamdy, Mohit Joshi, mrastgoo, Naman Dhingra, Naoise Holohan, Narendra Singh dangi, Noa Malem-Shinitski, Nolan, Nurseit Kamchyev, Oleksii Kachaiev, O... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.4.rst | main | scikit-learn | [
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.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_6: =========== Version 1.6 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_6\_0.py`. .. include:: changelog\_l... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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Array API compatible inputs. By :user:`Emily Chen ` :pr:`29207` - |Feature| :func:`sklearn.metrics.max\_error` now supports Array API compatible inputs. By :user:`Edoardo Abati ` :pr:`29212` - |Feature| :func:`sklearn.metrics.mean\_poisson\_deviance` now supports Array API compatible inputs. By :user:`Emily Chen ` :pr:... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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0.0549... | 0.110605 |
building with setuptools --------------------------------------------- From scikit-learn 1.6 onwards, support for building with setuptools has been removed. Meson is the only supported way to build scikit-learn. By :user:`Loïc Estève ` :pr:`29400` Free-threaded CPython 3.13 support ---------------------------------- sc... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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and :class:`ensemble.HistGradientBoostingRegressor` got a more granular control. Now, `verbose = 1` prints only summary messages, `verbose >= 2` prints the full information as before. By :user:`Christian Lorentzen ` :pr:`28179` - |API| The parameter `algorithm` of :class:`ensemble.AdaBoostClassifier` is deprecated and ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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By :user:`Olivier Grisel ` :pr:`30100` - |Fix| :class:`~sklearn.linear\_model.SGDOneClassSVM` now correctly inherits from :class:`~sklearn.base.OutlierMixin` and the tags are correctly set. By :user:`Guillaume Lemaitre ` :pr:`30227` - |API| Deprecates `copy\_X` in :class:`linear\_model.TheilSenRegressor` as the paramet... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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metadata, which can be set using the `transform\_input` parameter. By `Adrin Jalali`\_ :pr:`28901` - |Enhancement| :class:`pipeline.Pipeline` now warns about not being fitted before calling methods that require the pipeline to be fitted. This warning will become an error in 1.8. By `Adrin Jalali`\_ :pr:`29868` - |Fix| ... | https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/v1.6.rst | main | scikit-learn | [
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... | 0.028268 |
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