scikit learn documentation | scikit-learn Tutorials

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scikit-learn Tutorials — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/tutorial/index.html

WEBWelcome to scikit-learn; scikit-learn Tutorials. An introduction to machine learning with scikit-learn; A tutorial on statistical-learning for scientific data processing; Working With Text Data; Choosing the right estimator; External Resources, Videos and Talks; Getting Started; User Guide; Glossary of Common Terms and API Elements; Examples ...

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scikit-learn: machine learning in Python — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/index.html

WEBMachine Learning in Python. Getting Started Release Highlights for 1.4 GitHub. Simple and efficient tools for predictive data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - …

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User guide: contents — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/user_guide.html

WEBUser Guide ¶. 1. Supervised learning. 1.1. Linear Models. 1.2. Linear and Quadratic Discriminant Analysis. 1.3. Kernel ridge regression. 1.4. Support Vector Machines. 1.5. Stochastic Gradient Descent. 1.6. Nearest Neighbors. 1.7. Gaussian Processes. 1.8. Cross decomposition. 1.9. Naive Bayes. 1.10. Decision Trees. 1.11.

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Getting Started — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/getting_started.html

WEBScikit-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 …

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Welcome to scikit-learn — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/preface.html

WEBPurpose of this document. Statement of purpose: Scikit-learn in 2018. Architectural / general goals. Subpackage-specific goals. Scikit-learn governance and decision-making. Roles And Responsibilities. Decision Making Process. Enhancement proposals (SLEPs) , Installing scikit-learn- Installing the latest release, Third party distributions of ...

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scikit-learn: machine learning in Python — scikit-learn 1.3.0 documentation
scikit-learn.org

https://scikit-learn.org/0.18/_downloads/scikit-learn-docs.pdf

WEBscikit-learn: machine learning in Python — scikit-learn 1.3.0 documentation

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1.9. Naive Bayes — scikit-learn 1.4.2 documentation
scikit-learn.org

https://scikit-learn.org/stable/modules/naive_bayes.html

WEBNaive Bayes — scikit-learn 1.4.2 documentation. 1.9. Naive Bayes ¶. Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.

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scikit-learn: machine learning in Python - GitHub
github.com

https://github.com/scikit-learn/scikit-learn

WEBThe documentation includes more detailed installation instructions. Changelog. See the changelog for a history of notable changes to scikit-learn. Development. We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective.

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scikit-learn - Wikipedia
wikipedia.org

https://en.wikipedia.org/wiki/Scikit-learn

WEBscikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification , regression and clustering algorithms including support-vector machines , random forests , gradient boosting , k -means and DBSCAN , and is designed to interoperate ...

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Plotting Learning Curves and Checking Models’ Scalability - scikit-learn
scikit-learn.org

https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html

WEBLearning curves show the effect of adding more samples during the training process. The effect is depicted by checking the statistical performance of the model in terms of training score and testing score. Here, we compute the learning curve of a naive Bayes classifier and a SVM classifier with a RBF kernel using the digits dataset. The from ...

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