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Installing scikit-learn — scikit-learn 1.5.2 documentation
There are different ways to install scikit-learn: Install the latest official release. This is the best approach for most users. It will provide a stable version and pre-built packages are availabl...
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1.10. Decision Trees — scikit-learn 1.5.2 documentation
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning s...
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2.3. Clustering — scikit-learn 1.5.2 documentation
Clustering of unlabeled data can be performed with the module sklearn.cluster. Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on trai...
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6.3. Preprocessing data — scikit-learn 1.5.2 documentation
The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...
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scikit-learn: machine learning in Python — scikit-learn 1.5.2 documentation
September 2024. scikit-learn 1.5.2 is available for download (Changelog). July 2024. scikit-learn 1.5.1 is available for download (Changelog). May 2024. scikit-learn 1.5.0 is available for download (C...
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1.4. Support Vector Machines — scikit-learn 1.5.2 documentation
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high ...