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The project was started in 2007 by david cournapeau as a google summer of code project, and since then many volunteers have contributed It assumes basic working knowledge of machine. See the about us page for a list of core contributors
It is currently maintained by a team of volunteers It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities It builds on other scientific libraries like numpy, scipy and matplotlib to provide efficient tools for predictive data analysis and data mining
It offers a consistent and simple interface for a range of supervised and unsupervised learning algorithms, including classification, regression.
Install the latest official release This is the best approach for most users This is a quick option for those who have operating. The fit method generally accepts 2 inputs
The samples matrix (or design matrix) x The size of x is typically (n_samples, n_features), which means that samples are represented as rows and features are represented as columns The target values y which are real numbers for regression tasks, or integers for classification (or any other discrete set of values) For unsupervised learning tasks, y.
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