1 个仓库
Decision tree models specialized for predicting continuous numerical values.
Distinct from Tree Traversal Algorithms: Specifically implements regression trees for numerical prediction, unlike general tree traversal algorithms.
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This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro
Implements regression trees to estimate numerical outcomes by partitioning data into recursive segments.