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 through bagging and boosting, a text classification workflow with support for Chinese text segmentation, and comprehensive model performance evaluation through error analysis and the visualization of decision boundaries. The project also covers data preprocessing tasks such as feature normalization, vectorization, and the parsing of tabular data.