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Models that use probability distributions to categorize data.
Distinguishing note: Focuses on Gaussian Naive Bayes and similar probabilistic approaches.
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This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
Applies a probabilistic classifier that assumes data follows a normal distribution for each target category.
This project is a data mining algorithm library and machine learning reference implementation. It provides a collection of tools for performing classification, clustering, and association rule mining, as well as a toolkit for nature-inspired optimization. The library includes specialized utilities for graph and sequence mining, enabling the extraction of frequent subgraphs and sequential patterns. It also features a dimensionality reduction utility that uses rough set theory to remove redundant attributes from datasets. The project covers a broad range of analytical capabilities, including n
Implements probabilistic models to categorize data points by handling conditional dependencies.