How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Survival analysis built on top of scikit-learn
The main features of sebp/scikit-survival are: Survival Analysis, Statistical Modeling.
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Survival analysis in Python
This project is a Python data analysis library and exploratory data analysis framework designed for processing raw datasets. It provides a suite of tools for examining data, identifying anomalies, and applying statistical methods to uncover patterns. The repository functions as a machine learning modeling toolkit and a statistical data modeling suite. It includes predictive algorithms and mathematical models used to analyze relationships between data variables and derive insights from complex datasets. The project covers a broad range of capabilities including data science, machine learning
This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia
This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine learning and testing workflows. It functions as a privacy-preserving platform that models the underlying statistical distributions of source data to produce new records that maintain the original statistical properties and structural integrity. The tool distinguishes itself by utilizing CPU-optimized statistical sampling, allowing for high-performance data generation on standard hardware without the need for specialized graphics processing units. It employs a configuration-driv