viodotcom/ppca_rs
Python+Rust implementation of the Probabilistic Principal Component Analysis model
This tool helps data analysts and scientists process complex datasets by simplifying data and handling gaps. It takes in raw numerical data, even if some values are missing, and outputs a refined dataset with noise reduced and missing values intelligently filled, along with statistical insights. Professionals working with large, imperfect datasets who need robust statistical models will find this useful.
No commits in the last 6 months.
Use this if you need to reduce the complexity of high-dimensional data, fill in missing values with statistical confidence, and get a deeper probabilistic understanding of your data rather than just averages.
Not ideal if your dataset is small, complete, and you only require basic dimensionality reduction without a need for probabilistic insights or handling missing data.
Stars
36
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2
Language
Rust
License
MIT
Category
Last pushed
Aug 27, 2024
Commits (30d)
0
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