linfa aims to provide a comprehensive toolkit to build Machine Learning applications
Kin in spirit to Python’s
scikit-learn, it focuses on common preprocessing tasks
and classical ML algorithms for your everyday ML tasks.
Such bold ambitions! Where are we now? Are we learning yet?
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.
Kin in spirit to Python’s scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.
linfa stand right now? Are we learning yet?
linfa currently provides sub-packages with the following algorithms:
|clustering||Data clustering||Tested / Benchmarked||Unsupervised learning||Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model and DBSCAN|
|kernel||Kernel methods for data transformation||Tested||Pre-processing||Maps feature vector into higher-dimensional space|
|linear||Linear regression||Tested||Partial fit||Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM)|
|elasticnet||Elastic Net||Tested||Supervised learning||Linear regression with elastic net constraints|
|logistic||Logistic regression||Tested||Partial fit||Builds two-class logistic regression models|
|reduction||Dimensionality reduction||Tested||Pre-processing||Diffusion mapping and Principal Component Analysis (PCA)|
|trees||Decision trees||Experimental||Supervised learning||Linear decision trees|
|svm||Support Vector Machines||Tested||Supervised learning||Classification or regression analysis of labeled datasets|
|hierarchical||Agglomerative hierarchical clustering||Tested||Unsupervised learning||Cluster and build hierarchy of clusters|
|bayes||Naive Bayes||Tested||Supervised learning||Contains Gaussian Naive Bayes|
|ica||Independent component analysis||Tested||Unsupervised learning||Contains FastICA implementation|
We believe that only a significant community effort can nurture, build, and sustain a machine learning ecosystem in Rust - there is no other way forward.
If this strikes a chord with you, please take a look at the roadmap and get involved!
Correlation analysis for dataset features
Error types in Linfa
Common metrics functions for classification and regression
Provide traits for different classes of algorithms