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Linfa Menu
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.
Where does linfa stand right now? Are we learning yet?
linfa currently provides sub-packages with the following algorithms:
| Name | Purpose | Status | Category | Notes |
|---|---|---|---|---|
| bayes | Naive Bayes | Tested | Supervised learning | Contains Bernouilli, Gaussian and Multinomial Naive Bayes |
| clustering | Data clustering | Tested / Benchmarked | Unsupervised learning | Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model, DBSCAN and OPTICS |
| ensemble | Ensemble methods | Tested | Supervised learning | Contains bagging, random forest and AdaBoost |
| elasticnet | Elastic Net | Tested | Supervised learning | Linear regression with elastic net constraints |
| ftrl | Follow The Regularized Leader - proximal | Tested / Benchmarked | Partial fit | Contains L1 and L2 regularization. Possible incremental update |
| hierarchical | Agglomerative hierarchical clustering | Tested | Unsupervised learning | Cluster and build hierarchy of clusters |
| ica | Independent component analysis | Tested | Unsupervised learning | Contains FastICA implementation |
| kernel | Kernel methods for data transformation | Tested | Pre-processing | Maps feature vector into higher-dimensional space |
| lars | Linear regression | Tested | Supervised learning | Contains Least Angle Regression (LARS) |
| linear | Linear regression | Tested | Supervised learning | Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM) |
| logistic | Logistic regression | Tested | Partial fit | Builds two-class logistic regression models |
| nn | Nearest Neighbours & Distances | Tested / Benchmarked | Pre-processing | Spatial index structures and distance functions |
| pls | Partial Least Squares | Tested | Supervised learning | Contains PLS estimators for dimensionality reduction and regression |
| preprocessing | Normalization & Vectorization | Tested / Benchmarked | Pre-processing | Contains data normalization/whitening and count vectorization/tf-idf |
| reduction | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping, Principal Component Analysis (PCA), Random projections |
| svm | Support Vector Machines | Tested | Supervised learning | Classification or regression analysis of labeled datasets |
| trees | Decision trees | Tested / Benchmarked | Supervised learning | Linear decision trees |
| tsne | Dimensionality reduction | Tested | Unsupervised learning | Contains exact solution and Barnes-Hut approximation t-SNE |
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!