+++
title = "About"
date = 2021-02-26
+++
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.
## Current state
Where does `linfa` stand right now? [Are we learning yet?](http://www.arewelearningyet.com/)
`linfa` currently provides sub-packages with the following algorithms:
<div class="outer-table">
| [clustering](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-clustering) | Data clustering | Tested / Benchmarked | Unsupervised learning | Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model, DBSCAN and OPTICS |
| [kernel](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-kernel) | Kernel methods for data transformation | Tested | Pre-processing | Maps feature vector into higher-dimensional space|
| [linear](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-linear) | Linear regression | Tested | Partial fit | Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM) |
| [elasticnet](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-elasticnet) | Elastic Net | Tested | Supervised learning | Linear regression with elastic net constraints |
| [logistic](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-logistic) | Logistic regression | Tested | Partial fit | Builds two-class logistic regression models
| [reduction](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-reduction) | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping and Principal Component Analysis (PCA) |
| [trees](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-trees) | Decision trees | Experimental | Supervised learning | Linear decision trees
| [svm](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-svm) | Support Vector Machines | Tested | Supervised learning | Classification or regression analysis of labeled datasets |
| [hierarchical](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-hierarchical) | Agglomerative hierarchical clustering | Tested | Unsupervised learning | Cluster and build hierarchy of clusters |
| [bayes](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-bayes) | Naive Bayes | Tested | Supervised learning | Contains Gaussian Naive Bayes |
| [ica](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-ica) | Independent component analysis | Tested | Unsupervised learning | Contains FastICA implementation |
| [pls](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-pls) | Partial Least Squares | Tested | Supervised learning | Contains PLS estimators for dimensionality reduction and regression |
| [tsne](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-tsne) | Dimensionality reduction| Tested | Unsupervised learning | Contains exact solution and Barnes-Hut approximation t-SNE |
</div>
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](https://github.com/rust-ml/linfa/issues/7) and get involved!