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‎docs/website/content/about.md‎

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title = "About"
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date = 2021-02-26
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date = 2025-12-23
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Linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.
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<div class="outer-table">
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| Name | Purpose | Status | Category | Notes |
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| :--- | :--- | :---| :--- | :---|
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| [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 |
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| [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|
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| [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) |
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| [elasticnet](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-elasticnet) | Elastic Net | Tested | Supervised learning | Linear regression with elastic net constraints |
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| [logistic](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-logistic) | Logistic regression | Tested | Partial fit | Builds two-class logistic regression models
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| [reduction](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-reduction) | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping and Principal Component Analysis (PCA) |
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| [trees](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-trees) | Decision trees | Experimental | Supervised learning | Linear decision trees
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| [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 |
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| [hierarchical](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-hierarchical) | Agglomerative hierarchical clustering | Tested | Unsupervised learning | Cluster and build hierarchy of clusters |
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| [bayes](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-bayes) | Naive Bayes | Tested | Supervised learning | Contains Gaussian Naive Bayes |
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| [ica](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-ica) | Independent component analysis | Tested | Unsupervised learning | Contains FastICA implementation |
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| [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 |
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| [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 |
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| Name | Purpose | Status | Category | Notes |
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| :-------------------------------------------------------------------------------------------- | :--------------------------------------- | :-------------------- | :-------------------- | :---------------------------------------------------------------------------------------- |
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| [bayes](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-bayes/) | Naive Bayes | Tested | Supervised learning | Contains Bernouilli, Gaussian and Multinomial Naive Bayes |
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| [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 |
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| [ensemble](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-ensemble/) | Ensemble methods | Tested | Supervised learning | Contains bagging, random forest and AdaBoost |
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| [elasticnet](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-elasticnet/) | Elastic Net | Tested | Supervised learning | Linear regression with elastic net constraints |
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| [ftrl](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-ftrl/) | Follow The Regularized Leader - proximal | Tested / Benchmarked | Partial fit | Contains L1 and L2 regularization. Possible incremental update |
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| [hierarchical](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-hierarchical/) | Agglomerative hierarchical clustering | Tested | Unsupervised learning | Cluster and build hierarchy of clusters |
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| [ica](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-ica/) | Independent component analysis | Tested | Unsupervised learning | Contains FastICA implementation |
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| [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 |
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| [lars](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-lars/) | Linear regression | Tested | Supervised learning | Contains Least Angle Regression (LARS) |
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| [linear](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-linear/) | Linear regression | Tested | Supervised learning | Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM) |
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| [logistic](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-logistic/) | Logistic regression | Tested | Partial fit | Builds two-class logistic regression models |
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| [nn](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-nn/) | Nearest Neighbours & Distances | Tested / Benchmarked | Pre-processing | Spatial index structures and distance functions |
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| [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 |
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| [preprocessing](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-preprocessing/) | Normalization & Vectorization | Tested / Benchmarked | Pre-processing | Contains data normalization/whitening and count vectorization/tf-idf |
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| [reduction](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-reduction/) | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping, Principal Component Analysis (PCA), Random projections |
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| [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 |
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| [trees](https://github.com/rust-ml/linfa/tree/master/algorithms/linfa-trees/) | Decision trees | Tested / Benchmarked | Supervised learning | Linear decision trees |
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| [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 |
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</div>

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