This repository implements random forest models in Common Lisp for classification tasks.
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This Common Lisp repository collects algorithms for online linear classification.
About cl-online-learning
cl-online-learning is an open-source Common Lisp repository that implements online linear classification algorithms, covering both binary and multiclass classifiers. Binary options include Perceptron, AROW, SCW-I, logistic regression with SGD or ADAM, and FTRL-Proximal; multiclass options include one-vs-rest, one-vs-one, multiclass AROW, and softmax with FTRL-Proximal. Learners can be updated one data point at a time or trained collectively with a train function, and predictions can be tested against a dataset. Datasets use class labels and input vectors declared as single-float, and a READ-DATA function reads LIBSVM sparse format when given the feature count. The library can be loaded via Quicklisp, cloned from GitHub, or installed with Roswell, and command-line tools are provided as roswell scripts. The README notes that no convergence test is implemented, so training may need to be run several times.
Key features
- Binary classifiers: Perceptron, AROW, SCW-I, logistic regression with SGD or ADAM, FTRL-Proximal
- Multiclass methods: one-vs-rest, one-vs-one, multiclass AROW, softmax with FTRL-Proximal
- FTRL-Proximal drives weights to exactly zero via L1 regularization
- Data point is a class label and single-float input vector
- READ-DATA reads LIBSVM sparse format when given the number of features
- Command-line tools implemented as Roswell scripts
Best for: Common Lisp developers who need online linear classification algorithms for binary or multiclass datasets.
Good for
- Open source repository with multiple online linear classification algorithms
- Available from Quicklisp and installable via Roswell or GitHub clone
- Supports both binary and multiclass classification tasks
Watch out for
- Convergence test is not implemented; training may need several calls
- Input vectors and class labels must be declared as single-float
- READ-DATA requires the number of features to be supplied
cl-online-learning at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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cl-online-learning FAQ
How can cl-online-learning be installed?
It is available from Quicklisp via (ql:quickload :cl-online-learning), by cloning into quicklisp/local-projects, or with Roswell using ros install masatoi/cl-online-learning.
What format does the dataset reader expect?
READ-DATA reads a sparse format used in LIBSVM and requires the number of features; data points are a class label (+1 or -1) and a single-float input vector.
Can I train a model incrementally?
Yes, each model has an update function for one data point, and a TRAIN function learns a dataset collectively; the README notes training may need to be called several times until convergence because a convergence test is not implemented.
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