An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
scicloj.ml is an idiomatic Clojure machine learning library that provides a unified interface for various classification, regression, and unsupervised models. It solves the problem of fragmented ML tooling in Clojure by offering harmonized, data-driven pipelines and sophisticated cross-validation.
Clojure developers and data scientists looking for an idiomatic, functional approach to machine learning with seamless integration into Clojure workflows.
Developers choose scicloj.ml for its idiomatic Clojure design, extensible plugin architecture, and efficient data handling via established libraries like tech.ml.dataset and Smile.
A Clojure machine learning library
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Provides a unified, functional interface that integrates seamlessly with Clojure workflows, enabling data-driven pipelines as first-class citizens, as emphasized in the philosophy.
Leverages tech.ml.dataset for high-performance data storage and manipulation, ensuring scalability and speed with large datasets, as noted in the key features.
Open architecture allows easy integration of models from non-JVM languages, including deep learning, via plugins like sklearn.clj and scicloj.ml.clj-djl.
Includes built-in, easy-to-use cross-validation tools for evaluating pipeline performance, which is highlighted as a main feature for model selection.
The library is marked as deprecated and recommends using underlying libraries directly or the new 'noj' library, which may confuse users and limit future updates or support.
Acts as a shim over multiple libraries (e.g., tech.ml.dataset, Smile, metamorph), adding complexity that can obscure debugging and increase learning overhead.
Documentation is spread across external tutorials and repositories, and the deprecation note makes it challenging to find reliable, up-to-date guidance for new users.