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Ruby language bindings for LIBSVM
The main features of febeling/rb-libsvm are: Machine Learning, Machine Learning Libraries, Statistical and Predictive Models, Machine Learning Frameworks.
Projects with overlapping indexed features include: catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… djcp/linnaeus — Linnaeus is a redis-backed naive Bayesian classification system. Please see the rdoc for more information. Ruby 1.9 is… cjlin1/libsvm — This project is a support vector machine library implemented in C, providing an engine for classification and… deeplearning4j/deeplearning4j — Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph… ealdent/lda-ruby — This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatically cluster documents…
CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
This project is a support vector machine library implemented in C, providing an engine for classification and regression tasks. It functions as a machine learning kernel library and a statistical model validator used to categorize data points and predict continuous numerical values. The library allows for the definition of custom kernel functions to calculate similarity between data points in specialized datasets. It also includes tools for probabilistic modeling, such as estimating class membership, data density, and distribution boundaries. Broad capabilities cover model training for multi
Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph engine for defining and executing deep learning workflows and mathematical operations within the Java Virtual Machine. The project includes a dedicated importer for loading and running pretrained models exported from Keras, TensorFlow, and ONNX formats. Its tensor computing capabilities are driven by a modular native C++ math core to execute high-performance linear algebra operations. The framework covers neural network training, deep learning model inference, and the constru