A general classifier module to allow Bayesian and LSI classifications.
Las características principales de cardmagic/classifier son: Frameworks de Machine Learning, Machine Learning Frameworks.
Las alternativas de código abierto para cardmagic/classifier incluyen: catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… rust-ml/linfa — Linfa is a classical machine learning framework and statistical learning suite implemented in Rust. It provides a… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… asafschers/scoruby — Ruby Scoring API for PMML. huggingface/candle — Candle is a minimalist machine learning framework and deep learning inference engine designed for the Rust programming… smartcorelib/smartcore — A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging…
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
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
Candle is a minimalist machine learning framework and deep learning inference engine designed for the Rust programming language. It functions as a low-level tensor computation library, providing the necessary primitives for multi-dimensional array operations and mathematical transformations required to execute pre-trained neural network models. The framework distinguishes itself through a focus on memory efficiency and hardware utilization. It employs static-typed tensor operations to enforce shape validation and memory safety at compile time, while utilizing a lazy-loaded computational graph
