Model Evaluation
Showing 20 of 20 projects
A batteries-included machine learning library for Go with a scikit-learn inspired interface.
A command-line tool for red-teaming and vulnerability scanning of large language models (LLMs).
A high-level library for training and evaluating neural networks in PyTorch with a flexible engine and event system.
A benchmarking suite comparing the performance of public convolutional neural network implementations across multiple deep learning frameworks.
An intuitive Python library that adds single-line plotting functions for scikit-learn and other machine learning objects.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A multi-language library providing implementations of common supervised machine learning evaluation metrics.
A Python library for automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
A library for evaluating TensorFlow models on large datasets with distributed computation and slicing analysis.
A Python toolbox for explainable AI, providing tools for data analysis, model evaluation, and bias mitigation in machine learning.
A comparative Python framework for building, evaluating, and deploying multimodal recommender systems with auxiliary data.
A lightweight TensorFlow library for training and evaluating Generative Adversarial Networks (GANs).
Lightweight Python library for evaluating classification model robustness across out-of-distribution generalization, stability, and uncertainty metrics.
A Python library for evaluating binary classifiers in machine learning ensembles using Shapley value computation and approximation methods.
A Scala and JVM machine learning toolbox for research, education, and industry with an interactive REPL and end-to-end pipelines.
A Swiss knife collection of utility functions for developing and evaluating machine learning algorithms in Julia.
An R package for developing traditional credit risk scorecard models with functions for data preprocessing, WOE binning, variable selection, and performance evaluation.
A blockchain-based protocol for trustless evaluation and purchase of machine learning models on Ethereum.
A toolkit for evaluating and monitoring machine learning models in clinical healthcare settings.
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