Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues, such as model predictive performance, data integrity, data distribution mismatches, and more. The package is distributed under the GNU Affero General Public License (AGPL) and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code, documentation, examples, and an extensive user guide can be found at \url{https://github.com/deepchecks/deepchecks} and \url{https://docs.deepchecks.com/}.
Code (1)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, such as healthcare, finance, and customer service. Despite their poten…
Structure-Invariant Testing for Machine Translation
In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and …
Dependency ParsingMachine TranslationNMTSentence+1A Toolbox for Modelling Engagement with Educational Videos
With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and t…
Better Recommendations: Validating AI-generated Subject Terms Through LOC Linked Data Service
This article explores the integration of AI-generated subject terms into library cataloging, focusing on validation through the Library of Congress Linked Data Service. It examines the challenges of traditional subject c…
Deep Learning Library Testing: Definition, Methods and Challenges
In recent years, software systems powered by deep learning (DL) techniques have significantly facilitated people's lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying…
Deep LearningSurvey