EvALL
EvALL 2.0 (Evaluate ALL 2.0) is an evaluation tool for information systems that allows evaluation of a wide set of metrics covering many evaluation contexts, including classification, ranking, or LeWeDi.
Persistence
The user can save evaluations, as well as retrieve past evaluations.
Replicability
All evaluations are conducted using the same methodology, making them strictly comparable.
Effectiveness
All metrics are encompassed under the theory of measurement and have been doubly implemented and compared.
Generalization
Generalization is achieved through the use of a standardized input format that allows the user to evaluate all evaluation contexts.
What can I do with EvALL?
Evaluation against repository
Evaluation against your own Gold Standard
Evaluation Dashboard
Metrics
Analyze your results
Analysis Console
Publish your results
Publish your Gold Standard
Evaluation Contexts
Mono-label classification
Accuracy System Precision Kappa Precision Recall FMeasure ICM ICM NormHierarchical mono-label classification.
ICM ICM NormMulti-label classification
Precision Recall FMeasureHierarchical multi-label classification.
ICM ICM NormRanking
Precision at k R Precision MRR MAP DCG nDCGLeWiDi
Cross Entropy ICM-Soft ICM-Soft Norm
Evaluation Dashboard
The EvALL 2.0 Dashboard provides an intuitive interface for exploring and comparing the results obtained across various selected metrics and executed on information system predictions. Through dynamic and customizable graphics, the Dashboard allows for data analysis from different perspectives and adjustment of visualizations according to your research needs. Additionally, the ability to zoom in and capture screenshots of the graphics enables you to effectively document and share your findings in articles or research projects.
EvALL console
The PyEvALL console provides a comprehensive experience for visualizing and addressing format errors detected in your prediction files. From detecting duplicate instance identifiers to incorrect formats and inconsistent data types. Additionally, PyEvALL allows you to explore errors produced in the analysis of metric preconditions, helping you understand and effectively correct any inconsistencies in your systems.