Skip to main navigation
Skip to search
Skip to main content
Universidad Adolfo Ibáñez Home
English
Español
Search content at Universidad Adolfo Ibáñez
Home
Profiles
Research units
Projects
Research output
Prizes
Press/Media
TWEEF: Trustworthiness Estimation and Enhancement Framework for Machine Learning Models
Jonathan Ugalde
, Rodrigo Salas
,
Romina Torres
, Daira Velandia
, Aurelio F. Bariviera
, Pablo A. Estevez
, Maria Paz Godoy
Faculty of Engineering and Science
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'TWEEF: Trustworthiness Estimation and Enhancement Framework for Machine Learning Models'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Computer Science
Interpretability
100%
Machine Learning Model
100%
Linguistics
100%
Predictive Performance
50%
Predictive Model
50%
Performance Metric
50%
Model Generation
50%
Machine Learning
50%
Learning System
50%
Fuzzy Mathematics
50%
Fairness Performance
50%
Binary Classification
50%
Bias Mitigation
50%
Weighted Average
50%
Unified Pipeline
50%
INIS
machine learning
100%
metrics
100%
performance
75%
evaluation
50%
assessments
50%
aggregation
50%
tools
25%
configuration
25%
ecosystems
25%
datasets
25%
dimensions
25%
learning
25%
foundations
25%
benchmarks
25%
classification
25%
calculation methods
25%
adults
25%
pipelines
25%
fuzzy logic
25%
mitigation
25%
credits
25%
Keyphrases
Conventional Performance
12%
Trust Assessment
12%
Trust Score
12%