Published March 11, 2024 | Version 1.0.1
Python Library Restricted

Confidence score - True Class Probability (TCP)

Description

True Class Probability (TCP)[Corbiere et al., 2021] is a confidence score method that aims to flag whether a neural network prediction is conform to the true class probability distribution. It is a module that can be plugged on a neural network, and only requires the output of a given layer for training and operational phase. Specifically, TCP requires access to
the penultimate layer, which outputs the probability distributions (the logits).Training TCP boils down to a classification problem, which requires to have a labelled dataset (although it only requires the original labels; no additional labelling effort is required).

It is dedicated to classification and detection use cases.

Documentation

Methodological Guidelines

Benchmark

State of the art

Support

Support for True Class Probability must be obtained by sending an email to support@confiance.ai

Ensure your email contains :

  • Your name
  • A link to this page
  • the version you are working with
  • A clear description of the problematic (bug, crash, feature or help request)
  • A full description of the problem whichallow to reproduce it
  • Any file or screenshort element mandatory for the full understanding of the problematic

Files

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Additional details

Trustworthy Attributes
Reliability
Accountability
Robustness
Engineering roles
Systems Engineer
ML-Algorithm Engineer
Business domain expert
Use cases
Visual Inspection
Vision
Functional Set
Operation
Evaluation
Robustness
Functional maturity
Technological maturity