Kategóriák: Minden - evaluation - performance - metrics

a Graeme Smith 16 éve

294

Classifier Evaluation Presentation

The text outlines a structured approach to evaluating classifiers, beginning with an introduction that sets objectives and methods for comparison. It delves into the intricacies of black box classifiers, detailing the stages and features necessary for effective scoring and selection.

Classifier Evaluation Presentation

Classifier EvaluationPresentation

8. What Comes Next?

How to predict performance
Advert for my Australia paper
Possibly use error bounds
Why do this?
How to use "Don't Declare" threshold

7. Summary

What have I told them about

6. Real World Examples

Real world ROC curves
Using a target level of Pfa
Impact of a very cautious threshold
Impact of forced decision
Pre-processing and classifier used
Introduce dataset

5. ROC Curve Analysis

Interesting points in ROC space
Generating the curve
Type (b) ROC curve
Type (a) ROC curve

4. The Confusion Matrix

Test unknown target matrix
Test variant target matrix
Test target matrix

3. Evaluation Metrics

Probability of generalization
Probability of false alarm
Probability of declaration
Forced declaration
Reliability
Probability of correct classification

2. Black Box Classifier

Outputs
"Don't Declare"
Unknown
Declaration
Stages
Selection

"Don't declare"

Declare

Threshold
Scoring
Features
Inputs
Data to classify
Reference

1. Introduction

Objectives
Metrics
Comparison
Method
Radar ATR