Classifier Evaluation Presentation

Honelako gehiago

Enterprise Information Systems 2007-08

aurelio ravarini‑k egina

Advanced Operations Management - S2

T K‑k egina

Advanced Operations Management - S2

T K‑k egina

CHAPTER 14 CONSUMER DECISION PROCESS AND PROBLEM RECOGNITION

Clarissa Villegas‑k egina

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