Classifier Evaluation Presentation

Ver más

Managing Compensation

por SITI HAJAR BINTI ZAIDI

NISSAN MOTOR CORPORATION

por Iván Ortega

Measurement in Occupational Therapy Practice Mandy Miller

por Mandy Miller

Fostering Software Craftsmanship

por Paweł Badeński

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