ROC Curve is a nice modeling concept to know as it will used practically in nearly all models

irrespective of spoefic technique and irrespective of statistical software.

We use the Wikipedia for referring to easy to implement statistics rather than crusty

thick books which seem prohibitely dense and opaque to outsiders

-This is how you define the ROC Curve.

actual value | ||||
---|---|---|---|---|

p |
n |
total | ||

prediction outcome |
p’ |
True Positive |
False Positive |
P’ |

n’ |
False Negative |
True Negative |
N’ | |

total | P | N |

true positive (TP)

- eqv. with hit
- true negative (TN)
- eqv. with correct rejection
- false positive (FP)
- eqv. with false alarm, Type I error
- false negative (FN)
- eqv. with miss, Type II error
- true positive rate (TPR)
- eqv. with hit rate, recall, sensitivity
*T**P**R*=*T**P*/*P*=*T**P*/ (*T**P*+*F**N*)- false positive rate (FPR)
- eqv. with false alarm rate, fall-out
*F**P**R*=*F**P*/*N*=*F**P*/ (*F**P*+*T**N*)- accuracy (ACC)
*A**C**C*= (*T**P*+*T**N*) / (*P*+*N*)- specificity (SPC)
*S**P**C*=*T**N*/ (*F**P*+*T**N*) = 1 ?*F**P**R*- positive predictive value (PPV)
- eqv. with precision
*P**P**V*=*T**P*/ (*T**P*+*F**P*)

Here is a good java enabled page to calculate the ROC Curve.

http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html

And in case any one asks, ROC stands for Receiver Operating Characteristic. ……