Presentation on theme: "Curva ROC figuras esquemáticas Curva ROC figuras esquemáticas Prof. Ivan Balducci FOSJC / Unesp."— Presentation transcript:
Curva ROC figuras esquemáticas Curva ROC figuras esquemáticas Prof. Ivan Balducci FOSJC / Unesp
Each threshold c corresponds to a point (FPR, DR) on the X-Y plane. A ROC curve is obtained as c sweeps from - to + . cc Receiver Operating Characteristic (ROC) Curve 2
Lab Tests: What is “Abnormal”?
Desempenho True Positive False Positive False Negative True Negative AB TP – Classe é A e classificamos como A TN – Classe é B e classificamos como B FP – Classe é B e classificamos como A FN – Classe é A e classificamos como B
The Cut-off Value Trade off Sensitivity and specificity depend on the cut off value between what we define as normal and abnormal Assume high test values are abnormal; then, moving the cut-off value to a higher one increases FN results and decreases FP results (i.e. more specific) and vice versa There is always a trade off in setting the cut-off point
Each threshold c corresponds to a point (FPR, DR) on the X-Y plane. A ROC curve is obtained as c sweeps from - to + . cc Receiver Operating Characteristic (ROC) Curve 6
Receiver Operating Characteristic (ROC) Curves
Goodness-Of-Fit: Other Measures of Model Performance ROC (Receiver Operating Characteristic) Curve Sensitivity and Specificity are dependent on a given cut-point c. An ROC curve is obtained by plotting sensitivity against (1- specificity) for an entire range of possible cut-points. The area under the ROC curve is a measure of the model’s ability to discriminate between event and non-event in the following fashion: »Among all possible pairs (event, non-event), the proportion of pairs for which the event has higher probability than the corresponding non-event is equal to the area under ROC.
ROC Curve Interpretation: Area Under ROC Curve –If randomly selected pairs of subjects (one with event and one with non-event) are classified in such a way that the subject with higher estimated probability of the event belongs to the event group and the other subject to non-event group, then the proportion of correctly classified such pairs of subjects would be equal to the area under ROC Generally Accepted Rule: ROC = 0.5: no discrimination (no better than coin toss) 0.7 <= ROC < 0.8: acceptable discrimination 0.8 <= ROC < 0.9: excellent discrimination ROC > 0.9: outstanding discrimination Roc area is often used to compare predictive ability of different models;
The ROC The ROC shows the tradeoff between P FP and P TP as the threshold is varied
Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC) Healthy
0.7 Evaluating the results How can we measure the performance of a feature matcher? 0 1 1 false positive rate true positive rate # true positives # matching features (positives) 0.1 # false positives # unmatched features (negatives)
0.7 How can we measure the performance of a feature matcher? 0 1 1 false positive rate true positive rate # true positives # matching features (positives) 0.1 # false positives # unmatched features (negatives) ROC curve (“Receiver Operator Characteristic”) ROC Curves Generated by counting # current/incorrect matches, for different threholds Want to maximize area under the curve (AUC) Useful for comparing different feature matching methods For more info: http://en.wikipedia.org/wiki/Receiver_operating_characteristic http://en.wikipedia.org/wiki/Receiver_operating_characteristic
Cut-off Área sob curva Curva ROC especificidade sensibiidade