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ROC Curve
The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of
a binary classifier system at varying threshold values. It plots the True Positive Rate (TPR) against the False Positive
Rate (FPR) at various threshold settings.
This curve plots two parameters:
• True Positive Rate (Sensitivity) is the proportion of actual positive cases that are correctly identified by the
classifier.
TP
TPR =
TP + FN
• False Positive Rate is the proportion of actual negative cases that are incorrectly classified as positive.
FP
FPR =
FP + TN
To generate an ROC curve, you need to perform the following tasks:
• Vary the threshold of your classifier, usually ranging from 0 to 1, and calculate TPR and FPR at each threshold.
• Plot these TPR and FPR values on a graph. TPR is plotted on the y-axis, and FPR is plotted on the x-axis.
Lowering the classification threshold classifies more items as positive, thus increasing both false positives and true
positives. The following figure shows a typical ROC curve.
TP vs FP rate at one
decision threshold
TP vs FP rate at
another decision
1 threshold
TP Rate
0
0 FP Rate 1
Evaluate: Exoplanet Use Case
At this particular stage, we may need to
evaluate the model to find out the accuracy of
the model that makes the best prediction.
ROC is a metric that is used to find out the
accuracy of the model.
Figure shows the accuracy of the model using
ROC curve.
216 Touchpad Artificial Intelligence (Ver. 3.0)-IX

