What is ROC curve?

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ameerdgm
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It stands for Receiver Operating Characteristic. It is basically a plot between a true positive rate and a false positive rate, and it helps us to find out the right tradeoff between the true positive rate and the false positive rate for different probability thresholds of the predicted values. So, the closer the curve to the upper left corner, the better the model is. In other words, whichever curve has greater area under it that would be the better model.
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successdig
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BettyJ00
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The ROC curve, short for Receiver Operating Characteristic curve, is a graphical representation commonly used in binary classification tasks to evaluate the performance of a predictive model. It plots the true positive rate against the false positive rate at various threshold settings. Understanding the ROC curve aids in assessing the trade-off between sensitivity and specificity of a model. For instance, in car auction platforms like https://carfast-express.com/cars/check ROC analysis could help optimize bidding strategies by assessing prediction accuracy.
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