What do you mean by AUC curve?

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AUC (area under curve). Higher the area under the curve, better the prediction power of the model.

In the context of machine learning, the AUC (Area Under the Curve) refers to the area under the Receiver Operating Characteristic (ROC) curve. The ROC curve is a graphical representation that illustrates the performance of a binary classification model at various classification thresholds. The AUC is a single scalar value that quantifies the overall performance of the model across different thresholds.

AUC ranges from 0 to 1, where:

  • AUC = 0.5 indicates that the model’s performance is no better than random chance.
  • AUC > 0.5 indicates better-than-random performance, with higher values indicating better classification ability.
  • AUC = 1 implies perfect classification, where the model has a true positive rate of 1 and a false positive rate of 0.

So, in summary, the AUC is a valuable metric for evaluating the discriminatory power of a binary classification model, providing a concise summary of its performance across various thresholds.