没看懂呢，需要解释一下

问题如下：

Explain what ROC and AUC stand for and how they could be used in making lending decisions.

解释：

ROC stands for receiver operating characteristics and AUC is the area under the curve. The ROC curve plots the true positive rate on the y-axis against the false positive rate on the x-axis and the points on the curve emerge from varying the decision threshold. The ROC curve shows the tradeoff between the true positive rate and false positive rate when selecting the decision threshold.

The AUC shows pictorially how effective the model has been in separating the data points into clusters, with a higher AUC implying a better model fit, and so the AUC can be used to compare between models. An AUC of 1 would indicate a perfect fit, whereas a value of 0.5 would correspond with an entirely random set of predictions and therefore a model with no predictive ability.

One possible application of the ROC and AUC would be in the context of comparing models to determine whether a loan application should be rejected or accepted. A better model would be one with a higher AUC.