Neural Network Projects with Python

ROC curve

For classification tasks, we should also look at the ROC curve to evaluate our model. The ROC curve is a plot with the True Positive Rate (TPR) on the y axis and the False Positive Rate (FPR) on the x axis. TPR and FPR are defined as follows:

When we analyze the ROC curve, we look at the area under the curve (AUC) to evaluate the performance of the model that produced the curve. A large AUC indicates that the model is able to differentiate the respective classes with high accuracy, while a low AUC indicates that the model makes poor, ...