9/4/2023 0 Comments Cross entropy loss![]() ![]() The ROC curve is plotted with TPR and FPR, where TPR (True Positive Rate) on Y-axis and FPR(False Positive Rate) on X-axis.Ĭlassification algorithms can be used in different places.To visualize the performance of the multi-class classification model, we use the AUC-ROC Curve.It is a graph that shows the performance of the classification model at different thresholds.ROC curve stands for Receiver Operating Characteristics Curve and AUC stands for Area Under the Curve.The matrix consists of predictions result in a summarized form, which has a total number of correct predictions and incorrect predictions.The confusion matrix provides us a matrix/table as output and describes the performance of the model.Where y= Actual output, p= predicted output. For Binary classification, cross-entropy can be calculated as:. ![]() The lower log loss represents the higher accuracy of the model. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |