Neural Nets and Deep learning Lec 15 : Evaluation of Binary Classifiers
This video provides intuitive explanation of important Evaluation parameters for Binary classifiers using a toy example. Discussed Evaluation Parameters include Confusion Matrix, Precision, Recall, F1 score, Accuracy, Specificity, ROC Curve, Area under Curve, False Positive Rate, FPR, log loss, and Matthew correlation coefficient.