ROC and AUC Clearly explained | Everything about ROC and AUC in python

In this video I am explaining about ROC and AUC.The Receiver Operating Characteristic (ROC) curve is a graphical plot illustrating the performance of a binary classifier system across different discrimination thresholds. It shows the trade-off between true positive rate (sensitivity) and false positive rate (1-specificity). As the threshold for classifying a positive instance is varied, the true positive rate and false positive rate change, resulting in a curve that plots these rates against each other.

Area Under the Curve (AUC) quantifies the performance of a binary classification model based on the ROC curve. It represents the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. AUC ranges from 0 to 1, with higher values indicating better classifier performance. An AUC of 0.5 suggests the classifier performs no better than random guessing, while an AUC of 1 indicates perfect discrimination.

ROC and AUC are widely used in evaluating and comparing the performance of machine learning algorithms, particularly in tasks where discrimination between positive and negative instances is critical, such as medical diagnosis, fraud detection, and information retrieval. They provide insights into how well a model distinguishes between classes and help in selecting the optimal model or adjusting the classification threshold for specific application needs.


model evaluation for binary classifier :    • Everything about confusion matrix | p...