Receiver Operating Characteristic (ROC) Curve | Issue with Imbalanced Data | Explained With Example
Автор: RoboSathi
Загружено: 2025-11-22
Просмотров: 16
📘 Notes: https://robosathi.com/docs/maths/stat...
🎥 Full Video Link: • Performance Metrics - Confusion Matrix | P...
In this video, we’ll break down the Receiver Operating Characteristic (ROC) Curve — one of the most important tools to evaluate binary classifiers.
🎯 Learning Objectives
✅ What ROC Curve represents (TPR vs FPR)
✅ How threshold variation affects classification
✅ Step-by-step algorithm to plot the ROC curve
✅ Meaning of AUC (Area Under the Curve)
✅ Why ROC fails on imbalanced data and when to use Precision-Recall Curve instead
🎥 Related Video
✅ • Confusion Matrix in Machine Learning | Fal...
🎥 Full Course Link
✅ • Maths for AI & ML | Full Course - 2025
🕘 Time Stamp 🕔
00:00:00 - 00:03:38 Understanding ROC with Graph
00:03:39 - 00:06:05 Example for ROC
00:06:06 - 00:08:48 Binary Classification Example
00:08:49 - 00:21:20 Plotting ROC Graph
00:21:21 - 00:29:01 Issue with Imbalance Data
📘 Part of the Math for AI & ML series by RoboSathi
#ai #ml #roccurve #statistics #machineLearning
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