Скачать или смотреть AUC-ROC Curve Explained In Hindi | Machine Learning Interview Questions

Struggling to explain the AUC-ROC curve in a technical interview? This short, clear video breaks down one of the most important evaluation metrics in machine learning so you can explain it with confidence.

We cover exactly what AI/ML hiring managers want to hear:
✅ What the ROC (Receiver Operating Characteristic) Curve actually shows.
✅ The difference between True Positive Rate (TPR) and False Positive Rate (FPR).
✅ How AUC (Area Under the Curve) provides a single score to summarize model performance.
✅ Simple ways to interpret AUC scores (from 0.5 for random guessing to 1.0 for a perfect classifier).

By the end of this video, you will be able to clearly and concisely define the AUC-ROC curve and explain its practical significance, making you a stronger candidate in your next data science or machine learning interview.

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