Module 5 –Model Evaluation | KTU FDS | Confusion Matrix, Precision, Recall, ROC & Boosting Explained

Описание: Welcome to Module 5 of the Foundation of Data Science (KTU Syllabus)! 🎓
In this final module, we explore how to evaluate and improve model performance — the key step to ensure your data science models are accurate, reliable, and ready for real-world use.

📚 Topics Covered (KTU Module 5):
✅ Evaluating Model Performance

Confusion Matrix

Precision & Recall

Sensitivity & Specificity

F-Measure

ROC Curves

Cross Validation

K-Fold Cross Validation

Bootstrap Sampling

✅ Improving Model Performance

Bagging

Boosting

Random Forests

💡 You’ll Learn:

How to measure a model’s accuracy and effectiveness

How metrics like precision, recall, and F1-score help in decision-making

The difference between training, testing, and validation

How ensemble methods like bagging and boosting enhance model stability

🧠 Ideal For:

KTU B.Tech students

Beginners in AI, ML, and Data Science

Anyone preparing for KTU exams, interviews, or mini projects

📺 Watch till the end to master the key evaluation metrics and model improvement techniques used by every data scientist!
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