Feature engineering does not end at selection or extraction — it must be evaluated carefully.

This episode concludes Unit 3 by exploring how to assess feature quality, avoid common mistakes, and prepare for actual model training in Machine Learning.

Key topics:

• Evaluation: Measuring feature effectiveness using accuracy, generalization and efficiency.

• Challenges: Overfitting, data leakage, and improper preprocessing.

• Practical Thinking: Stability, interpretability and validation.

• Bridge Ahead: Preparing for Supervised and Unsupervised Learning.

This episode completes Unit 3 and sets the foundation for model training in upcoming units.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI