Not all features contribute equally to learning.

This episode focuses on feature selection — the process of identifying relevant and meaningful features while removing redundant and irrelevant information.

Key topics:

• Feature relevance: Why irrelevant features harm accuracy.

• Filter methods: Statistical techniques for feature selection.

• Wrapper methods: Model-based feature evaluation.

• Embedded methods: Feature selection during model training.

• Practical guidelines: When to use which method.

This episode connects theory with exam-oriented and real-world decision making.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAIhttps://internship.chatakeinnoworks.com