Unit 2 | Ep 04: The Great Equalizer – Feature Scaling
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Unit 2 | Ep 04: The Great Equalizer – Feature Scaling
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About this listen
Welcome to Mindforge ML. In this episode, we explore Feature Scaling—the mathematics of fairness in machine learning.
When one feature ranges from 0-1 and another from 0-10,000, your model gets confused. We discuss how to bring all your data to a level playing field without losing the relationships between them.
Key topics:
Normalization vs. Standardization: The battle between Min-Max and Z-Score.
Algorithm Sensitivity: Why KNN and SVMs fail without scaling, while Random Forests don't care.
Robust Scaling: How to scale data that is full of outliers.
Data Leakage: The golden rule of fit_transform() vs. transform().
Make sure your model listens to every feature equally.
Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI