Difference Between Feature Selection vs Feature Extraction in
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Are you diving into the world of machine learning and feeling puzzled by the terms "Feature Selection" and "Feature Extraction"? 🤔 Don't worry, we've got you covered! In this quick 1-minute YouTube Shorts video, we demystify the key differences between these two fundamental concepts in machine learning.
Feature Selection involves choosing a subset of relevant features from the original dataset, aiming to improve model performance and reduce overfitting. On the other hand, Feature Extraction involves transforming the original features into a new set of features, often of lower dimensionality, while retaining the essential information.
Understanding these concepts is crucial for building robust and efficient machine learning models. Let's break it down in just 60 seconds!
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