Dimensionality Reduction
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What is Dimensionality Reduction?
Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. Feature selection selects a subset of the original features, while feature extraction transforms the data into a lower-dimensional space. The goal is to simplify the data without losing important information, making it easier to analyze and model.
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Dimensionality Reduction: common questions
Can I learn Dimensionality Reduction in Hindi?
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Is this Dimensionality Reduction tutorial for beginners?
It is lesson 57 of 150 in the free Machine Learning roadmap, in stage 14, What is Machine Learning?. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.