What is dimensionality reduction? and What's its use
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What is dimensionality reduction? and What's its use
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Vishal GamiPosted Dec 2, 2025, 12:16 PM
Dimensionality reduction is a technique in data science and machine learning used to reduce the number of random variables (features) under consideration, by obtaining a set of principal variables. In simpler terms, it’s a way to compress data while preserving its essential information.
Cynthia SathuragiriPosted Dec 2, 2025, 9:12 AM
Dimensionality reduction means reducing the number of features in your data while keeping the important information.
Imagine you have a dataset of people with 100 features (height, weight, age, income, hobbies, etc.).
But your model only needs 5–10 important features to make good predictions.
Dimensionality reduction helps you automatically find and keep those important features and remove the unnecessary ones.
In short: It makes the data smaller, simpler, and easier for a model to understand.