What are some real-world applications of dimensionality reduction, and how has it impacted various industries?

Question

Anonymous · Accepted Answer

Feature selection is important in machine learning because it improves model
performance and accuracy by reducing overfitting, leading to faster training
times and enhanced interpretability. By removing irrelevant or redundant
features, the model can focus on the most informative ones, resulting in a
simpler, more robust, and easier-to-understand model that generalizes better to
new, unseen data.

What are some real-world applications of dimensionality reduction, and how has it impacted various industries?

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