What is the primary goal of dimensionality reduction?

Dimensionality reduction focuses on simplifying models by reducing the number of input variables or features while retaining the essential information in the dataset. This process helps to reduce noise and prevent overfitting, which can improve model performance, especially when dealing with high-dimensional data. By condensing the dataset into fewer dimensions, dimensionality reduction can make it easier to visualize the data, identify underlying patterns, and improve the efficiency of algorithms, leading to faster training times and reduced computational costs.

While reducing computational costs is indeed a benefit of dimensionality reduction, the primary goal is more about simplifying models and enhancing their performance by focusing on the most relevant features. The practice does not aim to increase complexity or add more features, as doing so would counteract its foundational purpose.