What is dimension reduction in Machine Learning?

Dimensionality reduction in machine learning is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can be achieved by transforming the data into a lower-dimensional space while preserving most of the relevant information. Dimensionality reduction techniques are commonly used to address the curse of dimensionality, improve computational efficiency, alleviate the risk of overfitting, and enhance visualization of high-dimensional data. Some popular methods for dimensionality reduction include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA). These techniques aim to capture the underlying structure or patterns in the data while reducing its complexity, thus facilitating more efficient and effective machine learning algorithms.