What does dimensionality reduction aim to achieve?

Dimensionality reduction is a process used in data science and machine learning that aims to simplify data sets by reducing the number of features or dimensions while retaining the essential structure and information within the data. This is particularly important when dealing with high-dimensional data, where the presence of too many features can lead to challenges such as overfitting, increased computational costs, and difficulties in visualizing and interpreting the data.

By preserving the essential structure with fewer features, dimensionality reduction techniques, such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE), allow data scientists to focus on the most significant variables without losing critical information. This helps in improving the performance of machine learning models and can also assist in uncovering patterns or trends that may be obscured in a higher-dimensional space. Thus, the primary goal of dimensionality reduction aligns perfectly with the choice that highlights the preservation of essential structure while simplifying the dataset.