What does dimensionality reduction aim to achieve in a dataset?

Dimensionality reduction is a process in data analysis and machine learning aimed at minimizing the number of features or dimensions in a dataset while retaining as much relevant information as possible. This is particularly useful in scenarios where a dataset has a large number of features, which can lead to issues such as overfitting, computational inefficiency, and difficulties in visualization.

By reducing the number of dimensions, dimensionality reduction techniques help simplify the model, making it easier to interpret and faster to train. Moreover, keeping significant information helps in preserving the underlying structure and patterns of the data, which is crucial for achieving optimal performance in machine learning tasks. Techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for this purpose.

The other options do not accurately describe the goal of dimensionality reduction. Increasing the number of features contradicts the principle, while eliminating relevant data or transforming data without changing its dimensions diverges from the main objective of preserving important information during the reduction process.