dimenzionalitáscsökkentést

Dimensionality refers to the number of features or attributes present in a dataset. In simpler terms, it's the count of variables used to describe each data point. For example, if you have a dataset of houses with information on their size, number of bedrooms, and location, the dimensionality would be three.

High dimensionality, where a dataset has a very large number of features, can present challenges. This is often referred to as the "curse of dimensionality." As the number of dimensions increases, the data becomes sparser, meaning that the data points are spread out over a much larger space. This sparsity can make it difficult for machine learning algorithms to find meaningful patterns and can lead to increased computational costs and potential overfitting.

Techniques exist to address high dimensionality. Dimensionality reduction aims to reduce the number of features while preserving as much of the important information as possible. Common methods include feature selection, which chooses a subset of the original features, and feature extraction, which creates new, fewer features by combining or transforming the original ones. Principal Component Analysis (PCA) is a popular feature extraction technique that transforms the data into a new set of uncorrelated variables called principal components.