Que es el Analisis de Componentes Principales (PCA) #shorts
Analysis of Principal Components (PCA)
Introduction to PCA
- The analysis of principal components, also known as PCA (Principal Component Analysis), is a technique used for dimensionality reduction in datasets.
- PCA creates new variables called components that are orthogonal to each other and capture the variance present in the original variables.
Visualization of Data
- In a three-dimensional dataset, PCA projects data onto a two-dimensional plane formed by the first two principal components, which are perpendicular to each other.
- A metaphor describes this process as placing a mirror among the data to maximize separation between individuals, thereby capturing more information in two dimensions.
Benefits of Dimensionality Reduction
- This technique aids in visualizing high-dimensional data using only one, two, or three dimensions.