Principal Components Analysis ( PCA ) is an algorithm to transform the columns of a dataset into a new set of features called Principal Components. By doing this, a large chunk of the information across the full dataset is effectively compressed in fewer feature columns. This enables dimensionality reduction and ability to visualize the separation of classes … Principal Component Analysis – How PCA algorithms works, the concept, math and implementation Read More » Given the data set below, figure out the which linear combinations matter the most out of these independent variables via Principle Component Analysis ( PCA ). Use PCA to reduce the given... Principal Component Analysis ( PCA ) stands as one of the most powerful techniques for tackling the curse of dimensionality in machine learning . Imagine trying to visualize a dataset with 100 features—it’s impossible for human minds to comprehend 100-dimensional space. PCA elegantly solves this problem by finding a way to represent your high-dimensional data in fewer dimensions while retaining most of the important information. It’s like taking a 3D object and finding the best angle to ...
