Machine Learning
1. What is PCA, why is it helpful, and how does it work?
PCA stands for principal component analysis, and it is a popular dimensionality reduction technique used in machine learning.
PCA simplifies the data by reducing the number of features or variables in the dataset while retaining as much information as possible.
It’s helpful because it (1) reduces the complexity of the data, making it easier to visualize and analyze, (2) eliminates multicollinearity, which is when two or more features are highly correlated with each other; and can (3) improve the performance of ML algorithms by reducing the number of features and eliminating redundant features.