About this document
PCA Explained for Data Science Interviews by ritikapawayyy is a document available to read on EtoBox.
The document outlines key concepts related to linear regression, including assumptions of error such as independence, homoscedasticity, and normality. It discusses the role of p-values in high-dimensional linear regression and the need for adjustments to avoid false positives. Additionally, it covers techniques for encoding high-cardinality categorical variables and explains how Principal Component Analysis (PCA) works for dimensionality reduction.
- Author
- ritikapawayyy
- Language
- EN