Machine learning and statistics with python

  Certainly! Here's a comprehensive blog post that delves into the concepts of Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), highlighting their differences, applications, and considerations for use. Introduction In the realm of statistical classification, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) are two foundational techniques. Both are grounded in probabilistic models and are particularly effective when the data adheres to certain assumptions. While they share similarities, their differences in assumptions and flexibility make them suitable for different scenarios. Linear Discriminant Analysis (LDA) LDA is a classification method that projects high-dimensional data onto a lower-dimensional space, aiming to maximize class separability. It operates under the assumption that: Each class follows a Gaussian (normal) distribution. All classes share the same covariance matrix. These assumptions lead to...