Carolina Bento writes about Linear Discriminant Analysis (LDA), a supervised learning technique used for dimensionality reduction and pattern recognition. The article explains how LDA maximizes class separability by maximizing the ratio of between-class to within-class variance, making it particularly useful for simplifying high-dimensional datasets while preserving core characteristics. Through a real estate dataset example, the author demonstrates how to implement LDA using ScikitLearn to visualize property type clusters and identify key features that distinguish different types of properties.
- LDA is a supervised method, unlike Principal Component Analysis (PCA), which is unsupervised.
- The maximum number of Linear Discriminants that can be calculated is $K-1$, where $K$ is the number of classes.
- Key assumptions for LDA include linear separability of data and following a Gaussian distribution.
- It helps reduce overfitting by removing redundant features and minimizing noise in high-dimensional spaces.
- LDA is a supervised method, unlike Principal Component Analysis (PCA), which is unsupervised.
- The maximum number of Linear Discriminants that can be calculated is $K-1$, where $K$ is the number of classes.
- Key assumptions for LDA include linear separability of data and following a Gaussian distribution.
- It helps reduce overfitting by removing redundant features and minimizing noise in high-dimensional spaces.
