Comparing LDA with Other classification

Comparing LDA with Other classification

Have you ever considered how we train machine learning models that could mirror our biases? As a seasoned data analyst, I frequently confront this problem. We desire objective systems yet inadvertently impose our preconceived notions of what patterns or 'ideal' data should look like. It highlights the strengths and limitations of techniques like Linear Discriminant Analysis (LDA). LDA excels in classifying well-defined groups, but what if our chosen features perpetuate hidden bias? Take image analysis: Algorithms focused on standardized visual norms could perpetuate existing inequalities or fail to uncover nuanced insights. Could different data selection, pre-processing, and alternative classification models lead to more equitable, accurate results? Table of Contents In this article, we explore LDA in the context of these intriguing questions. Rather than offering a simple tutorial, I'll critically examine its capabilities and how it differs from other classification methods.…