Balancing Accuracy and Explainability in Machine Learning Have you ever wondered why some Machine Learning applications can make incredibly accurate recommendations but can’t explain why, while others provide clear reasoning but aren’t quite as precise? This fundamental difference stems from one of machine learning’s most important conceptual distinctions: prediction versus inference. In our ongoing exploration […]
In the previous post, we explored the distinction between supervised and unsupervised learning. Today, we’re exploring fundamental mental models for machine learning: understanding classification and regression. Classification and regression in machine learning form the foundation of how supervised learning algorithms make predictions. Understanding when to apply each method is key to developing effective models that […]
Remember learning to ride a bike? Some of us had a parent running alongside, holding the seat and shouting guidance: “Pedal faster! Look ahead! Balance!” Others might have figured it out through trial and error – falling, getting back up, and gradually finding that magic balance point with no one giving instructions. These two approaches […]