MLE from scratch #2 – Bias, variance, and why training accuracy lies
After building a baseline model, the next failure many teams encounter is not low accuracy, but false confidence. Training metrics look strong. Loss decreases smoothly. Models appear stable. Yet performance degrades in production, generalization fails, and iteration becomes reactive. In many cases, the issue is not the algorithm itself, but a misunderstanding of how model complexity, data, and evaluation interact. This is where the bias–variance framework becomes essential. Not as a theoretical decomposition, but as a practical lens for interpreting error behavior and deciding what kind of change is actually justified. ...