Understanding Model Selection through Bias-Variance Trade-off
I came across an insightful video on YouTube that discusses model selection in the context of machine learning, particularly focusing on the bias-variance trade-off. You can view the video here. The crux of model evaluation lies in understanding how to balance model complexity and prediction accuracy. Simpler models like linear regression can be easier to interpret, but they often struggle in scenarios requiring flexibility. Conversely, more complex models, such as nearest-neighbor averaging or thin plate splines, can lead to overfitting if not assessed correctly.