Prediction Accuracy vs Model Interpretability

My take on machine learning based on statistics

The Trade-Off Between Prediction Accuracy and Model Interpretability

Statistical learning methods vary in flexibility - some, like linear regression, can model only simple relationships, while others, like splines, boosting, and neural networks, can capture highly complex patterns.

• Less flexible methods (e.g. linear regression, lasso) are often preferred for inference because they are more interpretable

• More flexible methods (e.g. Generalized Additive Models, boosting, neural networks) can model complex relationships but are harder to interpret

• For prediction-focused tasks, flexibility can help but also risks overfitting, sometimes making less flexible models more accurate.

Choosing the right method involves balancing flexibility, interpretability, and risk of overfitting.