Advantages and Disadvantages of Flexibility

 

1. Bias–Variance framing
   • You may mention that flexibility ↔ variance increases and bias decreases.
   • A very flexible model has low bias but high variance; a simpler model has high bias but low variance.
2. Additional advantages of flexibility
   • Capturing complex interactions: can automatically model non–linear patterns or high‐order interactions without hand‐crafting features.
   • Better asymptotic performance: as sample size grows, flexible methods often achieve faster convergence to the true function.
3. Additional disadvantages of flexibility
   • Interpretability: highly flexible models (e.g. deep nets, random forests) are harder to interpret.
   • Computational cost: more parameters or complex fitting routines → longer training times, more memory.
   • Need for careful tuning: hyperparameter selection, regularization, model‐selection overhead.
4. When each is preferred
   • Flexible preferred
     • High signal‐to‐noise ratio and very large sample sizes.
     • When modeling very complex patterns (e.g. image, text, genomics).
   • Less flexible preferred
     • When interpretability and simplicity are paramount (e.g. regulatory settings).
     • When cost of mis‐tuning is high or computational resources are limited.
     • When noise dominates signal (low SNR), simpler models generalize more robustly.