How do you prevent overfitting when training machine learning models? Essentials

We’ll start by spotting the tell-tale signs of overfitting—great training scores, lousy test scores—then try quick “first-aid” tricks like trimming decision trees and adding early-stopping. Next you’ll add regularization (L1/L2, dropout) and validation checkpoints to keep models honest, and finally boost robustness with cross-validation, simpler features, and a dash more data. By the end you’ll confidently diagnose, treat, and prevent overfitting so your models generalize to the real world.