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Glossary
Model Tuning
AI DEFINITION

Model Tuning

In machine learning, raw algorithms rarely deliver their best performance out of the box. Model tuning is the craft of adjusting hyperparameters—the knobs and levers that govern how a model learns. Unlike parameters (weights learned during training), hyperparameters are set beforehand and must be carefully chosen.

Consider a neural network: its learning rate determines how quickly it adapts to data; its batch size affects convergence stability; the number of layers shapes its capacity to capture complexity. A poor choice can lead to underfitting (the model is too simple) or overfitting (the model memorizes training data instead of generalizing).

Tuning strategies have evolved:

  • Grid search provides brute-force thoroughness but is computationally expensive.
  • Random search surprisingly often outperforms grid search in efficiency.
  • Bayesian optimization uses probabilistic models to guide the search toward promising regions.
  • More recent techniques like AutoML frameworks now automate large parts of the process, democratizing high-quality model development.

Ultimately, tuning is both science and art: it requires a blend of experimentation, domain knowledge, and computational resources. In practice, companies rarely skip it—Google’s AutoML, for instance, relies heavily on systematic hyperparameter optimization to produce production-ready models.

🔗 References:

  • Snoek et al., Practical Bayesian Optimization of Machine Learning Algorithms (NIPS 2012)