Preparing and evaluating a model is as essential as tuning one. Working with different ML frameworks/libraries that provide us with the standard set of algorithms, we hardly ever use them straight out of the box.

ML algorithms have different parameters or knobs, which can be tuned based on the project requirements and different evaluation results. Model tuning works by iterating over different settings of hyperparameters or metaparameters to achieve better results. Hyperparameters are knobs at a high-level abstraction, which are set before the learning process begins.

Grid search, randomized hyperparameter search, Bayesian optimization, and so on are some of the popular ways of performing model tuning. Though model tuning is very important, overdoing it might impact the learning process adversely. Some of the issues related to overdoing the tuning process were discussed in the section bias-variance trade-off.