Hands-On Transfer Learning with Python

Model tuning

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.

This is different from model level parameters, which are learned during the training phase. Hence, model tuning is also termed hyperparameter optimization.

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