Model tuning 

By this phase, we should have a baseline model with which we can go further for tuning the model to make it perform even better. Model tuning corresponds to hyperparameter tuning/optimization.

ML models come with different hyperparameters that cannot be learned from model training. Their values are set by the practitioners. You can compare the hyperparameter values to the knobs of an audio equalizer where we manually adjust the knobs to have the perfect aural experience. We will see how hyperparameter tuning can drastically enhance the performance of a model in later chapters.

There are several techniques for tuning hyperparameters and the most popularly incorporated are the following:

• Grid searching
• Random searching
• Bayesian optimization
• Gradient-based optimization
• Evolutionary optimization