Many models already mentioned in this chapter have multiple parameters that determine how the model will perform. Selecting some is relatively straightforward, but there are many that we simply cannot set intuitively. That's where hyperparameters-tuning comes to play. The hyperparameters-tuning methods help us select the best (or close to) set of parameters that maximizes some metric we defined.

In this recipe, we will show you two approaches for hyperparameter-tuning.