Fine-tuning machine learning models via grid search
In machine learning, we have two types of parameters: those that are learned from the training data, for example, the weights in logistic regression, and the parameters of a learning algorithm that are optimized separately. The latter are the tuning parameters (or hyperparameters) of a model, for example, the regularization parameter in logistic regression or the maximum depth parameter of a decision tree.
In the previous section, we used validation curves to improve the performance of a model by tuning one of its hyperparameters. In this section, we will take a look at a popular hyperparameter optimization technique called grid search, which can further help to improve the performance of a model by finding the optimal combination of hyperparameter values.
Tuning hyperparameters via grid search
The grid search approach is quite simple: it’s a brute-force exhaustive search paradigm where we specify a list of values...
