In a happy situation the sensitivity of the optimzation to hyperparameter changes is low. That's why the 'random' approach provides reasonable results. If the o

In a happy situation the sensitivity of the optimzation to hyperparameter changes is low. That's why the 'random' approach provides reasonable results. If the optimization quality were heavily dependent on the hyperparameter, for an exaggerated example, only providing good results for exactly one value of the hyperparameter, then guessing 60 times and getting within 5% of the best value of the hyperparameter would not guarantee a good model optimization. The main difficulty with hyperparameters is that one often does not actually know a priori a reasonable range to search in. Suppose you have a regularisation constant C - without some calculation based on your data how can you pick that constant? By picking the range of the hyperparameter, the problem is just punted to a hyperhyperparameter. More interesting than blindly guessing values, is measuring the sensitivity of recall, precision and cross validation performance to changes in the hyperparameters. Make sure that the sensitivity is low!