This means it will take a lot of time to perform the entire search which can get very computationally expensive. Grid search works by trying every possible combination of parameters you want to try in your model.

This is a widely used and traditional method that performs hyperparameter tuning to determine the optimal values for a given model. How to optimize hyperparameters Grid Search There are some common strategies for optimizing hyperparameters. A good choice of hyperparameters can really make an algorithm shine. This is why you need to optimize them in order to get the right combination that will give you the best performance. But the default values do not always perform well on different types of Machine Learning projects. Most of these machine learning algorithms come with the default values of their hyperparameters. Therefore Hyperparameter optimization is considered the trickiest part of building machine learning models. This process plays a vital role in the prediction accuracy of a machine learning algorithm. So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time. These parameters are tunable and can directly affect how well a model trains. In short, hyperparameters are different parameter values that are used to control the learning process and have a significant effect on the performance of machine learning models.Īn example of hyperparameters in the Random Forest algorithm is the number of estimators ( n_estimators), maximum depth ( max_depth), and criterion. What is hyperparameter optimization?īefore I define hyperparameter optimization, you need to understand what a hyperparameter is. This task always comes after the model selection process where you choose the model that is performing better than other models.

One of the steps you have to perform is hyperparameter optimization on your selected model.

When working on a machine learning project, you need to follow a series of steps until you reach your goal.

Nonmem weighted optimization