Random Search Flashcards
Random Search
Random Search is an approach to hyperparameter tuning that involves randomly selecting values for each hyperparameter, evaluating them, and then choosing the best one. In summary, Random Search can be an efficient way to perform hyperparameter tuning, particularly when the number of hyperparameters or their possible values are large. Although it may not always find the absolute best set of hyperparameters, it often finds a good set with much less computational effort than Grid Search.
- Definition
Random Search is a method of hyperparameter tuning in which random combinations of hyperparameters are used to find the best solution for the built model. It is an alternative to the Grid Search method.
- Process
Unlike Grid Search, which tries out every single combination of hyperparameters, Random Search only tries out a random subset of combinations. For each iteration, hyperparameters are randomly sampled from the predefined range of values and used to train a model.
- Model Training
As with Grid Search, each set of hyperparameters defines a unique model that is trained independently of the others.
- Model Evaluation
Each model’s performance is evaluated on a validation set, or using cross-validation. The performance is typically evaluated using a problem-specific metric, like accuracy for classification problems or mean squared error for regression problems.
- Selection of Best Parameters
The hyperparameters of the model that performed best on the validation data are selected.
- Advantages
Random Search can be less computationally expensive than Grid Search because it does not attempt to try all possible combinations of hyperparameters, which can make it a better choice when dealing with a large number of hyperparameters or when time/resources are limited.
- Drawbacks
A potential downside of Random Search is that it may not find the absolute best hyperparameters, as it doesn’t try all combinations. However, in practice, it often finds a set of hyperparameters that is close to the best.
- Parallelization
Similar to Grid Search, the process of Random Search can also be parallelized, reducing the computational time.
- Usage
Random Search can be used across a variety of machine learning algorithms, like Neural Networks, Support Vector Machines, and ensemble methods, to find near-optimal hyperparameters.
