Hadoop
Machine Learning
A hyperparameter optimization technique that systematically works through multiple combinations of parameter settings.
Detailed Explanation
Grid Search is a systematic hyperparameter optimization technique in machine learning that exhaustively explores a specified subset of hyperparameters by training and evaluating models on all possible combinations. This approach helps identify the optimal parameter set for a given algorithm, improving model performance while ensuring thoroughness despite potential computational costs.
Use Cases
- •Optimizing a random forest's parameters to improve predictive accuracy on a new dataset using exhaustive hyperparameter search.
Related Terms
Other terms in the Machine Learning category