Grid Search

Grid search is a hyperparameter tuning technique used to find the optimal combination of hyperparameters for a machine learning model. It involves creating a grid of all possible combinations of hyperparameters and evaluating the model performance for each combination. The hyperparameters with the best performance are then selected as the optimal hyperparameters for the model. Grid search is a computationally expensive technique, as it requires training and evaluating the model for every combination of hyperparameters in the grid. However, it is a widely used technique in data science and machine learning, as it can help improve the performance of a model significantly.
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