Question

A data scientist is tuning a scikit-learn GradientBoostingClassifier on a single-node cluster. The search space includes 5 continuous hyperparameters, each with many candidate values, and the team has a strict wall-clock time budget. They want to explore the space efficiently and still have a reasonable chance of finding a strong configuration. Which tuning approach best fits these constraints?

Accepted Answer

Use RandomizedSearchCV with a fixed n_iter to sample a bounded number of configurations from the space

Answer

Use GridSearchCV to exhaustively evaluate every combination of the candidate values

Answer

Manually iterate over each hyperparameter one at a time, holding the others fixed

Answer

Increase the number of cross-validation folds to compensate for fewer configurations tested