Скачать или смотреть Implementing Random Search for Hyperparameter Tuning

Random search is a more efficient alternative to grid search. Instead of evaluating every possible combination, random search samples a fixed number of hyperparameter combinations from the defined search space. In MLflow, you can implement random search using libraries like Scikit-learn or Optuna, which integrate with MLflow for logging and tracking. The main advantage of random search is its ability to explore a wider range of hyperparameter configurations with fewer evaluations. This makes it less computationally expensive than grid search while still being effective in finding good hyperparameter settings. An example of random search could involve tuning hyperparameters for a decision tree classifier. By randomly sampling different combinations, you may discover optimal settings that grid search might overlook due to its structured approach. However, random search does not guarantee finding the absolute best combination, especially if the search space is vast. Nonetheless, it offers a balance between exploration and computational efficiency.