How would you tune the hyperparameters for a Random Forest model for a given classification problem, and what factors would you consider?
- Focus only on the number of trees
- Grid Search considering the number of trees, depth, and other hyperparameters
- Manual selection without considering the problem
- Random selection
Tuning the hyperparameters for a Random Forest model can be effectively done using Grid Search. Considering factors such as the number of trees, depth, minimum samples split, and others allows for a comprehensive search through the hyperparameter space to find the optimal configuration tailored to the specific classification problem.
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