Hyperparameter Sweep

Systematically search for optimal hyperparameters.

Step 1: Define Search Space

Common spaces: learning_rate (log_uniform 1e-5 to 1e-2), batch_size (categorical), weight_decay (log_uniform), warmup_ratio, dropout

Step 2: Choose Search Strategy

• •Grid search: exhaustive, for 2-3 params with few values
• •Random search: recommended default, 20-50 runs usually sufficient
• •Bayesian: for expensive runs with smooth objective

Step 3: Set Budget

Max runs, max total time, early stopping configuration

Step 4: Execute Sweep

Track progress, report best so far, apply early stopping to bad runs

Step 5: Analyze Results

• •Best configuration with metrics
• •Parameter importance visualization
• •Sensitivity analysis (high/medium/low for each param)
• •Recommendations for next steps