Validate model optimization

When implementing AI model optimization techniques such as early stopping algorithms or hyperparameter tuning, include proper validation mechanisms to help users effectively reduce model overfitting and improve accuracy.

Validation should include:

1. Pre-execution checks on algorithm settings and configurations
2. Meaningful error messages for incorrect parameter ranges or incompatible settings
3. Documentation that explains the impact of each optimization technique

For example, when implementing early stopping validation for Katib experiments:

# Example validation for early stopping settings
algorithm:
  earlyStoppingSettings:
    # Validate these values with appropriate ranges and types
    evaluationInterval: 1  # Validate this is a positive integer
    threshold: 0.01  # Validate this is a positive float
    comparisonType: "smaller"  # Validate this is one of ["smaller", "larger"]

This approach helps prevent common errors in machine learning workflows, reduces debugging time, and improves model quality by ensuring optimization techniques are correctly applied.