Electron microscopy
  
Comparison among Grid Search, Bayesian Optimization,
Random Search and Manual Search
- Python Automation and Machine Learning for ICs -
- An Online Book -
Python Automation and Machine Learning for ICs                                                           http://www.globalsino.com/ICs/        


Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

Table 3749. Comparison among Grid Search, Bayesian Optimization, Random Search and Manual Search.

  Grid Search Bayesian Optimization Random Search Manual Search
Definition It systematically searches through a predefined set of hyperparameter values. It models the objective function and uses a probabilistic surrogate model to guide the search for the optimal hyperparameters. It randomly samples hyperparameter combinations from the predefined search space. Hyperparameters are manually selected based on domain knowledge or trial and error.
Advantages          Simple and easy to understand.
         Exhaustive search over the entire hyperparameter space.		          Efficient in terms of computational resources, especially in high-dimensional spaces.
         Adapts and focuses on promising regions of the hyperparameter space.		          Less computationally expensive compared to Grid Search.
         Can be more effective than Grid Search in high-dimensional spaces.		          Allows for the incorporation of human expertise.
         Quick and straightforward for simple models.
Disadvantages          Computationally expensive, especially in high-dimensional spaces.
         May not be efficient in terms of time and resources.		          Requires careful tuning of its own parameters.
         May not perform well when the objective function is not smooth or has discontinuities.		          It may not efficiently explore the hyperparameter space and might miss important regions.          Prone to bias and may not explore the entire hyperparameter space.
         Time-consuming and not scalable for complex models or high-dimensional spaces.
Computational Cost Grid Search is the most computationally expensive, followed by Bayesian Optimization, Random Search, and Manual Search.
Exploration Efficiency Bayesian Optimization is generally more efficient in exploring promising regions compared to Grid Search and Random Search.
Ease of Use Grid Search and Random Search are relatively straightforward, while Bayesian Optimization requires more understanding and tuning.
Expertise Incorporation Manual Search allows direct incorporation of domain expertise, whereas other methods rely on algorithmic search.

 

============================================

         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         

 

 

 

 

 



















































 

 

 

 

 
=================================================================================