Step-by-Step Process of RSM in Machine Learning
Steps Involved in Response Surface Methodology are:
- Define the Objective: Maximize the accuracy of a neural network on a given dataset.
- Select Factors and Levels: Identify hyperparameters such as learning rate, batch size, and the number of hidden layers.
- Design of Experiments (DoE): Use a Central Composite Design (CCD) to explore the parameter space.
- Conduct Experiments: Train the neural network with different combinations of hyperparameters as per the CCD and record the accuracy.
- Fit a Regression Model: Fit a quadratic regression model to the collected data.
- Analyze the Response Surface: Plot the response surface to visualize how changes in hyperparameters affect accuracy.
- Optimization: Use optimization techniques to find the combination of hyperparameters that maximize accuracy.
Optimizing Machine Learning Models Using Response Surface Methodology
Optimizing complex processes and Machine Learning models is a critical task. One powerful technique that has gained prominence for this purpose is Response Surface Methodology (RSM). This article delves into the intricacies of RSM, elucidating its principles, applications, and providing practical examples to illustrate its utility.
Table of Content
- What is Response Surface Methodology (RSM)?
- Why Use RSM in Machine Learning?
- Step-by-Step Process of RSM in Machine Learning
- Implementing Response Surface Methodology
- Hyperparameter Optimization Using Central Composite Design
- Analyze response surface
- Optimization (Gradient Descent â Simplified)
- Use-Cases and Applications for Response Surface Methodology
- Advantages and Limitations of Response Surface Methodology
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