Automated Hyperparameter Search Space Definition
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Solution Overview
Problem
Defining a suitable search space for hyperparameter optimization in machine learning is challenging, as it requires expertise and is not automated, leading to inefficiencies in finding optimal hyperparameter values for machine learning models.
Innovation Solution
An automated method is introduced to determine optimal hyperparameter ranges by leveraging historical data from previous Bayesian optimization runs, using techniques like transfer learning to adjust the search space based on user-defined parameters, allowing for the selection of optimal hyperparameter values without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual definition of search space is used, then expertise and control are improved, but automation and efficiency deteriorate
Solution Approach 1:
The system performs self-service by automatically defining search spaces using historical data from previous optimization runs. The automated search space definition leverages transfer learning to extract useful information from past experiments, eliminating the need for manual expert intervention while maintaining optimization effectiveness.
Solution Approach 2:
The system performs preliminary action by pre-defining search spaces using historical data before the current optimization task begins. By leveraging transfer learning from previous runs, the system prepares optimized search space parameters in advance, improving efficiency without requiring manual tuning for each new task.
2Productivity
If automated search space definition is implemented, then efficiency and productivity are improved, but measurement precision and accuracy deteriorate
Solution Approach 1:
The system uses feedback from historical optimization runs to iteratively improve search space definitions. By analyzing past performance data and using transfer learning, the system continuously refines its automated search space definition, achieving both high productivity and accurate precision through feedback-driven improvement.
Data Source
AI summary
Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.