Machine Learning Algorithm Selection via Data Binning
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Solution Overview
Problem
The challenge in selecting a suitable machine learning algorithm for a given dataset is exacerbated by the computational expense of evaluating multiple algorithms, often requiring the use of entire datasets, which is time-consuming and resource-intensive, leading to prohibitive costs and environmental impact.
Innovation Solution
A method involving data binning to select a subset of data entries, allowing for the construction and evaluation of multiple machine learning models using a reduced dataset, thereby reducing computational expenses and environmental footprint while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire dataset is used to evaluate multiple machine learning algorithms, then the accuracy and reliability of model selection is improved, but the computational cost and time consumption increases significantly
Solution Approach 1:
The patent divides the entire dataset into multiple subsets (e.g., through sampling or splitting into training and evaluation sets). Multiple machine learning algorithms are then evaluated on these smaller subsets in parallel, allowing for faster comparison while maintaining selection accuracy. This segmentation approach reduces the computational burden of evaluating all algorithms on the complete dataset.
Solution Approach 2:
The patent performs preliminary evaluation of machine learning algorithms on a subset of the data before final deployment. By conducting initial assessments on reduced datasets, the system can pre-screen algorithms and identify promising candidates without committing full computational resources, thereby reducing overall evaluation time while preserving reliable selection capability.
2Measurement precision
If multiple machine learning algorithms are constructed and evaluated, then the quality of model selection is improved, but the computational expense increases
Solution Approach 1:
The patent implements a two-stage evaluation process where algorithms are first assessed on a smaller subset of data to identify promising candidates, and then only the top-performing algorithms are evaluated on the complete dataset. This partial action approach allows for thorough performance assessment of selected algorithms while avoiding the excessive computational cost of evaluating all algorithms on full data, thus reducing energy consumption while maintaining precise model selection.
3Productivity
If a subset of data entries is used for model evaluation, then the computational cost is reduced, but the accuracy of performance indication may be compromised
Solution Approach 1:
The patent implements a feedback mechanism where the performance of algorithms evaluated on data subsets is monitored and used to guide subsequent evaluation steps. If subset evaluations show high variance or low confidence in algorithm ranking, the system automatically triggers re-evaluation on larger or complete datasets. This feedback loop ensures that computational shortcuts do not compromise the accuracy of model performance indications, maintaining reliability while improving overall evaluation efficiency.
Data Source
AI summary
A method of machine learning algorithm selection may include obtaining a dataset that includes multiple data entries. In some embodiments, each of the data entries may include multiple features and one of the multiple features may be designated as a target variable. The method may further include selecting a subset of the data entries. In some embodiments, selecting the subset of the data entries may include binning the data entries into multiple data bins based on values in the target variable and selecting a subset of the binned data entries from each of the multiple data bins as the subset of the data entries. The method may further include constructing multiple machine learning models using the subset of the data entries and selecting one of the multiple machine learning models based on an evaluation of the multiple machine learning models.