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Towards ML Models' Recommendations
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AI
DOI：10.1007/s41019-024-00262-x.png)
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摘要
En 中文
Artificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.
Keyword:
Machine learning model
Supervised learning
Ontology
User input
Similarities criteria
ML models and user inputs alignment
ML models retrieval
期刊
D
IF:
4.6
论文数:
250
被引数:
665
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引用论文
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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation在回归分析评估中，决定系数R平方比SMAPE，MAE，MAPE，MSE和RMSE更具信息性
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