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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Benchmarking And Practical Evaluation Of Machine And Statistical Learning Methods In Credit Scoring: A Method Selection Perspective, Gwen Verbeck
UNLV Theses, Dissertations, Professional Papers, and Capstones
Predictive models are important tools used in all scientific fields. Machine learning (ML) algorithms and statistical models are widely used for decision-making because of their capability to tackle intricate and unique problems. In domains where data are high-dimensional and contain irrelevant and redundant features, ML algorithms are known to have superior performance over traditional (statistical) learning methods. However, researchers and analysts are often faced with a myriad of techniques to choose from, with no clear consensus on which will perform best for their specific task. Considering resource limitations, exhaustive exploration of all available methods is impractical and often fails to …
Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh
Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh
UNLV Theses, Dissertations, Professional Papers, and Capstones
Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …