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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 871–880 of 2050 papers

TitleStatusHype
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Causal Q-Aggregation for CATE Model Selection—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
Feedback-Controlled Sequential Lasso Screening—0
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