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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 1691–1700 of 2050 papers

TitleStatusHype
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Classification with Scattering Operators—0
Classification with Sparse Overlapping Groups—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clipper: A Low-Latency Online Prediction Serving System—0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
Clustering Discrete-Valued Time Series—0
Clustering evolving data using kernel-based methods—0
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