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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 1651–1660 of 2050 papers

TitleStatusHype
Convex Covariate Clustering for ClassificationCode0
Automatic Gradient BoostingCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Minimum discrepancy principle strategy for choosing k in k-NN regressionCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Catastrophic forgetting: still a problem for DNNsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
We Need to Talk About train-dev-test SplitsCode0
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