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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 641–650 of 2050 papers

TitleStatusHype
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Precision-Recall-Gain Curves: PR Analysis Done RightCode0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Data-Driven Online Model Selection With Regret Guarantees—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
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