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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 501–510 of 2050 papers

TitleStatusHype
Nonlinear Causal Discovery for Grouped Data—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Selective machine learning of doubly robust functionals—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Data-driven calibration of linear estimators with minimal penalties—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Data-Driven Online Model Selection With Regret Guarantees—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
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