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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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TitleStatusHype
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Interpretability in Linear Brain Decoding—0
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Factors in Fashion: Factor Analysis towards the Mode—0
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