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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 1–10 of 2050 papers

TitleStatusHype
Topic Modeling and Link-Prediction for Material Property Discovery—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
The use of cross validation in the analysis of designed experimentsCode0
Leveraging Predictive Equivalence in Decision TreesCode0
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives—0
The Sample Complexity of Parameter-Free Stochastic Convex Optimization—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
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