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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 391–400 of 2050 papers

TitleStatusHype
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Causal Falling Rule Lists—0
Causal Q-Aggregation for CATE Model Selection—0
Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
A Rule-Based Epidemiological Modelling Framework—0
Binary Matrix Factorization via Dictionary Learning—0
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