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What is Model Validation

Chapter 14
Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics
It is a phase of machine learning that quantifies the ability of an ML or statistical model to produce predictions or outputs with enough fidelity to be used reliably to achieve business objectives.
Published in Chapter:
Predicting Healthcare Readmissions Using Artificial Intelligence
Manu Banga (GLA University, India)
Copyright: © 2023 |Pages: 23
DOI: 10.4018/978-1-6684-7105-0.ch014
Abstract
Hospital readmission systems increase the efficiency of initial treatment at hospitals. This chapter proposes a novel prediction model for identifying risk factors using machine learning techniques, and the proposed model is tested using 10-fold cross-validation for generalization and finds hidden patterns in the diagnosis, medications, lab test results, and basic characteristics of patients related to readmissions. This model predicts a statistically problem solving using searching patterns. Based on the findings of this study, for the given dataset, pruning dataset manifested the most accurate prediction of readmissions to the hospital with 94.8% accuracy for patients admitted in a year.
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Chapter 18
Computational Tools and Techniques to Predict Aquatic Toxicity of Some Halogenated Pollutants
Model validation is verification of the generated model under a guided frame work to access the performance to a desired level.
Published in Chapter: Computational Tools and Techniques to Predict Aquatic Toxicity of Some Halogenated Pollutants; From: Handbook of Research on the Adverse Effects of Pesticide Pollution in Aquatic Ecosystems
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Chapter 45
System Dynamics to Understand Public Information Technology
This consists of a series of activities to build confidence in a system-dynamics model as a useful policy tool in a specific context.
Published in Chapter: System Dynamics to Understand Public Information Technology; From: Handbook of Research on Public Information Technology
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Chapter 11
Intelligent System for Predicting Healthcare Readmissions
It is a phase of machine learning that quantifies the ability of an ML or statistical model to produce predictions or outputs with enough fidelity to be used reliably to achieve business objectives.
Published in Chapter: Intelligent System for Predicting Healthcare Readmissions; From: Analyzing Current Digital Healthcare Trends Using Social Networks
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Chapter 4
Verification and Validation of Simulation Models
is generally defined corroboration that a computerized model with its area of applicability possesses an acceptable range of accuracy reliable with the planned use of a model and is a definition employed.
Published in Chapter: Verification and Validation of Simulation Models; From: Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications
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Chapter 20
Modelling of Consumer Goods Markets: An Agent-Based Computational Approach
Validation in the context of agent based modeling refers to the process of ensuring the behavior exhibited by a simulated system matches the real target system which it is trying to mimic. This involves a systematic exploration and calibration of model parameters, ensuring robustness of the model, eliminating logical errors in the model code and finally, ensuring that the model replicates the target system with a high degree of accuracy both at the macro level and at the micro level.
Published in Chapter: Modelling of Consumer Goods Markets: An Agent-Based Computational Approach; From: Handbook of Research on Managing and Influencing Consumer Behavior
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Chapter 6
Introduction to Python and Its Statistical Applications
Model validation is a process that used to test whether the output of a statistical model is acceptable using data generating process.
Published in Chapter: Introduction to Python and Its Statistical Applications; From: Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
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Chapter 9
Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
A process of model construction that used to validate the model efficiency. Commonly used process for validation include 50%(Training)/50%(Testing), 75%(Training)/25%(Testing), 80%(Training)/20%(Testing), Even-Year(Training)/Odd-Year(Testing), K-Fold Cross Validation, etc.
Published in Chapter: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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