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What is Model Testing

Chapter 14
Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics
In machine learning, model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. The testing set consisting of a set of testing samples should be separated from the both training and validation sets, but it should follow the same probability distribution as the training set. Each testing sample has a known value of the target. Based on the comparison of the model’s predicted value, and the known target, for each testing sample, the performance of the trained model can be measured. There are a number of statistical metrics that can be used to assess testing results including mean squared errors and receiver operating characteristics curves. The question of which one should be used is largely dependent on the type of models and the type of application. For a regression (Regression Analysis) model, the standard error of estimate is widely used.
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 43
A Cross Sample Analysis: To Examine the Predictive Validity of an Instrument
Using statistics procedures to examine and confirm a model, usually an initial model is developed first, and then tested and examined.
Published in Chapter: A Cross Sample Analysis: To Examine the Predictive Validity of an Instrument; From: Handbook of Research on E-Learning Applications for Career and Technical Education: Technologies for Vocational Training
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Chapter 4
Automatic Syllabus Classification Using Support Vector Machines
A procedure performed after model training that applies the trained model to a different data set with known classes and evaluates the performance of the trained model.
Published in Chapter: Automatic Syllabus Classification Using Support Vector Machines; From: Handbook of Research on Text and Web Mining Technologies
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Chapter 11
Intelligent System for Predicting Healthcare Readmissions
In machine learning, model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. The testing set consisting of a set of testing samples should be separated from the both training and validation sets, but it should follow the same probability distribution as the training set. Each testing sample has a known value of the target. Based on the comparison of the model’s predicted value, and the known target, for each testing sample, the performance of the trained model can be measured. There are a number of statistical metrics that can be used to assess testing results including mean squared errors and receiver operating characteristics curves. The question of which one should be used is largely dependent on the type of models and the type of application. For a regression (Regression Analysis) model, the standard error of estimate is widely used.
Published in Chapter: Intelligent System for Predicting Healthcare Readmissions; From: Analyzing Current Digital Healthcare Trends Using Social Networks
Full Text Chapter Download: US $37.50 Add to Cart
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