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What is Model Evaluation

Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
Model Evaluation is an integral part of the model development process. It helps to find the best model that represents our data and how well the chosen model will work in the future.
Published in Chapter:
Introduction to Data Science
M. Govindarajan (Annamalai University, India)
DOI: 10.4018/978-1-7998-3053-5.ch001
Abstract
This chapter focuses on introduction to the field of data science. Data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. The term data science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data science helps to discover hidden patterns from the raw data. It enables to translate a business problem into a research project and then translate it back into a practical solution. The purpose of this chapter is to provide emphasis on integration and synthesis of concepts, techniques, applications, and tools to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
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The Practice of Structural Equation Modeling
Model evaluation is an employment of various fit measures to examine the fit between data and theoretical model.
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