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What is K-Fold Cross Validation

Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
K-fold cross validation is a type of model validation. K-fold cross validation first partition dataset into k sections evenly. Each fold is considered as holdout section once and rest is used for model construction. After all holdout section are predicted, actual and predicted dependent quantities are compared for validation.
Published in Chapter:
Generalized Linear Model for Automobile Fatality Rate Prediction in R
Gao Niu (Bryant University, USA) and Alan Olinsky (Bryant University, USA)
DOI: 10.4018/978-1-7998-2768-9.ch005
This chapter demonstrates the descriptive and statistical modeling function in R. The automobile fatal accident data of the United States is extracted from the Fatality Analysis Reporting System (FARS). The model will be used to understand significant contributing factors of automobile accident death when a fatal crash happens. First, descriptive analysis is performed by basic R functions and packages. Then, generalized linear model (GLM) with logit link function is explored and constructed. Finally, multiple validation metrics are introduced and calculated to ensure the reasonability and accuracy of the predictions. The focus of this chapter is to demonstrate the power and flexibility of the most popular Open Source Statistical Software (OSSS) through a real data analysis.
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Restaurant Sales Prediction Using Machine Learning
Refers to a method of splitting data into K folds to provide an effective implementation of data and provide a unique pattern that is easily understandable.
Full Text Chapter Download: US $37.50 Add to Cart
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