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What is Forward Selection

Chapter 5
Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
Forward selection is a type of stepwise variable selection which starts with the model with no variable. All variables will be tested and the one variable with the most statistical criteria improvement will be added. The process is repeated until no more variable addition could improve the statistical criteria.
Published in Chapter:
Generalized Linear Model for Automobile Fatality Rate Prediction in R
Gao Niu (Bryant University, USA) and Alan Olinsky (Bryant University, USA)
Copyright: © 2020 |Pages: 25
DOI: 10.4018/978-1-7998-2768-9.ch005
Abstract
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.
Full Text Chapter Download: US $37.50 Add to Cart
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Chapter 3
Medical Image Classification
This feature selection method starts with a empty set of features and at iteration it adds the best features to the resultant set. This is continued in a step by step manner by selecting the best features in every iteration.
Published in Chapter: Medical Image Classification; From: Medical Image Processing for Improved Clinical Diagnosis
Full Text Chapter Download: US $37.50 Add to Cart
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