Web Effort Estimation Using Classification and Regression Trees

2008 ◽  
pp. 203-239
Author(s):  
Emilia Mendes
Keyword(s):  
Regression Trees ◽  
Classification And Regression Trees ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Real Effort ◽  
Classification And Regression ◽  
Effort Prediction ◽  
Case Based

The use of realistic effort estimates is fundamental to both software and Web project management as they help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. Different effort techniques have been used to obtain effort estimates for Web projects. Two—stepwise regression and case-based reasoning—have already been presented in Chapters V and VI respectively. In this chapter we detail a third technique used to obtain effort estimates for Web projects, known as classification and regression trees (CART), that is considered a machine-learning technique. We detail its use by means of a case study where a real effort prediction model based on data from completed industrial Web projects is constructed step by step.

Web Development Effort Estimation

2011 ◽  
pp. 26-56 ◽  
Author(s):  
Emilia Mendes ◽  
Silvia Abrahão
Keyword(s):  
Multivariate Regression ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Web Development ◽  
Effort Estimation ◽  
Estimation Techniques ◽  
Classification And Regression ◽  
Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

Web Effort Estimation Using Case-Based Reasoning

2008 ◽  
pp. 158-202
Author(s):  
Emilia Mendes
Keyword(s):  
Prediction Accuracy ◽  
Early Stage ◽  
Real Data ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Practitioners ◽  
Case Based

Software practitioners recognise the importance of realistic effort estimates to the successful management of software projects, the Web being no exception. Having realistic estimates at an early stage in a project’s life cycle allow project managers and development organisations to manage resources effectively. Several techniques have been proposed to date to help organisations estimate effort for new projects. One of these is a machine-learning technique called case-based reasoning. This chapter presents a case study that details step by step, using real data from completed industrial Web projects, how to obtain effort estimates using case-based reasoning, and how to assess the prediction accuracy of this technique. The reason to describe the use of case-based reasoning for effort estimation is motivated by its previous use with promising results in Web effort estimation studies.

Web Effort Estimation Usiing Regression Analysis

2008 ◽  
pp. 120-157
Author(s):  
Emilia Mendes
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Statistical Technique ◽  
Project Managers ◽  
Web Development ◽  
Effort Estimation ◽  
Model Based ◽  
Real Effort ◽  
Effort Prediction

Software effort models and estimates help project managers allocate resources, control costs, and schedule and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, this chapter presents a case study where a real effort prediction model based on data from completed industrial Web projects is constructed step by step using a statistical technique called regression analysis.

Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

2021 ◽  
pp. 175045892096263
Author(s):  
Margaret O Lewen ◽  
Jay Berry ◽  
Connor Johnson ◽  
Rachael Grace ◽  
Laurie Glader ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Loss ◽  
Spinal Fusion ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Neuromuscular Scoliosis ◽  
Independent Variables ◽  
Estimated Blood Loss ◽  
Laboratory Results ◽  
Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

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