Classification Trees vs. Logistic Regression in the Generic Skill Development in Engineering

2018 ◽  
Vol 22 (4) ◽  
Author(s):  
Jorge Pérez Rave ◽  
Favián González Echavarría
Keyword(s):  
Logistic Regression ◽  
Skill Development ◽  
Classification Trees ◽  
Generic Skill

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

scholarly journals On generic skill development: An engineering perspective

2008 ◽  
Vol 18 (3) ◽  
pp. 355-363 ◽  
Author(s):  
Khoa N. Le ◽  
Vivian W.Y. Tam
Keyword(s):  
Skill Development ◽  
Generic Skill

Application of classification trees and logistic regression to determine factors responsible for lamb mortality

2012 ◽  
Vol 103 (2-3) ◽  
pp. 225-231 ◽  
Author(s):  
Dariusz Piwczyński ◽  
Beata Sitkowska ◽  
Ewa Wiśniewska
Keyword(s):  
Logistic Regression ◽  
Classification Trees

Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions

2013 ◽  
Vol 22 (4) ◽  
pp. 459 ◽  
Author(s):  
Matt P. Plucinski
Keyword(s):  
Logistic Regression ◽  
Response Times ◽  
Classification Trees ◽  
Predictor Variables ◽  
Initial Attack ◽  
Small Areas ◽  
Long Duration ◽  
Fire Area ◽  
Weather Parameters ◽  
Operational Data

Most grassfires that occur in southern Australia are contained to small areas by local suppression resources. Those that are not require extra resources from neighbouring districts. Identifying these fires at the start of initial attack can prompt early resource requests so that resources arrive earlier when they can more effectively assist with containment. This study uses operational data collected from Australian grassfires that used ground tankers and aircraft for suppression. Variables were limited to those available when the first situation report is provided to incident controllers and included weather parameters, resource response times, slope, curing state, pasture condition and estimated fire area at initial attack. Logistic regression and classification trees were used to identify grassfires likely to escape initial attack by (a) becoming large (final area ≥100 ha), (b) being of long duration (containment time ≥4 h) or (c) either or both of these. These fires would benefit from having more resources deployed to them than are normally available. The best models used initial fire area and Grassland Fire Danger Index as predictor variables. Preliminary operational decision guides developed from classification trees could be used by fire managers to make quick assessments of the need for extra resources at early stages of a fire.

Landscape Influences on Stream Habitats and Biological Assemblages

2006 ◽  
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Fish Species ◽  
Brook Trout ◽  
Gap Analysis ◽  
Classification Trees ◽  
Training Data ◽  
Distribution Maps ◽  
Resource Managers ◽  
Natural Resource Managers

<em>Abstract.</em>—As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout <em>Salvelinus fontinalis </em>presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Predictive performance of missing data methods for logistic regression, classification trees and neural networks

2001 ◽  
Vol 71 (2) ◽  
pp. 115-140 ◽  
Author(s):  
H.Schmid. Christopher ◽  
Norma. Terrin ◽  
John L. Griffith ◽  
Ralph B. D’agostino ◽  
Harry P:. Selker
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Missing Data ◽  
Classification Trees ◽  
Predictive Performance
Sign in / Sign up

Export Citation Format

Share Document