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In this present book Chapter-I is an introductory one. In Chapter - II, a careful observation has been made on relative importance of explanatory variable in multiple regression employing partial standardization technique. In Chapter - III, Method of path analysis has been employed to trace out the variable importance of maternal anthropometric indicators on pregnancy outcome. In Chapter - IV, The study of variable importance has been generated in the light of logistic regression employing the study of second cancer and its risk factors. In Chapter ¿ V, a few machine learning techniques such as Support Vector Machine, k- Nearest Neighbor, Naïve Bayes method, Random Forest and Neural Network have also been applied on the second cancer data. In Chapter ¿ VI, the main conclusions of the present research study have been depicted. It also narrates the plan for the future research as an extension in the lines of the study. Several relevant references have been documented under Bibliography. Under Appendix we provided the complete data used in this research.
| Autorius: | Marugundla Sudarsana Rao, M. Venkataramanaiah, Katari Ashok Chandra, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2015 |
| Knygos puslapių skaičius: | 220 |
| ISBN-10: | 3659794821 |
| ISBN-13: | 9783659794827 |
| Formatas: | 220 x 150 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „On Some Aspects of Variable Importance in Regression Models: Variable Selection by Machine Learning Techniques“
