TY  - JOUR
T1  - Predictive Modeling Analysis Impact of Predictor Variables
Towards Dependent Variable
AU - Nengsih, Warnia 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 19
SP  - 4837
EP  - 4840
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.4837.4840
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.4837.4840
KW  - Predictive modelling
KW  -predictor variable
KW  -decision tree
KW  -accuracy
KW  -principles
KW  -learning
AB  - Predictive modeling is one of the concepts to find a pattern or a learning model for the next test data.
One implementation of this modeling is the decision tree concept. Data used in the simulation is vacant land
data. Indicator analysis was conducted to determine patterns or learning models produced from test results
using predictor variables towards dependent variable as seen from variable selection as the root and number
of variables. Thus, it can be obtained a result that number of variables that used affect the pattern or learning
model resulted. Capturing the root to obtain the decision tree does not affect learning model that obtained, so
any variable that is used as a root produces the same learning model. The accuracy of variable selection also
affects the patterns or learning models resulted. The fewer and inaccuracy in choosing the predictor variables
affect the pattern or learning model resulted. Therefore, determination of the used variables must meet the
principles of validity.
ER  -