TY  - JOUR
T1  - Implementation of Random Forest Machine Learning Algorithm
AU - Roshen Sarma, R. AU - R Joshi, Rajath AU - Prashanth, R. AU - Wajahath, Syed AU - Chidaravalli, Sharmila 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 21
SP  - 5603
EP  - 5608
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.5603.5608
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.5603.5608
KW  - Machine learning
KW  -ensemble
KW  -random forest
KW  -variables
KW  -algorithm
KW  -prediction
AB  - This is aimed to implement Random Forest (RF) classification machine learning algorithm performance
and investigate its properties. Implementation and all experiments are done in R environment using the Kaggle
Dataset-Titanic: machine learning from disaster. Variable importance is estimated for the dataset using this
method. Finally, variable selection using importance ranks influence on RF classification rates is analyzed.
ER  -