@article{MAKHILLJEAS2017122114980,
    title = {Implementation of Random Forest Machine Learning Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {21},
    pages = {5603-5608},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5603.5608},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5603.5608},
    author = {R.,Rajath,R.,Syed and},
    keywords = {Machine learning,ensemble,random forest,variables,algorithm,prediction},
    abstract = {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.}
    }