Abstract
Machine Learning has got the popularity in recent times. Apart from machine learning the decision tree is one of the most sought out algorithms to classify or predict future instances with already trained data set. Random Forest is an extended version of decision tree which can predict the future instances with multiple classifiers rather than single classifier to reach accuracy and correctness of the prediction. The performances of the Random Forest model is reconnoitered and vary with other models of classification which yield institutionalization, regularization, connection, high penchant change and highlight choice on the learning models. We incorporate principled projection strategies which are aiding to predict the future values. Ensemble techniques are machine learning techniques where more than one learners are constructed for given task. The ultimate aim of ensemble methods is to find high accuracy with greater performance. Ensembles are taking a different approach than single classifier to highlight the data. In this, more than one ensemble is constructed and all individual learners are combined based on some voting strategy. In the current study, we have outlined the concept of Random forest ensembles in classification.
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Shaik, A.B., Srinivasan, S. (2019). A Brief Survey on Random Forest Ensembles in Classification Model. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-13-2354-6_27
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DOI: https://doi.org/10.1007/978-981-13-2354-6_27
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