Applying Ensemble Approach on U.S. Census Data Classification

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.10, No. 9)

Publication Date: 2021-09-30

Authors : Raj Kumar Pal; Jugal Chaturvedi; V. Sai Teja; Leena Shibu;

Page : 1-11

Keywords : Precision; Recall; Specificity; F1 Score; Accuracy; Ture Positive (TP); True Negative(TN); False Positive(FP); False Negative(FN); Recursive Feature Elimination(RFE); KNN; Random Forest(RF); Ada Boost; Gradient Boost; Extreme Gradient Boost (XGBoost);

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Abstract

During this paper, we have a tendency to examine the adult financial gain dataset obtainable at the UC Irvine Machine Learning Repository. To aim predict whether or not associate individual's financial gain are going to be bigger than $50,000 per annum victimization completely, different boosting and bagging strategies and compare models supported many attributes from the census information.

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Applying Ensemble Approach on U.S. Census Data Classification

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Applying Ensemble Approach on U.S. Census Data Classification﻿

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Applying Ensemble Approach on U.S. Census Data Classification