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ISSN 2063-5346
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LACK OF EFFICIENCY IN ENVISAGING THE USER TRAITS OVER ONLINE SOCIAL MEDIA BASED ON INDIAN METRICS DURING PANDEMIC USING NOVEL DECISION TREE ALGORITHM COMPARING WITH GRADIENT BOOSTED DECISION TREE ALGORITHM

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V. Sai Ram Kumar, Shri Vindhya A
» doi: 10.31838/ecb/2023.12.sa1.487

Abstract

Aim: The primary aim of this research is to increase the intensity percentage of user traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Decision Tree algorithm algorithm and Gradient Boosted Decision Tree algorithm. Materials and Methods: Decision Tree Classifier algorithm with test size=10 and Gradient Boosted Decision Tree algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Decision Tree classifier constructs regression and classification model in the shape of tree. Gradient Boosted Decision Tree is an advanced version of decision tree that improves the performance by weak learners working sequentially . Results and Discussion: Decision Tree algorithm has greater efficiency (87%) when compared to Gradient Boosted Decision Tree efficiency (60%). The results achieved with significance value p=0.448 (p>0.05) shows that two groups are statistically insignificant. Conclusion: Decision Tree algorithm executes remarkably greater than the Gradient Boosted Decision Tree algorithm.

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