 A methodical evaluation of classifiers in predicting academic performance for a multi-class approachA. Princy Christy and N. Rama International Journal of Data Analysis Techniques and Strategies, 2021, vol. 13, issue 3, 207-226 Abstract: Predictive analytics has gained importance in recent years as it helps to proactively identify factors that contribute to the success or failure of an event in the relevant field. Academic achievements of students can be predicted early by employing algorithms and analysing relevant data thereby devising solutions to improve performance. In this process choosing the right algorithm is very crucial since performance of algorithms vary depending on the distribution of data and the way it is tuned to handle the data. In order to enhance the performance of algorithms their hyper-parameters were tuned. Many multi-class classifiers were examined and the prediction accuracy of each model developed by employing them was compared. Depending on their classification accuracy the models developed were used to predict the performance of the students. This was done by using micro and macro averaging because of multi-class features. The results show that ensemble classifiers performed well than their individual counterparts. Keywords: multi-class; classification; prediction; performance metrics; XGBoost; random forest classifier; feature importance; grid search; macro-average; micro-average. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:13:y:2021:i:3:p:207-226 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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