Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

Awujoola, Olalekan J.; Odion, Philip O.; Irhebhude, Martins E.; Aminu, Halima

doi:10.37231/myjas.2021.6.1.276

Cited by 13 publications

(9 citation statements)

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“…The supervised and unsupervised machine learning classifiers were considered, including the Naive Bayes, logistic regression, and SVM. The results reveal that random forest and the decision tree perform well, and logistic regression is better than other methods, with an accuracy of 93% [8]. Gupta et al used the quantitative model of business dynamics and the machine learning algorithm in counterfactual analysis to study the effect of long-term trends or external shocks on total employment growth.…”

Section: Recent Related Workmentioning

confidence: 99%

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

2022

Wireless Communications and Mobile Computing

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This exploration aims to investigate the employment and entrepreneurship of college students. College students majoring in information and computing sciences in Xinxiang City are taken as the research object. A machine learning model for college students’ employment and entrepreneurship is established. Experiments are conducted using Python language and a machine learning framework. First, based on the employment and entrepreneurship indexes of college students in machine learning, the initial data of college students’ training quality evaluation are obtained from the educational administration system, library management system, college evaluation materials, and questionnaire survey. Then, the combination weighting method is adopted to determine the index weight, quantify the data, and modify the parameters of the data provided by the framework. The Gaussian kernel is selected as the kernel function, and the sample data used by the machine learning model are labeled. Finally, the sample data are employed to train and test the model. After the consistency test, the model reaches the optimal value after 7 iterations, with an error rate of 0.01 and an accuracy rate of 99%. The final error rate of entrepreneurship and innovation model based on machine learning is less than 0.1, which is consistent with the actual situation. The model can meet the requirements of college students’ entrepreneurship and employment evaluation. It proves that it can be applied to the research of college students’ employment and entrepreneurship and has certain theoretical guidance and practical significance for the evaluation of college students’ employment and entrepreneurship level.

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Section: Recent Related Workmentioning

confidence: 99%

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

2022

Wireless Communications and Mobile Computing

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“…Furthermore, the research evaluates the performance of these models in accurately categorizing qualitative responses, contributing to the advancement of machine learning techniques in educational research. This assessment ensures the reliability and effectiveness of the machine learning models [7] employed in the study. The primary research contribution lies in developing and applying advanced machine learning methods to analyze qualitative data in educational contexts [8], providing a novel approach to understanding the effects of educational policies like the UAQTE program.…”

Section: Introductionmentioning

confidence: 94%

Beyond BERT: Exploring the Efficacy of RoBERTa and ALBERT in Supervised Multiclass Text Classification

Sy,

Maceda,

Canon

et al. 2024

IJACSA

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This study investigates the performance of transformer-based machine learning models, specifically BERT, RoBERTa, and ALBERT, in multiclass text classification within the context of the Universal Access to Quality Tertiary Education (UAQTE) program. The aim is to systematically categorize and analyze qualitative responses to uncover domain-specific patterns in students' experiences. Through rigorous evaluation of various hyperparameter configurations, consistent enhancements in model performance are observed with smaller batch sizes and increased epochs, while optimal learning rates further boost accuracy. However, achieving an optimal balance between sequence length and model efficacy presents nuanced challenges, with instances of overfitting emerging after a certain number of epochs. Notably, the findings underscore the effectiveness of the UAQTE program in addressing student needs, particularly evident in categories such as "Family Support" and "Financial Support," with RoBERTa emerging as a standout choice due to its stable performance during training. Future research should focus on fine-tuning hyperparameter values and adopting continuous monitoring mechanisms to reduce overfitting. Furthermore, ongoing review and modification of educational efforts, informed by evidence-based decision-making and stakeholder feedback, is critical to fulfill students' changing needs effectively.

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“…The two major ML techniques for addressing complex problems are supervised and unsupervised learning. Unsupervised learning entails identifying similarities between data points and their characteristics to create groups without prior knowledge of the final output classes and sets [30]. In contrast, supervised learning involves training an algorithm on a labeled dataset to predict new data.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach

Imianvan,

Robinson,

Asuquo

et al. 2024

JAMCS

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Personnel recruitment processes in various government agencies, ministries, boards, and parastatals encounter challenges in effectively selecting candidates who meet specified requirements for job placement on time. Moreover, human resource (HR) managers face the additional burden of appeasing top government officials while also mitigating issues of nepotism and bias during recruitment. The success or failure of any organization heavily relies on the recruitment and retention of its workforce. Consequently, the decision to select suitable candidates for job positions is of utmost importance to management in every organization. This work develops a structured intelligent system that selects the best machine learning (ML) classification model for predicting applicants’ employability based on their attributes using the industry job selection criteria. A dataset of 16240 applicants’ records collected from Akwa State Universal Basic Education Board (AKSUBEB) was used to train and test the performance of the ML models. Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Decision Tree (DT) classifiers were deployed where results indicate that DT emerged the most effective classifier with a 98% prediction accuracy followed by RF with accuracy of 97.59% while LR recorded the least accuracy of 79.43%. This outcome indicates that tree-based ML structures can significantly help HR personnel to efficiently select suitable candidates for given job positions with reduced overhead in the recruitment process.

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Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

Cited by 13 publications

References 0 publications

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

Beyond BERT: Exploring the Efficacy of RoBERTa and ALBERT in Supervised Multiclass Text Classification

Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach

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