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Performance evaluation of classification algorithms on academic performance of postgraduate students
Published 2023-02Subjects: “…Educational Data mining…”
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Page will reload when a filter is selected or excluded.- Data Mining 98 results 98
- Technology & Engineering 98 results 98
- Technology & Engineering — Computing 98 results 98
- Algorithm 1 results 1
- Body weight 1 results 1
- Classification and regression tree (CART) is a tree-based data mining algorithm that develops a model to predict an outcome. This study purposed to create a model to predict the body weight (BWT) of Red Sokoto (RS), Sahel (SH), and West African Dwarf (WAD) goats using morphological measurements (such as body length, BL; head girth, HG; head width, HDW; face length, FAL; height at wither, HTW; rump length, RL; shoulder width, SW; rump width, RW; and rump height, RH). In total, 600 goats were used for this study (200 each of RS, SH, and WAD goats). Pearson’s Moment Correlation was used to evaluate the degree of association between BWT and each morphological measurement. Concomitantly, CART analysis was performed to estimate which independent variable (morphological measurements) played a considerable role in the BWT (dependent variable) prediction. In RS and WAD goats, a positive and statistically significant (p < 0.0001) correlation was observed between BWT and each morphological measurement. However, in SH goats, both positive and negative statistically significant correlations were observed between BWT and morphological measurements. The CART analysis indicated that in RS and WAD goats, HG played a considerable role in BWT prediction, while, in SH goats, BL was considered the most critical independent variable in BWT prediction. Therefore, this study suggests that HG can be used as a prognostic index for BWT estimation in Red Sokoto and West African Dwarf, while BL can be used for Sahel goats. 1 results 1
- Classification and regression tree (CART) is a tree-based data mining algorithm that develops a model to predict an outcome. This study purposed to create a model to predict the body weight (BWT) of Red Sokoto (RS), Sahel (SH), and West African Dwarf (WAD) goats using morphological measurements (such as body length, BL; head girth, HG; head width, HDW; face length, FAL; height at wither, HTW; rump length, RL; shoulder width, SW; rump width, RW; and rump height, RH). In total, 600 goats were used for this study (200 each of RS, SH, and WAD goats). Pearson’s Moment Correlation was used to evaluate the degree of association between BWT and each morphological measurement. Concomitantly, CART analysis was performed to estimate which independent variable (morphological measurements) played a considerable role in the BWT (dependent variable) prediction. In RS and WAD goats, a positive and statistically significant (p < 0.0001) correlation was observed between BWT and each morphological measurement. However, in SH goats, both positive and negative statistically significant correlations were observed between BWT and morphological measurements. The CART analysis indicated that in RS and WAD goats, HG played a considerable role in BWT prediction, while, in SH goats, BL was considered the most critical independent variable in BWT prediction. Therefore, this study suggests that HG can be used as a prognostic index for BWT estimation in Red Sokoto and West African Dwarf, while BL can be used for Sahel goats. The SAS codes used are available via a GitHub repository (https://github.com/Soullevram/CART). 1 results 1
- Data mining 1 results 1
- Decision Tree 1 results 1
- Educational Data mining 1 results 1
- Educational data mining has contributed to enhancing student academic performance by way of enabling stakeholders in academic institutions to have a pre-knowledge of the risks and dangers ahead and how to mitigate them. Prediction algorithms perform differently on dataset, and so, the need to develop models using different prediction algorithms and evaluating the result of such predictions is very important in order to be sure the best algorithm for a particular dataset is used. This work employed four classifiers: K-Nearest-Neighbour, Neural Network, Naïve Bayes and Decision Tree to model and, evaluated their models to know the performance of each on the target dataset. Their results were evaluated based on the various performance metrics. The results showed that Decision Tree had the highest accuracy on the dataset with test accuracy of 48.25% and therefore is the most suitable out of the four classifiers for performing prediction modelling on the dataset. Naïve Bayes is the second-best prediction model that can be used for predicting academic performance with an accuracy of 36.25%., followed by Neural Network with accuracy of 32.5 % and then K-Nearest Neighbour with accuracy of 32.5% but with lower precision, recall and area under Receiver Operating Characteristic curve. 1 results 1
- Goat 1 results 1
- K-Nearest Neighbour 1 results 1
- Naive Bayes 1 results 1
- Neural Network 1 results 1
- algorithm 1 results 1
- body weight 1 results 1
- data mining 1 results 1
- goats 1 results 1
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