article · Journal of applied science and environmental management
A benchmarking assessment evaluated multiple supervised machine learning algorithms to compare their efficiency and performance metrics. Using Python and data retrieved from the Kaggle repository, the evaluation tested K-Nearest Neighbor, Random Forest, and Decision Tree variants, specifically ID3, C4.5, C5.0, and CART. The results indicate clear trade-offs between prediction accuracy and processing speed. Among the evaluated models, the C4.5 decision tree variant achieved the highest prediction accuracy. Conversely, the CART decision tree variant and K-Nearest Neighbor required the shortest durations for learning and prediction. Consequently, selecting an algorithm depends on operational priorities: C4.5 serves best when predictive precision is paramount, whereas CART and K-Nearest Neighbor are preferable when low training and execution times are the primary requirement.
Choosing the right machine learning algorithm requires balancing precision against computational speed. Understanding the distinct operational strengths of algorithms such as C4.5, CART, and K-Nearest Neighbor allows practitioners to select models that fit their specific constraints, whether they need highly accurate classifications or rapid data processing under limited computational budgets.
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Machine learning provides more verbose algorithms capable of accurately predicting, classifying groups as needed. Consequently, the objective of this paper is to assess the benchmarking of Supervised Machine Learning Algorithms of K-Nearest Neighbor, Random Forest, Decision Tree and it variants (ID3, C4.5, C5.0 and CART) based on efficiency and performance metrics using python programming after downloading dataset from Kaggle repository. Dataset to the aforementioned models reveals that, the C4.5 variant of decision tree had the highest prediction accuracy, CART and KNN had the minimal learning and prediction time. If accuracy is the based preference, C4.5 variant of decision tree should be recognized, but when the chief concern is nominal time for training and prediction, then CART and KNN standout.
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DOI: 10.4314/jasem.v29i3.15
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