Ishaan Gupta
Independent Researcher
India
Abstract
Machine learning (ML) algorithms have gained significant traction in solving complex engineering problems, especially those involving structured data sets. Structured data, commonly represented in tabular formats, requires efficient algorithms for classification and regression tasks. This study presents a comparative performance analysis of several widely used ML algorithms on structured data sets to identify their strengths and weaknesses in terms of accuracy, computational efficiency, and robustness. The algorithms evaluated include Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting Machines (GBM). Using benchmark datasets from the UCI Machine Learning Repository, the study employs cross-validation to ensure reliability. Results indicate that ensemble methods such as Random Forest and GBM generally outperform single classifiers in accuracy but demand higher computational resources. This work provides engineers and data scientists with practical insights into selecting appropriate ML algorithms for structured data applications.
Keywords
Machine learning, structured data, decision trees, random forests, support vector machines, classification, regression, performance analysis.
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