Unveiling the Power of Machine Learning Algorithms
Mehak Malhotra, Amir Ahmad Dar, Akshat Jain, Chamindu Adithya · Advances in electronic government, digital divide, and regional development book series · 2023
The study compares neural networks, k-nearest neighbors, decision trees, and support vector machines in various scenarios, exploring both supervised and unsupervised learning approaches. Benchmark datasets from the banking, healthcare, and image recognition sectors are used to validate the study. Performance metrics, including accuracy, precision, recall, and F1 score, are employed for a comprehensive analysis. The study addresses the transparency of machine learning algorithms, investigating ways to simplify complicated decision-making processes. It also explores how algorithmic performance is affected by parametric modifications to find the best configurations for specific applications. The report offers significant perspectives for professionals, researchers, and policymakers in sectors other than academia, boosting their basic understanding of machine learning technology in revolutionary domains. All in all, this study simplifies machine learning techniques and offers useful insights into their effectiveness and suitability in many fields.