Abstract
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.
Top2. Literature Review
Machine learning is based on the idea that computers can learn from data without explicit programming. Basic learning algorithms were developed by pioneers such as Alan Turing and Arthur Samuel, whose early research in the 20th century laid the foundation for machine learning. Machine learning progressed from basic rule-based models to complex algorithms with the ability to process large amounts of data as processing power increased.
The story of machine learning unfolds throughout time, beginning in the mid-20th century with the early attempts of geniuses such as Arthur Samuel, who is credited with coining the phrase “machine learning.” This era, which spanned the 1950s and 1960s, saw the birth of self-learning algorithms, as demonstrated by Samuel's construction of a checkers program designed to improve its performance via experience. Decision tree algorithms, an early expression of supervised learning, were introduced in the 1970s. As the 1980s progressed, the importance of symbolic learning and expert systems attempted to replicate human decision-making processes. The 1990s saw the rise of Support Vector Machines and neural networks, resulting in an evolution toward statistical methodologies. During this time, however, computational limits prevented wider adoption. Ensemble approaches caused an uprising in the late 1990s and early 2000s, boosting the effectiveness of machine learning models. By the mid-2000s, the convergence of large datasets and improved computer power had set the ground for the return of neural networks, resulting in significant advances in deep learning. Deep learning has risen in popularity in recent years, pushed by technological advances in technology and the widespread availability of large datasets. Milestones like AlphaGo's victory over a human champion in 2016 highlight the field's development, with the following advances in natural language processing enhancing its impact. Machine learning has entered the fabric of machine learning, easily integrating into numerous areas ranging from healthcare to finance. The historical timeline highlights a complicated interplay between algorithmic innovation, data accessibility, and computational capabilities, defining machine learning's history as a dynamic and pivotal domain within the larger landscape of artificial intelligence (Breiman, 2017; Dietterich, 2000; LeCun et al., 2015; Rumelhart et al., 1986; Samuel, 2000; Silver et al., 2016; Vapnik, 1999).