A Comprehensive Discourse on Shallow Learning and its Applications

Abstract

Shallow learning, a fundamental approach in machine learning, encompasses a variety of algorithms and techniques aimed at learning patterns and making predictions from labelled data. Unlike deep learning, which involves complex architectures with multiple layers of abstraction, shallow learning focuses on simpler models with limited complexity. This abstract explores the essence of shallow learning, its algorithms, applications, and challenges. Shallow learning algorithms include classic methods such as decision trees, support vector machines, k-nearest neighbours, and logistic regression, among others. These algorithms are typically trained using supervised learning techniques, where the model learns from input-output pairs to make predictions on new, unseen data. Shallow learning models excel in tasks such as classification and regression, where the goal is to assign labels or predict continuous values to input data. Applications of shallow learning span across various domains, including healthcare, finance, marketing, and cyber security. In healthcare, shallow learning models are used for disease diagnosis and prognosis prediction based on patient data. In finance, these models aid in fraud detection, credit scoring, and stock market prediction. Marketing applications involve customer segmentation and churn prediction, while in cyber security; shallow learning is utilized for malware detection and network intrusion detection.