Statistical and Machine Learning
Statistical and Machine Learning (S&ML) combines traditional statistical methods with modern computational algorithms to analyse and model data. Statistical learning focuses on estimation and inference, using probabilistic models and assumptions to explain relationships between variables, while machine learning emphasises predictive accuracy through flexible, data-driven approaches. Classical methods such as regression, time series analysis, and hypothesis testing form the foundation of statistical learning. In contrast, machine learning techniques, including decision trees, neural networks, and ensemble methods, leverage computational power to handle complex, high-dimensional data.
The synergy between statistics and machine learning is evident in various contexts, such as regularisation (Ridge, LASSO, MEnet), Bayesian inference, and ensemble learning, which merge interpretability with predictive strength. While statistical methods remain crucial for hypothesis-driven analysis and explainability, machine learning excels in high-dimensional, unstructured environments where traditional models struggle. Integrating statistical rigor with machine learning’s adaptability enhances decision-making as data science advances, making S&ML essential in fields like finance, healthcare, and social sciences.