Machine learning develops computational models that learn patterns from data to make increasingly accurate predictions or classifications. It extends familiar statistical tools such as linear and logistic regression through large datasets, nonlinear relationships, and iterative training. section 1.1.4
Machine learning is characterized by:
- Using data-driven learning rather than relying only on explicitly programmed rules. section 1.1.4
- Connecting statistical foundations—including probability, optimization, and generalization—to flexible predictive models. section 1.1.4
- Supporting analysis workflows through Python libraries such as scikit-learn and statsmodels. section 1.2.1
Typical applications
Regression supports trend modeling, forecasting, and identifying behavioral drivers; classification models support categorical decisions. Decision trees and random forests capture subtle or dynamic patterns that conventional approaches may miss. section 1.1.4 section 1.1.4
