What kind of models does machine learning rely on?

Machine learning fundamentally relies on algorithms and statistical models to function effectively. These components are essential because they enable the system to learn from data, identify patterns, and make predictions or decisions based on new, unseen data.

Algorithms are the procedures or formulas that machines use to process data and derive insights. They operate on the data, learning from it by adjusting the model parameters to minimize errors in predictions. Statistical models, on the other hand, allow these algorithms to quantify relationships within the data and handle uncertainty, making the approaches robust and adaptable.

In contrast to other options, machine learning does not solely depend on theoretical or social models, nor does it operate strictly on fixed, predefined rules. Unlike traditional programming, where rules are explicitly defined by programmers, machine learning models improvise and develop their own rules through training on data. This approach leads to more flexibility and adaptability, enabling applications in varied fields like finance, healthcare, and marketing, where data patterns are complex and not easily scripted.