Blog | Roberto Vega

How machine learning works. A non-technical explanation.

To use machine learning we require 4 basic components: Data, a model, a cost function (learning objective) and an optimization method (learning algorithm)
Learning algorithms learn parameters to solve an equation. The prediction given by machine learning is based on the solution of that equation.
Machine learning scientist provide the ‘backbone’ of the equation, but the learning algorithm learns ‘the details’ based on the given data.

Association and prediction studies have different goals. Machine learning excels in prediction studies.
Association studies focus on understanding a phenomena. They look for relationships between variables and outcomes, but they might not have predictive power.
Prediction studies use many variables to create predictors. They learn patterns in the training data to make predictions on new data. They might be very accurate, but hard to interpret, and that is fine.