The foundation; quality and quantity of data determine how well the model learns.
The input variables or attributes used by the model to identify patterns.
The mathematical method (e.g., Decision Trees, Neural Networks) used to learn from data.
Measuring model performance using metrics (e.g., accuracy, precision, recall) to ensure reliability.