• Parameters: is a set of measurable factors that define a system and the part of the model that is learned from past training data.
  • Predictive modeling: the process of using a mathematical approach to predict future events or outcomes by analyzing patterns in a given set of input data.
  • Early stopping: a technique used to avoid Overfitting when training a machine learning model using an iterative method.
  • iteration: the process of repeating a statement/block of code a specific number of times, producing an output one after another.
  • Drift: A change in the model’s output, meaning that new output differs from output results from past.