Machine learning uses various terms to describe its components:

• Labels: These are the target variables that the model should predict. Example: Construction cost (e.g., in dollars), duration of construction work (e.g., in months).
• Features: These are independent variables that serve as inputs to the model. In a prediction model, they are used to predict labels. Examples: Site area (in square meters), number of floors of a building, total building area (in square meters), geographic location (latitude and longitude), type of materials used in construction. The number of characteristics also determines the dimensionality of the data.
• Model: This is a collection of different hypotheses, one of which approximates the target function to be predicted or approximated. Example: A machine learning model that uses regression analysis techniques to predict the cost and timing of construction.
• Learning Algorithm: This is the process of finding the best-fitting hypothesis in a model that accurately fits the target function using a training data set. Example: A linear regression, KNN or random forest algorithm that analyzes cost and construction time data to identify relationships and patterns.
• Training: During machine learning training, the algorithm analyzes the training data, finding patterns that correspond to the relationship between input attributes and target labels. The result of this process is a trained machine learning model that is ready for prediction. Example: A process in which an algorithm analyzes historical construction data (cost, time, facility characteristics) to create a predictive model.