Training is a supervised problem phase also known as building whereby the software analyses many cases where the target value is already known.
In the training process, the model “learns” the logic for making the prediction.
A building operation is also called “training” a model, and the model is said to “learn” from the training data.
A common data mining practice is to build (or train) your model against part of the source data, and then to test the model against the remaining portion of your data. See evaluation method
Models are often trained to identify the set of features, algorithms, and hyper-parameters that create the best model for their problem. Before arriving at the ideal model, it is not uncommon to train hundreds of models that do not make the cut.
A model that seeks to identify the customers who are likely to respond to a promotion must be trained by analyzing the characteristics of many customers who are known to have responded or not responded to a promotion in the past.
A record of a trained model to build the history will be like: