In simplest terms, a machine learning model is an algorithm that has been trained.
An algorithm is a procedure run on data to solve a mathematical problem. During training, the algorithm is applied to a dataset to learn its internal logic or rules. The algorithm is then applied to a different data subset to test performance (i.e., how well did it learn the rules?). After training, testing, and refining through parameter adjustment, the model can then be applied to the problem using similar datasets (with different values) and perform similarly.
For example, if a model is trained to identify customers lifetime value (LTV) from a specific type of dataset (e.g., customer demographics, specific attributes, and 180 days of customer activity), the model can be applied to other datasets with matching structure and perform as well as on test datasets.
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