Training, validation, and testing are parts of the machine learning model building process. Using historical data, the model is trained and “learns” to identify patterns and trends. The model is then validated through comparing its outputs to known outcomes for a second dataset. The model is evaluated on its ability to specify correctly or get close to the true outcome or target variable in that second dataset. This stage of evaluating the model allows its builder to see how well it is performing and to compare it to different versions of the model. Those different versions may use other predictive approaches or be set up in different ways that offer better or worse perfomance. Finally, the model is tested on a third set of data it has never seen before, allowing its builder to judge its likely performance when it is deployed.