d up, as well as a descriptive paragraph briefly detailing the variables the score is summarising. The NEET model is composed of three internal “sub-models” that feed into the final NEET model risk score:  Attendance Model  Attainment  Supporting Families Model Each of these is a logistic regression model, whose output is scaled to the range 0-100. Logistic regression is a well-known and long-standing method for modelling a binary outcome. Scores above 50 are considered potentially at risk on that outcome. The derived risk score is a combination of the output of the three sub-models. People are only assigned a risk score if they are considered potentially at risk by at least two of the three sub-models (at least one education model and the supporting family model). The goal is to make sure that the NEET Risk Model final result is focused on education related issues, balancing the cases highlighted by the Supporting Families sub-model. Additionally, internally within the models', different variables are weighed based on understanding of stronger predictive factors of vulnerability. The model is trained with a cohort of around 1000 cases of NEET individuals to represent the target distribution in the input variables. These are then used to check the wider cohort for indicators that they might become NEET. They ask, “at the point this person became/was becoming NEET, what did they look like?” and then the model checks for other cases that share indicators. The goal of the model is to define a pre-NEET archetype that can then be used to provide support for children and young people before they leave education. The model is refreshed weekly and retrained about every 18 months because the NEET cohort changes at the end of every school year or the following September. The i