olice team responsible for reducing CCE/CSE, known as Operation TOPAZ. An important advantage of a decision tree as the machine learning method, is that it has straightforward interpretation that can provide direct insights to help explain model predictions, for example which of the input variables were the most important drivers of a particular outcome. This is not shown in the TFD, but means the developer is able to check that the outputs make sense while developing the models. This advantage can outweigh gains in prediction accuracy of more sophisticated models. CSE/CCE Model Inputs The CCE model has 27 predictor variables, all drawn from the Niche database (the police’s crime recording system). The variables are counts of occurrences within certain time periods where a child is reported as missing, or as the victim or offender in domestic abuse, violence, or public order offence, or sexual offence, or as the victim of a vehicle offence. The time periods are 6, 12, 24, 30, and 60 months preceding the current day. However, as we discovered in our fieldwork, it is unclear what the 27 variables are and how these are weighted as part of the model. The CSE model is trained using the Barnardos Against Sexual Exploitation (BASE) cohort of around 1000 children known to have been sexually exploited as the at risk of CSE class, alongside a set of children assumed not to be at risk, taken from the Police database, which includes all under 18s in Avon and Somerset. Social Finance Limited +44 (0) 20 7770 6836 87 Vauxhall Walk, London SE11 5HJ socialfinance.org.uk 94 The CCE model is trained using the Barnardos ROUTES (a programme designed to reduce CCE) cohort as the at risk of CCE class, and a set of children assumed not to be at risk of criminal exploitation, taken from the Pol