...We saw the challenge as a great application for a machine learning (ML) model. ML models are computer programs that can automatically improve their own performance (judged by a chosen...
...tools that enable others to build the things they need. The federated model For these reasons we are exploring a federated model of API discovery. By federated, we mean...
...method involved enriching an internal dataset with features such as household income, tenure and deprivation; and running a random forest model in R. The resulting model has a prioritised feature...
...To create the model, data such as criminal history, network information and intelligence was user. Afterwards the model is tested using training data to predict the probability of becoming a...
...in model flexibility. In each case the models are trained on 3 weeks worth of data and are used to predict the following week (hour-by-hour). This helps circumvent overfitting issues...
...label was high enough. Unlabelled comments were passed into an unsupervised LDA topic model to identify emerging topics that could become new tags, ensuring data wasn’t wasted. A supervised model...
...full-time on the team, and the service manager was rich in her praise of the individual in this role. It was clear how an embedded data analyst is benefitting the...
...Although this work was going well, it was taking quite a long time and was very resource and engagement heavy - both for us at GDS and for the departments...
...responses. When faced with such a daunting analysis task, the first port of call is statistical modelling, normally using the General Linear Model (GLM). Statistical modelling tools scan survey responses...
...operating model is for HMRC to decide. GDS also recognise the ambitious and exciting programme of reform that HMRC have for transforming their digital services. However, the operating model must...