Often, the process flow of many big data projects is iterative, which means a lot of back-and-forth testing new ideas, new features to include, tweaking various hyper-parameters, and so on, with a fail fast attitude. The end result of these projects is usually a model that can answer a question being posed. Notice that we didn't say accurately answer a question being posed! One pitfall of many data scientists these days is their inability to generalize a model for new data, meaning that they have overfit their data so that the model provides poor results when given new data. Accuracy is extremely task-dependent and is usually dictated by the business needs with some sensitivity analysis being done to weigh the cost-benefits of the model outcomes. However, there are a few standard accuracy measures that we will go over throughout this book so that you can compare various models to see how changes to the model impact the result.