DataScience

Data Scoring is a key component of understanding machine learning model outcomes and choosing the most accurate model that produces the most valuable insights. Once you have a model in production scoring new data, you’ll uncover insights that you can use to create business value. Using the above example, the model scores identify which current customers are at a high risk of churning, enabling you to plan outreach or special offers to prevent that from happening. Model development is generally a two-stage process. The first stage is training and validation, during which you apply algorithms to data for which you know the outcomes to uncover patterns between its features and the target variable. The second stage is scoring, in which you apply the trained model to a new dataset. Then, the model returns outcomes in the form of probability scores for classification problems and estimated averages for regression problems. Finally, you deploy the trained model into a production application o...