DATA Scoring : Geting value from BIG Data

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 or use the insights it uncovers to improve business processes.

For example, to score a model meant to predict the likelihood of customer churn:

Build a churn behavior model using a historical dataset that contains information on which customers churned and other information that you believe contributed to that outcome.

Apply the model to existing customer data to produce a value, or “score,” that estimates their likelihood to churn.

Different ways to score models include:

Batch scoring. Useful for when the model’s decisions don’t have to be implemented immediately. For example, a marketer may batch score a model on a list of purchased leads to determine which are most likely to buy their product.

Real-time scoring. Useful when time is of the essence in realizing value from the model. For example, a bank needs a fraud model to score credit card transactions within milliseconds to quickly deny likely fraudulent transactions.