What is the main purpose of the 'Apply Model' step in a predictive workflow?

• A. Train the Model
• B. Score New Data Using the Trained Model
• C. Validate the Model
• D. Visualize Data

Answer: (B)

The Apply Model step is about using a trained model to generate predictions on new, unseen data. After the model has learned from historical records, applying it means feeding fresh examples and getting outputs such as predicted probabilities or class labels. This is the operational phase where the model becomes a decision-support tool, scoring or classifying incoming data to inform actions (for instance, approving a loan, predicting churn, or flagging anomalies). It’s distinct from training, which builds the model from past data, and validation, which assesses how well the model would perform on new data. Visualization, while helpful for understanding results, is not about producing predictions.