The dataset used to apply a predictive model to generate new predictions is called the ______ data.

• A. Scoring
• B. Training
• C. Validation
• D. Testing

Answer: (A)

Scoring is the phase where you take the trained model and apply it to new, unseen data to generate predictions or scores. After building and validating the model on training and validation data, you use it on fresh records to produce outputs such as predicted probabilities or class labels. This is different from training data (used to learn the model), validation data (used to fine-tune parameters and guard against overfitting), and testing data (used to estimate how the model will perform on unseen data). In practice, scoring turns the model into a predictive tool for real-world, new inputs.