Data Mining - Scoring (Applying)

Thomas Bayes


The process of applying a model to new data is known as scoring.

Apply data, also called scoring data, is the actual population to which a model is applied.

Scoring operation for:


You might build a model that identifies the characteristics of customers who frequently buy a certain product.

To obtain a list of customers who shop at a certain store and are likely to buy a related product, you might apply the model to the customer data for that store.

In this case, the store customer data is the scoring data.



Scoring is the purpose of classification and regression, the principal supervised mining techniques. Most supervised learning can be applied to a population of interest.

Oracle Data Mining does not support the scoring operation for attribute importance.


Unsupervised Models are built on a population of interest to obtain information about that population; they cannot be applied to separate data.

Although unsupervised data mining does not specify a target, most unsupervised learning can be applied to a population of interest.

Unsupervised Function Scoring operation Supported
clustering and feature extraction Yes
association rules No

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