Data Mining - Scoring (Applying)

1 - About

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:

3 - Example

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.

4 - Functions

4.1 - Supervised

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.

4.2 - Unsupervised

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
data_mining/scoring.txt · Last modified: 2014/02/03 20:42 by gerardnico