Data Mining - Scoring (Applying)
Table of Contents
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:
- and feature extraction.
2 - Articles Related
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|