Predictive Analytics vs. Information Retrieval
Predictive Analytics and Information Retrieval (IR) are two technologies used in data mining. However, they are used for different purposes, as the table below illustrates. One common aspect is that the quality of both types of solutions can be measured with Precision and Recall metrics. See the previous post for more information about precision and recall.
|
|
Information Retrieval
|
Predictive Analytics
|
|
General Purpose
|
Multi-database, open-ended research
|
Prediction, classification and scoring
|
|
Scope of Results
|
Wide
|
Narrow
|
|
Type of Results
|
Entire Documents from various sources, such as
internal databases, document collections, and Google results.
|
A single value, such as a risk percentage
(e.g., "42%"), classification ("red", "blue", "green"), or predicted
value. (e.g., "54.2")
|
|
Setup
|
Connect to source databases, then perform any
desired query.
|
Develop a custom model for each task.
|
|
Typical Uses
|
Research background info on a given technical
issue.
|
Pinpoint high-risk situations among hundreds or
even millions of known cases.
|
|
Mechanism
|
Sophisticated indexing of source documents.
|
Discover complex patterns in high-dimensional
spaces.
|
|
How are results found?
|
Matching against a user-supplied ad-hoc query.
|
Matching against mathematical patterns that
were learned during a training phase.
|
|
Strengths
|
Flexibility - handle any ad-hoc query on the
fly.
|
Automation - provide answers without user
intervention..
|