|
Classic
Overview | Classic
Uniqueness | Classic
Key features | Classic
Userbase | Classic
System Requirements
Classic
Uniqueness
Fast, Robust and Scalable
Classic has been developed with speed, robustness and
scalability as central to its design allowing it to run
on data ranging from a few records to millions of records.
Generation of PMML output
The Predictive Modelling Markup Language (PMML) is a
standard, XML representation for the knowledge discovered
using data mining. Classic can produce the resulting
decision tree in PMML enabling the easy exchange for
knowledge produced by Classic and other data mining
vendor applications and scoring engines.
Ability to assign models to the leaf nodes resulting
in more complex models
Most decision tree induction algorithms generate a decision
tree through the use of information theoretic and statistical
measures, progressively partitioning the data provided
as input to the algorithm, until further splitting of
the data actually degrades the performance of the resulting
knowledge on unseen data due to overfitting. Once the
tree has been built, a new data element can be assigned
to one of the leaf nodes based on the values of the
independent attributes.
A unique feature of Classic is its ability to combine
the simplicity of the decision tree univariate splits
with other modelling paradigms assigned locally to the
tree’s leaf nodes, creating more complex local decision
surfaces and producing more accurate models from data.
back to products |
|
|
Coral
Summary
