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Classic
Overview | Classic
Uniqueness | Classic
Key features | Classic
Userbase | Classic
System Requirements
Classic Key Features
Handling
Missing Values
Classic uses surrogate predicates within each decision node
to deal with missing data. Surrogate predicates are used when
the data record being scored has a missing value for the split
attribute of the node. The surrogate mimics the split effected
by the primary split predicate of the node.
Binary and Multiple Splits
Classic provides the user with the power to adjust the level
of bushiness of the resulting tree by adjusting the maximum
number of branches emerging from each node.
Handle Continuous and Categorical
Data
Classic automatically decides on the optimal binning of continuous
attributes to maximise their contribution to the resulting gain
in information by using it as the principal split or surrogate
predicate.
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.
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.
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Coral
Summary
