PMML 4.3 - Changes from PMML 4.2.1
General Structure
- Allowed
PROB-NUMBER to accept scientific notation.
- Added GaussianProcess to
MODEL-ELEMENT
- Added BayesianNetwork to
MODEL-ELEMENT
Mining Schema
- Clarified when
lowValue and
highValue are required.
Built-in Functions
- Added new functions:
normalCDF
- Normal Cumulative Density Function
normalPDF
- Normal Probability Density Function
stdNormalCDF
- Standard Cumulative Density Function
stdNormalPDF
- Standards Normal Probability Density Function
erf
- Related Error Function
normalIDF
- Normal Inverse Distribution Function
stdNormalIDF
- Standard Normal Inverse Distribution Function
- Corrections to documentation.
Output
- Made
dataType of OutputField required.
- Added a new attribute
isFinalResult to
OutputField .
- Added mentions of KNN models with regard to the
rank attribute and the entityId
feature.
Model Explanation
- Allowed multiple instances of
LiftData in
PredictiveModelQuality .
Scope of Fields
- Allowed an
OutputField with
feature="transformedValue" to forward reference a
derived field defined in LocalTransformations .
Association Rules
- Added new attributes
field and category
to Item element.
- Augmented documentation.
General Regression
- Added clarifications to the documentation.
KNN
- Corrected record and field counts in Scoring Example 1.
Multiple Models
- Clarifications to various model combination methods.
- Defined how predicted probabilities are to be calculated for the
following model combination methods:
majorityVote
weightedMajorityVote
average
weightedAverage
max
median
Neural Networks
- New activation function
rectifier .
Support Vector Machines
- Added a new attribute
maxWins to
SupportVectorMachineModel .
- Added a new element
CategoricalPredictor to
VectorFields .
- Dropped an inadvertent requirement to specify
classificationMethod on regression models.
- Added
CategoricalPredictor as an alternative
to FieldRef in
VectorFields element.
Regression
- Major changes to the documentation regarding classification
model scoring.
Rule Sets
- Minor corrections to the documentation.
|