A PMML-compliant scoring engine supports:

• PMML-compliant models (4.2 and earlier versions) produced by various vendors, except for Baseline Model, ScoreCard Model, Sequence Model, and Text Model. Refer to the Data Mining Group (DMG) web site for a list of supported models.
• Non-PMML models produced by IBM SPSS products: Discriminant and Bayesian networks
• PMML 4.2 transformations completely

Different kinds of models can produce various scoring results. For example:

• Classification models (those with a categorical target: Bayes Net, General Regression, Mining, Naive Bayes, k-Nearest Neighbor, Neural Network, Regression, Ruleset, Support Vector Machine, and Tree) produce:
  • Predicted values
  • Probabilities
  • Confidence values
• Regression models (those with a continuous target: General Regression, Mining, k-Nearest Neighbor, Neural Network, Regression, and Tree) produce predicted values; some also produce standard errors.
• Cox regression (in General Regression) produces predicted survival probability and cumulative hazard values.
• Tree models also produce Node ID.
• Clustering models produce Cluster ID and Cluster affinity.
• Anomaly Detection (represented as Clustering) produces anomaly index and top reasons.
• Association models produce Consequent, Rule ID, and confidence for top matching rules.

Python example code:

from spss.ml.score import Score

with open("linear.pmml") as reader:
    pmmlString = reader.read()

score = Score().fromPMML(pmmlString)
scoredDf = score.transform(data)
scoredDf.show()

Parent topic: SPSS predictive analytics algorithms