This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementPMML WriterThis nodes writes a PMML model from a PMML model port into a PMML v4.0 compliant
file or to a remote location denoted by an URL. If a PMML file from another version is read by the PMML Reader and
directly written by this node, it is converted into PMML v4.0.
If the model is not valid (unknown data types, etc.) an exception is
thrown during execution.
Note that if the destination location is a remote URL not all options are available because in general it's
not possible to determine whether the remote location exists. In this case it will always be overwritten.
Options
Popular Predecessors
- k-Means19 %
Naive Bayes Learner13 %Logistic Regression Learner8 %- Missing Value7 %
- R to PMML6 %
- Decision Tree to Ruleset5 %
- Java Edit Variable3 %
- Ruleset Editor2 %
- Create Temp Dir (legacy)2 %
- Column Filter (PMML)2 %
- Fuzzy c-Means2 %
PMML Writer1 %Model Reader1 %- One to Many (PMML)1 %
Table Row to Variable1 %- Number to Category (Apply)1 %
PMML Reader1 %- Denormalizer (PMML)1 %
- PMML Validation Data Appender1 %
- String Manipulation (Variable)1 %
- Numeric Binner (PMML)1 %
Number To String (PMML)1 %- Table to PMML Ensemble< 1 %
- Create File Name (legacy)< 1 %
- Cell to PMML< 1 %
- Cell to Model< 1 %
- Logistic Regression Learner< 1 %
- PMML Ensemble Loop End< 1 %
- Decision Tree Learner< 1 %
- Normalizer Apply (PMML)< 1 %
- PMML Reader< 1 %
- Category to Number (Apply)< 1 %
Merge Variables< 1 %PMML Reader< 1 %- RProp MLP Learner< 1 %
- Linear Regression Learner< 1 %
- SVM Learner< 1 %
- Gradient Boosted Trees to PMML< 1 %
- Empty PMML Creator< 1 %
Table Column to Variable< 1 %- Simple Regression Tree to PMML< 1 %
- Explorer Browser< 1 %
- Java Edit Variable (simple)< 1 %
- Create Directory (legacy)< 1 %
- Decision Tree Predictor< 1 %
- Category to Number< 1 %
- Rule Engine (Dictionary)< 1 %
- PMML Predictor< 1 %
- Naive Bayes Learner< 1 %
- Polynomial Regression Learner< 1 %
- Spark Missing Value< 1 %
- String Input (legacy)< 1 %
- Many to One (PMML)< 1 %
CASE Switch Variable (Start)< 1 %CASE Switch Model (End)< 1 %- DB Auto-Binner< 1 %
Loop End< 1 %Logistic Regression Learner< 1 %Tree Ensemble Learner< 1 %- PMML Model Appender< 1 %
- PMML Transformation Appender< 1 %
- Normalizer (PMML)< 1 %
- String Manipulation< 1 %
- Spark MLlib to PMML< 1 %
- Model Loop Start< 1 %
- Date&Time Input (legacy)< 1 %
- Value Selection (legacy)< 1 %
- Run Garbage Collector< 1 %
- URL to File Path (Variable)< 1 %
Image Writer (Port)< 1 %Extract Context Properties< 1 %Model Writer< 1 %- Table Creator< 1 %
Scorer< 1 %- Simple Regression Tree Learner< 1 %
- Normalizer< 1 %
- Number to String (PMML)< 1 %
String To Number (PMML)< 1 %- String to Number (PMML)< 1 %
- Spark Normalizer< 1 %
- Boosting Predictor Loop Start< 1 %
- Local File Browser Configuration< 1 %
- String Configuration< 1 %
- Text Output (legacy)< 1 %
Table Row to Variable< 1 %
Views
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Workflows
Credit Scoring Model(diksha kumari)KNIME Hub- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Hub
- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Forum
- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Hub
- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Hub
- Show all 86 workflows
Links
Developers
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