Fuses multiple prediction confidences into one, combined prediction, using the selected fusion method.
The selected fusion method will be applied to all confidence values (resulting from multiple predictions)
of the same class. After the fusion method was applied, the resulting combined prediction confidences
will be normalized to add up to one, such that they can be interpreted as probabilities.
Note: Missing values will be skipped.
Options
Popular Predecessors
Scorer8 %- Decision Tree Predictor7 %
Column Appender4 %- MultiLayerPerceptron Predictor4 %
String To Number4 %- Prediction Fusion3 %
Random Forest Predictor2 %- ROC Curve (legacy)2 %
File Reader2 %Naive Bayes Predictor2 %- PNN Predictor2 %
- Random Forest Predictor2 %
Number To String2 %- CSV Reader2 %
Row Filter (deprecated)2 %- Weka Predictor (3.7)2 %
- Joiner2 %
- X-Aggregator1 %
- Category to Number1 %
Joiner1 %- Voting Loop End1 %
- Naive Bayes Predictor1 %
- Model to Cell1 %
- File Reader1 %
Regression Predictor1 %- Cross Joiner1 %
- Column Filter1 %
- Normalizer1 %
- Missing Value1 %
PMML Ensemble Predictor1 %- Logistic Regression Predictor1 %
- Gradient Boosted Trees Predictor1 %
- Partitioning1 %
CSV Reader< 1 %Concatenate (Optional in)< 1 %Excel Reader (XLS)< 1 %- K Nearest Neighbor< 1 %
- Rule Engine (Dictionary)< 1 %
- Boosting Learner Loop End< 1 %
- Excel Reader< 1 %
- Create Collection Column< 1 %
Table Row to Variable< 1 %- Fuzzy Rule Learner< 1 %
- Linear Regression Learner< 1 %
- Rule-based Row Filter< 1 %
- Math Formula< 1 %
- JPMML Classifier< 1 %
- JSON to Table< 1 %
- Table Creator< 1 %
Loop End< 1 %PCA< 1 %- Polynomial Regression Learner< 1 %
- Association Rule Learner< 1 %
- SVM Predictor< 1 %
- XGBoost Predictor (Regression)< 1 %
- RDKit Descriptor Calculation< 1 %
Table Reader< 1 %- Java Snippet< 1 %
- Fuzzy Rule Predictor< 1 %
- Regression Predictor< 1 %
Simple Regression Tree Predictor< 1 %Gradient Boosted Trees Predictor< 1 %- Concatenate< 1 %
- Number to Category (Apply)< 1 %
- Reference Column Filter< 1 %
Column Rename< 1 %- Boosting Learner Loop Start< 1 %
- Parameter Optimization Loop Start< 1 %
- Fuzzy c-Means< 1 %
- k-Means< 1 %
Random Forest Predictor< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
Tree Ensemble Learner< 1 %- Tree Ensemble Predictor (Regression)< 1 %
- Random Forest Learner (Regression)< 1 %
- Random Forest Predictor (Regression)< 1 %
- Simple Regression Tree Predictor< 1 %
- Column Aggregator< 1 %
- Column Resorter< 1 %
- Reference Row Filter< 1 %
- GroupBy< 1 %
- Normalizer (Apply)< 1 %
- Number Rounder< 1 %
- RowID< 1 %
- Sorter< 1 %
- String Replacer< 1 %
- Rule Engine< 1 %
Merge Variables< 1 %- Crosstab< 1 %
- Color Manager< 1 %
K Nearest Neighbor (Distance Function)< 1 %- Model Loop End< 1 %
- Math Formula (Multi Column)< 1 %
- Java Snippet Row Filter< 1 %
- Date&Time Difference< 1 %
- Create Table Structure< 1 %
- Double to Integer< 1 %
Color Appender< 1 %- StanfordNLP NE Tagger< 1 %
- XGBoost Predictor< 1 %
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Workflows
01_AnalyticsKNIME Hub- 01_Combining_Classifiers_using_Prediction_FusionKNIME Hub
- 04_Cross-Platform_Ensemble_ModelKNIME Hub
- StromverträgeKNIME Hub
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