Together with the corresponding loop end node a boosting loop can be constructed. It repeatedly trains simple models and weights them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The first output contains the re- and over-sampled dataset, rows that have been predicted wrong are contained more often than correctly predicted rows.
Popular Successors
- MultiLayerPerceptron Predictor7 %
Naive Bayes Learner5 %- Weka Predictor (3.7)4 %
Naive Bayes Predictor3 %Gradient Boosted Trees Learner3 %- Partitioning3 %
Gradient Boosted Trees Learner (Regression)3 %Random Forest Learner3 %- Polynomial Regression Learner2 %
- Boosting Predictor Loop Start2 %
- Naive Bayes Learner2 %
- Gradient Boosted Trees Predictor2 %
Random Forest Predictor2 %- Decision Tree Learner2 %
- Decision Tree Predictor2 %
- RProp MLP Learner2 %
Tree Ensemble Predictor2 %- Tree Ensemble Predictor2 %
- Random Forest Learner (Regression)2 %
- Boosting Learner Loop End2 %
- Naive Bayes Predictor1 %
- Linear Regression Learner1 %
- Gradient Boosted Trees Learner1 %
- Tree Ensemble Learner (Regression)1 %
- Random Forest Learner1 %
- Random Forest Predictor1 %
- Random Forest Predictor (Regression)1 %
- Model Loop Start1 %
- Boosting Predictor Loop End1 %
Regression Predictor1 %Gradient Boosted Trees Predictor1 %- Simple Regression Tree Learner1 %
Loop End< 1 %- X-Partitioner< 1 %
- PNN Predictor< 1 %
Scorer< 1 %- SMOTE< 1 %
- SVM Predictor< 1 %
Tree Ensemble Predictor< 1 %Random Forest Learner (Regression)< 1 %Random Forest Predictor< 1 %Gradient Boosted Trees Predictor< 1 %Tree Ensemble Learner< 1 %- Concatenate< 1 %
- Bootstrap Sampling< 1 %
- DL4J Feedforward Learner (Classification) (legacy)< 1 %
- Fuzzy Rule Predictor< 1 %
- PNN Learner (DDA)< 1 %
- k-Means< 1 %
- Logistic Regression Predictor< 1 %
- Column Filter< 1 %
XGBoost Tree Ensemble Learner< 1 %- Active Learn Loop Start (Legacy)< 1 %
- ARIMA Learner< 1 %
Table Row to Variable< 1 %Database Looping< 1 %- CSV Reader< 1 %
- Table Writer< 1 %
- Java Edit Variable< 1 %
- Feature Selection Loop Start (1:1)< 1 %
Naive Bayes Learner< 1 %- Fuzzy Rule Learner< 1 %
Logistic Regression Learner< 1 %- Logistic Regression Learner< 1 %
- Regression Predictor< 1 %
- SOTA Predictor< 1 %
- Association Rule Learner< 1 %
- SVM Learner< 1 %
Random Forest Predictor (Regression)< 1 %Simple Regression Tree Learner< 1 %- Gradient Boosted Trees Learner (Regression)< 1 %
- PMML Gradient Boosted Trees Predictor< 1 %
- Gradient Boosted Trees Predictor (Regression)< 1 %
- Tree Ensemble Learner< 1 %
- Tree Ensemble Predictor (Regression)< 1 %
Number To String< 1 %Linear Correlation< 1 %- Double to Integer< 1 %
- Reference Column Filter< 1 %
Row Splitter (deprecated)< 1 %Joiner< 1 %- Normalizer< 1 %
- Pivot< 1 %
- Normalizer Apply (PMML)< 1 %
- RowID< 1 %
- Rank Correlation< 1 %
- Statistics< 1 %
- Interactive Pie chart (legacy)< 1 %
- Distance Matrix Calculate< 1 %
- Keras Network Learner< 1 %
- Boosting Learner Loop Start< 1 %
- Table to PMML Ensemble< 1 %
- PMML Predictor< 1 %
- Prediction Fusion< 1 %
- Cell to Model< 1 %
- Voting Loop End< 1 %
- Table to H2O< 1 %
- Association Rule Learner (Borgelt)< 1 %
- Math Formula< 1 %
- LIBSVMLearner< 1 %
Excel Sheet Appender (XLS)< 1 %- R Snippet< 1 %
XGBoost Linear Ensemble Learner< 1 %- XPath< 1 %
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Workflows
02_Scaling_Analytics_w_BigDataKNIME Hub- AI_project_Group2KNIME Hub
- BoostingKNIME Hub
- Group_5_AI_CleanKNIME Hub
- Group_5_AI_Clean_LucasKNIME Hub
- Show all 13 workflows
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