This is the end of a feature selection loop which selects features according to a feature selection strategy. It allows you to select a flow variable that contains a prediction score (usually from a preceding Scorer node). You then need to specify whether the score variable should be minimized or maximized.
Options
Popular Predecessors
- Numeric Scorer36 %
- Scorer33 %
Numeric Scorer6 %Table Column to Variable6 %- Scorer (JavaScript)2 %
- Table Row to Variable1 %
- CSV Writer< 1 %
- Table Column to Variable< 1 %
- Feature Selection Loop Start (1:1)< 1 %
Table Row to Variable< 1 %CSV Writer< 1 %- Interactive Table (legacy)< 1 %
- Regression Predictor< 1 %
- Math Formula< 1 %
Naive Bayes Predictor< 1 %- Scatter Plot (JavaScript)< 1 %
- H2O Numeric Scorer< 1 %
- Math Formula (Variable)< 1 %
- Random Forest Predictor< 1 %
- Double to Integer< 1 %
- ROC Curve (legacy)< 1 %
- Parameter Optimization Loop End< 1 %
Table Column to Variable< 1 %- H2O Multinomial Scorer< 1 %
- Extract Variables (Data)< 1 %
- Decision Tree Predictor< 1 %
- RProp MLP Learner< 1 %
- Logistic Regression Predictor< 1 %
Scorer< 1 %- Row Filter< 1 %
- ROC Curve (JavaScript)< 1 %
Variable Loop End< 1 %- Entropy Scorer< 1 %
Merge Variables< 1 %- Naive Bayes Predictor< 1 %
- Silhouette Coefficient< 1 %
- Variable Loop End< 1 %
- Logistic Regression Learner< 1 %
- GroupBy< 1 %
Regression Predictor< 1 %- Column Combiner< 1 %
- Lift Chart (legacy)< 1 %
- Java Snippet< 1 %
- Feature Selection Filter< 1 %
- Feature Selection Loop Start (2:2)< 1 %
Gradient Boosted Trees Learner (Regression)< 1 %- Column Auto Type Cast< 1 %
- Column Selection (legacy)< 1 %
Table Row to Variable< 1 %- X-Aggregator< 1 %
- Correlation Filter< 1 %
- Rule Engine Variable< 1 %
- Java Edit Variable (simple)< 1 %
- Weka Predictor (3.7)< 1 %
Table Column to Variable< 1 %- SVM Predictor< 1 %
- Random Forest Predictor (Regression)< 1 %
- Linear Correlation< 1 %
- Normalizer< 1 %
- Partitioning< 1 %
- RowID< 1 %
- Rank Correlation< 1 %
- Single sample t-test< 1 %
- Scatter Matrix (legacy)< 1 %
- Spark Scorer< 1 %
- Math Formula (Multi Column)< 1 %
- HeatMap (JFreeChart)< 1 %
- Value Selection (legacy)< 1 %
- Table Row to Variable Loop Start< 1 %
- Wait...< 1 %
- Image to Table< 1 %
File Reader< 1 %- Java Edit Variable< 1 %
- Decision Tree Learner< 1 %
- Linear Regression Learner< 1 %
Linear Correlation< 1 %- Column Filter< 1 %
- Table Transposer< 1 %
- Value Counter< 1 %
- Extract Table Dimension< 1 %
H2O Binomial Scorer< 1 %- Table Row To Variable Run-for-time Loop Start< 1 %
- Benchmark Start (Flow Variable)< 1 %
- URI Port to Variable< 1 %
- Catch Errors (Var Ports)< 1 %
Table Row To Variable Loop Start< 1 %- Table Creator< 1 %
- Feature Selection Loop End< 1 %
Loop End< 1 %- SMOTE< 1 %
Number To String< 1 %- Number to String< 1 %
String To Number< 1 %- Round Double< 1 %
- Sorter< 1 %
- String Manipulation (Variable)< 1 %
- Rule Engine< 1 %
- Paired t-test< 1 %
- Statistics< 1 %
- Numeric Distances< 1 %
- Explorer Browser< 1 %
- H2O Gradient Boosting Machine Learner (Regression)< 1 %
- Column Filter (legacy)< 1 %
- Double Input (legacy)< 1 %
- Table View (JavaScript)< 1 %
- Parameter Optimization Loop Start< 1 %
- R Learner< 1 %
- Time Generator (legacy)< 1 %
RMSE Calculator< 1 %- Naive Bayes Learner< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Domain Calculator< 1 %
- H2O Random Forest Learner (Regression)< 1 %
- H2O Binomial Scorer< 1 %
- StanfordNLP NE Scorer< 1 %
- Double Configuration< 1 %
- Lift Chart (JavaScript)< 1 %
- Binary Classification Inspector< 1 %
- R Snippet< 1 %
- Table to R< 1 %
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Workflows
01_AnalyticsKNIME Hub- 01_AnalyticsKNIME Hub
- 01_Bike_Restocking_Alert_Training_w_Feature_SelectionKNIME Hub
- 01_Feature_Elimination_Done_ManuallyKNIME Hub
- 01_Feature_Elimination_with_Naive_BayesKNIME Hub
- Show all 138 workflows
Links
Developers
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