This node takes a model built with a feature selection loop as input and lets you choose the subset of columns you want to include in the output table. The dialog shows all computed subsets together with their scores. You can select a subset manually or specify a score threshold. If you specify a threshold, the smallest feature set whose score meets the threshold requirement is selected. If the "Minimize Score" option was checked in the Feature Selection Loop End node, this is the smallest feature set with a score below the specified threshold; if the "Minimize Score" option was left unchecked in the Feature Selection Loop End node, this is the smallest feature set with a score above the specified threshold.
Options
Popular Successors
Scorer6 %Column Appender3 %- Correlation Filter2 %
Linear Correlation2 %- Table Partitioner2 %
Joiner2 %Excel Writer (XLS)2 %- Value Selection (legacy)2 %
- Logistic Regression Learner1 %
- Shuffle1 %
- Table View1 %
- Naive Bayes Learner1 %
- Naive Bayes Predictor1 %
Gradient Boosted Trees Learner1 %- Random Forest Predictor1 %
- Reference Column Filter1 %
- Numeric Outliers1 %
- Scatter Plot (JavaScript) (legacy)1 %
Naive Bayes Predictor1 %- MultiLayerPerceptron Predictor1 %
- SMOTE1 %
- Random Forest Learner1 %
- One to Many1 %
- Column Filter1 %
- Joiner1 %
- Sorter1 %
- Line Plot (JavaScript) (legacy)1 %
- CSV Writer< 1 %
- Table Writer< 1 %
- Feature Selection Filter< 1 %
- Feature Selection Loop End< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Feature Selection Loop Start (2:2)< 1 %
- X-Partitioner< 1 %
k-Means (deprecated)< 1 %- Decision Tree Learner< 1 %
- Decision Tree Predictor< 1 %
- Linear Regression Learner< 1 %
- Polynomial Regression Learner< 1 %
- Regression Predictor< 1 %
- Scorer< 1 %
- SVM Learner< 1 %
- Gradient Boosted Trees Learner (Regression)< 1 %
- Tree Ensemble Learner< 1 %
- Random Forest Learner (Regression)< 1 %
- Simple Regression Tree Learner< 1 %
- Concatenate< 1 %
- Number to String< 1 %
- Column Appender< 1 %
- Equal Size Sampling< 1 %
- Low Variance Filter< 1 %
- Normalizer< 1 %
Column Rename< 1 %- Scatter Plot (legacy)< 1 %
Color Manager< 1 %- Table to H2O< 1 %
- Excel Writer< 1 %
- Weka Predictor (3.7)< 1 %
- Table View (JavaScript) (legacy)< 1 %
- PCA< 1 %
Number To String< 1 %Row Filter (deprecated)< 1 %- ROC Curve (legacy)< 1 %
Table Row to Variable< 1 %- RProp MLP Learner< 1 %
- Gradient Boosted Trees Learner< 1 %
- Random Forest Predictor (Regression)< 1 %
- Normalizer (Apply)< 1 %
- Missing Value< 1 %
- Row Sampler< 1 %
- Statistics< 1 %
- End IF< 1 %
Variable to Table Column< 1 %- Extract Variables (Data)< 1 %
CSV Writer< 1 %Loop End< 1 %Counting Loop Start< 1 %K Nearest Neighbor< 1 %- SVM Predictor< 1 %
- Number to String (PMML)< 1 %
- Rule Engine< 1 %
- Cronbach Alpha< 1 %
- Line Plot (legacy)< 1 %
Excel Sheet Appender (XLS)< 1 %Excel Writer (XLS)< 1 %- Network Viewer< 1 %
XGBoost Tree Ensemble Learner< 1 %- Open in Python< 1 %
- Inject Variables (Data)< 1 %
- Table Row to Variable< 1 %
- Variable to Table Row< 1 %
- ARFF Writer< 1 %
Table Writer< 1 %Loop End (2 ports)< 1 %Interval Loop Start< 1 %- Chunk Loop Start< 1 %
Variable Loop End< 1 %- X-Aggregator< 1 %
Naive Bayes Learner< 1 %- Fuzzy Rule Learner< 1 %
- PNN Learner (DDA)< 1 %
- DBSCAN< 1 %
- Decision Tree to Image< 1 %
- K Nearest Neighbor< 1 %
PCA< 1 %- Logistic Regression Predictor< 1 %
Regression Predictor< 1 %Numeric Scorer< 1 %- Numeric Scorer< 1 %
Gradient Boosted Trees Predictor< 1 %- Tree Ensemble Predictor< 1 %
Random Forest Learner< 1 %Concatenate (Optional in)< 1 %- Category to Number< 1 %
String To Number< 1 %- String to Number< 1 %
- Column Name Extractor< 1 %
- Column Merger< 1 %
Constant Value Column< 1 %- Cross Joiner< 1 %
- CAIM Binner< 1 %
Row Splitter (deprecated)< 1 %- Row Filter (Labs)< 1 %
- Reference Row Filter< 1 %
- Reference Row Splitter< 1 %
- GroupBy< 1 %
- Missing Value (Apply)< 1 %
- Rank< 1 %
- Reference Column Resorter< 1 %
Column Splitter< 1 %- Target Shuffling< 1 %
- Table Transposer< 1 %
- Value Counter< 1 %
- Rule-based Row Filter< 1 %
Rank Correlation< 1 %- Rank Correlation< 1 %
- Data Explorer< 1 %
- Independent groups t-test< 1 %
- Linear Discriminant Analysis< 1 %
- Cache< 1 %
- Extract Table Spec< 1 %
Merge Variables< 1 %- Timer Info< 1 %
- Interactive Histogram (legacy)< 1 %
- Interactive Table (legacy)< 1 %
- Capture Workflow End< 1 %
- Parallel Chunk End< 1 %
DB Writer< 1 %- String Distances< 1 %
- Distance Matrix Calculate< 1 %
- Keras Network Learner< 1 %
- Line Plot (Plotly)< 1 %
- DL4J Feedforward Learner (Classification) (legacy)< 1 %
- Math Formula< 1 %
- Java Snippet Row Filter< 1 %
- TF< 1 %
- ROC Curve (JavaScript) (legacy)< 1 %
- Feature Table< 1 %
- Multiobjective Score Computation< 1 %
- Rowset Filter< 1 %
- Parameter Optimization Loop End< 1 %
- Score Erosion< 1 %
- Python Source (legacy)< 1 %
- Add Table to R< 1 %
Date&Time Difference< 1 %Date&Time-based Row Filter< 1 %XGBoost Tree Ensemble Learner (Regression)< 1 %
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Workflows
[03] F1 - Circuit GroupingsKNIME Hub- 01_AnalyticsKNIME Hub
- 01_AnalyticsKNIME Hub
- 01_Bike_Restocking_Alert_Training_w_Feature_SelectionKNIME Hub
- 01_Feature_Elimination_Done_ManuallyKNIME Hub
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