The Weka Predictor takes a model generated in a weka node and classifies the test data at the inport.
Options
Popular Predecessors
Weka Classifier Reader (3.7)7 %CSV Reader6 %Number To String5 %PCA Apply4 %- X-Partitioner3 %
String To Number3 %- Partitioning2 %
Excel Reader (XLS)2 %Scorer2 %- CSV Reader2 %
- Auto-Binner1 %
- Joiner1 %
- Normalizer (Apply)1 %
- Rule-based Row Filter1 %
- Row Sampling1 %
- Boosting Predictor Loop Start1 %
- Model Loop Start1 %
- Column Resorter1 %
- Model Reader1 %
- Bootstrap Sampling< 1 %
- Boosting Learner Loop Start< 1 %
Column Appender< 1 %- Missing Value Column Filter< 1 %
Color Appender< 1 %- Column Filter< 1 %
- Math Formula< 1 %
Naive Bayes Learner< 1 %XLS Reader< 1 %- Number to String< 1 %
- Scorer< 1 %
Backward Feature Elimination Start (1:1)< 1 %- Concatenate< 1 %
- Statistics< 1 %
Loop End< 1 %Naive Bayes Predictor< 1 %- String to Number< 1 %
- Normalizer Apply (PMML)< 1 %
- Excel Reader< 1 %
- ARFF Reader< 1 %
Counting Loop Start< 1 %- Group Loop Start< 1 %
K Nearest Neighbor< 1 %- One to Many< 1 %
- String Replacer< 1 %
- Rule-based Row Splitter< 1 %
- Weka Predictor (3.7)< 1 %
- GET Request< 1 %
- Constant Value Column Filter< 1 %
- Top k Row Filter< 1 %
- k-Means< 1 %
Random Forest Learner< 1 %Row Filter (deprecated)< 1 %- File Reader< 1 %
- Java Snippet< 1 %
- Chunk Loop Start< 1 %
- Cluster Assigner< 1 %
- Decision Tree Predictor< 1 %
- MultiLayerPerceptron Predictor< 1 %
- SVM Predictor< 1 %
- Category to Number (Apply)< 1 %
- Correlation Filter< 1 %
Row Splitter (deprecated)< 1 %Joiner< 1 %- Number Rounder< 1 %
- Data Generator< 1 %
- Random Forest Learner (Regression)< 1 %
- Table Creator< 1 %
- Feature Selection Filter< 1 %
- Linear Regression Learner< 1 %
- Reference Column Filter< 1 %
- Normalizer< 1 %
- Missing Value (Apply)< 1 %
- Missing Value< 1 %
Column Rename< 1 %- Color Manager< 1 %
- Cell to Model< 1 %
- Weka Cluster Assigner (3.7)< 1 %
- PCA Apply< 1 %
- Cross Joiner< 1 %
- Weka Classifier Reader (3.7)< 1 %
- Parallel Chunk Start< 1 %
- Capture Workflow Start< 1 %
Backward Feature Elimination Start (2:2)< 1 %Cell Replacer< 1 %- Column Name Extractor< 1 %
- CAIM Applier< 1 %
- Domain Calculator< 1 %
- GroupBy< 1 %
- Target Shuffling< 1 %
- Rule Engine< 1 %
- RDKit Descriptor Calculation< 1 %
- Edit Nominal Domain< 1 %
- Low Variance Filter< 1 %
- Value Counter< 1 %
- Linear Discriminant Analysis Apply< 1 %
Excel Reader (XLS)< 1 %Model Reader< 1 %- Polynomial Regression Learner< 1 %
Random Forest Learner< 1 %- Denormalizer< 1 %
- Save Workflow< 1 %
Excel Reader (XLS)< 1 %- R Snippet< 1 %
Variable to Table Row< 1 %Recursive Loop Start (2 ports)< 1 %Variable Loop End< 1 %- Regression Predictor< 1 %
- Decision Tree to Ruleset< 1 %
- Numeric Outliers (Apply)< 1 %
- Boosting Learner Loop End< 1 %
- Table to JSON< 1 %
- Variable to Table Row< 1 %
- Acoustic Feature Extractor< 1 %
Database Reader< 1 %File Reader< 1 %Table Reader< 1 %- Loop End (Column Append)< 1 %
Interval Loop Start< 1 %- Random Forest Learner< 1 %
- Numeric Binner< 1 %
- Category to Number< 1 %
- Numeric Binner (PMML)< 1 %
- Column Splitter< 1 %
- Cache< 1 %
- Molecular Properties< 1 %
- Catch Errors (Generic Ports)< 1 %
Variable to Table Column< 1 %Model Writer< 1 %PCA Compute< 1 %PCA< 1 %- Logistic Regression Learner< 1 %
- Numeric Scorer< 1 %
- Tree Ensemble Learner (Regression)< 1 %
- Random Forest Predictor (Regression)< 1 %
Double To Int< 1 %- HiLite Row Splitter< 1 %
- Normalizer (PMML)< 1 %
- K Nearest Neighbor (Distance Function)< 1 %
- Loop End< 1 %
- SHAP Loop Start< 1 %
- Outlier Removal< 1 %
Table Row To Variable Loop Start< 1 %- X-Aggregator< 1 %
- SMOTE< 1 %
Gradient Boosted Trees Predictor< 1 %- Auto-Binner (Apply)< 1 %
- Expand Bit Vector< 1 %
Cell Splitter< 1 %- Constant Value Column< 1 %
- Double to Integer< 1 %
- Equal Size Sampling< 1 %
- Reference Row Filter< 1 %
- Pivot< 1 %
- RowID< 1 %
- Shuffle< 1 %
- String Manipulation< 1 %
- ROC Curve (legacy)< 1 %
- SDF Reader< 1 %
PMML Predictor< 1 %- LIBSVMPredictor< 1 %
Document Data Extractor< 1 %Strings To Document< 1 %- Column Filter (legacy)< 1 %
- Parameter Optimization Loop Start< 1 %
Python Script (1⇒1)< 1 %- String to Date&Time< 1 %
- Fingerprints< 1 %
FreeGeoIP< 1 %- Compiled Model Predictor< 1 %
- Try (Data Ports)< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Feature Selection Loop Start (2:2)< 1 %
Recursive Loop Start< 1 %- Naive Bayes Predictor< 1 %
- PNN Predictor< 1 %
- Hierarchical Cluster Assigner (local)< 1 %
- K Nearest Neighbor< 1 %
- MDS< 1 %
Logistic Regression Learner< 1 %Regression Predictor< 1 %- PCA Inversion< 1 %
- Gradient Boosted Trees Predictor (Regression)< 1 %
- Column Aggregator< 1 %
- Column Renamer (Dictionary)< 1 %
Linear Correlation< 1 %- Extract Missing Value Cause< 1 %
- Nominal Value Row Filter< 1 %
- Nominal Value Row Splitter< 1 %
- Row Splitter (Labs)< 1 %
- Column Filter (PMML)< 1 %
Number To String (PMML)< 1 %- String to Number (PMML)< 1 %
- Ungroup< 1 %
- Rule Engine Variable< 1 %
- Ruleset Predictor< 1 %
Rank Correlation< 1 %- Linear Discriminant Analysis< 1 %
- IF Switch< 1 %
- Crosstab< 1 %
- Empty Table Creator< 1 %
- Random Number Assigner< 1 %
- H2O Local Context< 1 %
- Math Formula (Multi Column)< 1 %
Read Excel Sheet Names (XLS)< 1 %- Java Snippet Row Filter< 1 %
Bag of Words Creator< 1 %- Document Vector Applier< 1 %
- String to Term< 1 %
Weka Predictor< 1 %Date Input (legacy)< 1 %- Double Input (legacy)< 1 %
- String Input (legacy)< 1 %
- JSON Transformer< 1 %
Python Script (2⇒2)< 1 %- R Source (Table)< 1 %
- Disturber Node< 1 %
- Date&Time-based Row Filter< 1 %
RDKit Structure Normalizer< 1 %- Fingerprints Expander< 1 %
- Variable to Table Column< 1 %
- Lag Column< 1 %
- Reference Row Splitter< 1 %
- Number to String (PMML)< 1 %
- PMML Transformation Apply< 1 %
- Java Snippet (simple)< 1 %
- Weka Predictor (3.6) (legacy)< 1 %
- Table Editor (JavaScript)< 1 %
- Shapley Values Loop Start< 1 %
Precision Checker (Event Log)< 1 %
Views
- Weka Node View
- Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.
Workflows
01_Forest_Fire_PredictionKNIME Hub- 01. BasicsKNIME Hub
- 02. Evaluation MetricsKNIME Hub
- 03. X-ValidationKNIME Hub
- AD_AQP4_CS_VSKNIME Hub
- Show all 79 workflows
Links
- No links available
Developers
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