This node uses a SVM model generated by the SVM learner node to predict the output for given values. Only the columns that have been used during training are used in the SVM learner. The output table will contain one extra column, which contains the prediction and optinally the class probabilities.
Options
Popular Predecessors
- SVM Learner40 %
- Partitioning11 %
PMML Reader4 %- Column Filter4 %
- Normalizer (Apply)3 %
- X-Partitioner3 %
Number To String3 %Scorer2 %Model Reader2 %- CSV Reader1 %
String To Number1 %PCA Apply< 1 %- Normalizer< 1 %
- One to Many< 1 %
- Missing Value (Apply)< 1 %
- Boosting Predictor Loop Start< 1 %
- File Reader< 1 %
- Table Creator< 1 %
- PMML Reader< 1 %
- Reference Row Filter< 1 %
Excel Reader (XLS)< 1 %- PCA< 1 %
- Document Vector Applier< 1 %
- Model Reader< 1 %
Column Appender< 1 %- ARFF Reader< 1 %
File Reader< 1 %- Normalizer Apply (PMML)< 1 %
- RDKit Descriptor Calculation< 1 %
- Denormalizer< 1 %
- Table View (JavaScript)< 1 %
- String to Number< 1 %
- ROC Curve (JavaScript)< 1 %
- SVM Predictor< 1 %
- Bootstrap Sampling< 1 %
- Domain Calculator< 1 %
Column Rename< 1 %- Row Sampling< 1 %
- Cell to Model< 1 %
XLS Reader< 1 %- Logistic Regression Learner< 1 %
- Random Forest Predictor< 1 %
Excel Reader (XLS)< 1 %- CSV Writer< 1 %
- k-Means< 1 %
Row Filter (deprecated)< 1 %- Scatter Plot (JavaScript)< 1 %
CSV Reader< 1 %- Decision Tree Learner< 1 %
- MultiLayerPerceptron Predictor< 1 %
- RProp MLP Learner< 1 %
- Linear Regression Learner< 1 %
- Auto-Binner< 1 %
- Category to Number< 1 %
- Equal Size Sampling< 1 %
- Nominal Value Row Filter< 1 %
Row Splitter (deprecated)< 1 %- GroupBy< 1 %
- Missing Value< 1 %
- Normalizer (PMML)< 1 %
- Shuffle< 1 %
- Rule Engine< 1 %
Color Appender< 1 %- LIBSVMPredictor< 1 %
Database Reader< 1 %Naive Bayes Learner< 1 %- Naive Bayes Learner< 1 %
- PNN Predictor< 1 %
- Number to String< 1 %
- Scorer< 1 %
- PCA Apply< 1 %
- Joiner< 1 %
- Excel Reader< 1 %
- File Reader (Complex Format)< 1 %
- XGBoost Predictor< 1 %
- Model Loop End< 1 %
- Loop End< 1 %
- Outlier Removal< 1 %
- PNN Learner (DDA)< 1 %
- Numeric Binner< 1 %
- Missing Value Column Filter< 1 %
- Logistic Regression Predictor< 1 %
- String Manipulation (Multi Column)< 1 %
- X-Aggregator< 1 %
- Concatenate< 1 %
- Constant Value Column< 1 %
Linear Correlation< 1 %- Model Loop Start< 1 %
- Document Vector< 1 %
- Number to Category (Apply)< 1 %
- Column Rename (Regex)< 1 %
- Color Manager< 1 %
- Scatter Plot (Plotly)< 1 %
- Boosting Learner Loop Start< 1 %
- Fingerprint to String< 1 %
- Counting Loop Start< 1 %
- Naive Bayes Predictor< 1 %
- Expand Bit Vector< 1 %
- Target Shuffling< 1 %
CSV Writer< 1 %Table Reader< 1 %- SMOTE< 1 %
- Reference Column Filter< 1 %
- Shape Manager< 1 %
- Category to Class< 1 %
- R Snippet< 1 %
Gradient Boosted Trees Predictor< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
- Tree Ensemble Learner< 1 %
- Double to Integer< 1 %
- Column Filter (PMML)< 1 %
- Number to String (PMML)< 1 %
- RowID< 1 %
Merge Variables< 1 %Document vector< 1 %- Column Filter (legacy)< 1 %
- Date&Time Difference< 1 %
- Feature Selection Filter< 1 %
- Random Forest Learner< 1 %
- String Replacer< 1 %
- Top k Row Filter< 1 %
- Rule-based Row Splitter< 1 %
- Data Explorer< 1 %
- DF< 1 %
- Document Vector Hashing< 1 %
Database Connection Table Reader< 1 %Recursive Loop Start< 1 %- Decision Tree Predictor< 1 %
K Nearest Neighbor< 1 %- MDS< 1 %
- Column Combiner< 1 %
- Category to Number (Apply)< 1 %
- Correlation Filter< 1 %
Joiner< 1 %- Column Splitter< 1 %
- Statistics< 1 %
- IF Switch< 1 %
- PMML Transformation Apply< 1 %
- Stresser< 1 %
Excel Reader (XLS)< 1 %- Vocabulary Extractor (legacy)< 1 %
- Fingerprints Expander< 1 %
- Active Learn Loop Start (Legacy)< 1 %
Active Learn Loop Start< 1 %- CSV Array Reader< 1 %
- Java Snippet< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Chunk Loop Start< 1 %
Naive Bayes Predictor< 1 %- Fuzzy Rule Predictor< 1 %
- Fuzzy c-Means< 1 %
- K Nearest Neighbor< 1 %
- Polynomial Regression Learner< 1 %
- Regression Predictor< 1 %
- SOTA Predictor< 1 %
Gradient Boosted Trees Predictor< 1 %- Random Forest Learner (Regression)< 1 %
- Random Forest Predictor (Regression)< 1 %
- Auto-Binner (Apply)< 1 %
- Create Bit Vector< 1 %
- Case Converter< 1 %
- Many to One< 1 %
- Table Validator< 1 %
- Reference Column Splitter< 1 %
- Filter Apply Row Splitter< 1 %
- Low Variance Filter< 1 %
- Pivot< 1 %
Number To String (PMML)< 1 %- String to Number (PMML)< 1 %
- Rank< 1 %
- Number Rounder< 1 %
- Numeric Row Splitter< 1 %
- String Manipulation (Variable)< 1 %
- Value Counter< 1 %
- Rule-based Row Filter< 1 %
- Numeric Outliers (Apply)< 1 %
- Empty Table Switch< 1 %
- Box Plot (legacy)< 1 %
- ROC Curve (legacy)< 1 %
- Parallel Chunk Start< 1 %
- DB Reader< 1 %
- Boosting Learner Loop End< 1 %
PMML Predictor< 1 %- Prediction Fusion< 1 %
- Math Formula< 1 %
- Math Formula (Multi Column)< 1 %
Read Excel Sheet Names (XLS)< 1 %- Case Converter< 1 %
- Decision Tree View (JavaScript)< 1 %
- Line Plot (JavaScript)< 1 %
- Container Output (Table)< 1 %
- Binary Object to Model< 1 %
- Model to Binary Object< 1 %
- Catch Errors (Generic Ports)< 1 %
- Table Row to Variable< 1 %
- Model Writer< 1 %
- Table Reader< 1 %
Counting Loop Start< 1 %- Group Loop Start< 1 %
Recursive Loop Start (2 ports)< 1 %- Cluster Assigner< 1 %
- Column Name Extractor< 1 %
- Column Resorter< 1 %
- String Manipulation< 1 %
- Table Transposer< 1 %
- Numeric Outliers< 1 %
- One-way ANOVA< 1 %
CASE Switch Data (Start)< 1 %- Java Snippet (simple)< 1 %
- Chi-Square Keyword Extractor< 1 %
- Date&Time to String< 1 %
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Workflows
Credit Scoring Model(diksha kumari)KNIME Hub- (test) credit score modelKNIME Hub
- 01_Combining_Classifiers_using_Prediction_FusionKNIME Hub
- 01_CreditScoringKNIME Hub
- 01_CreditScoringKNIME Hub
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