Predicts the response using a regression model.
The node needs to be connected to a regression node model* and some
test data. It is only executable if the test data contains the columns
that are used by the learner model. This node appends a new column
to the input table containing the prediction for each row.
*You can use the Linear Regression Learner node or the Polynomial Regression Learner node to create regression models.
Options
Popular Predecessors
- Linear Regression Learner42 %
- Partitioning17 %
- Polynomial Regression Learner6 %
- Normalizer (Apply)3 %
- Excel Reader2 %
String To Number2 %- CSV Reader2 %
- Column Filter1 %
- Logistic Regression Learner< 1 %
- Table Creator< 1 %
- X-Partitioner< 1 %
- File Reader< 1 %
Numeric Scorer< 1 %Row Filter (deprecated)< 1 %- String to Number< 1 %
Linear Correlation< 1 %Number To String< 1 %- Math Formula< 1 %
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- PMML Reader< 1 %
- Normalizer< 1 %
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- Missing Value< 1 %
- Model Reader< 1 %
- Category to Number< 1 %
Row Splitter (deprecated)< 1 %- Joiner< 1 %
Database Reader< 1 %PCA Apply< 1 %- Python Script (legacy)< 1 %
- Line Plot (legacy)< 1 %
- Linear Correlation< 1 %
- Scorer< 1 %
- Category to Number (Apply)< 1 %
- Reference Row Filter< 1 %
- Rule Engine< 1 %
- Counting Loop Start< 1 %
- Column Appender< 1 %
- Decision Tree Learner< 1 %
- Regression Predictor< 1 %
- Correlation Filter< 1 %
- RProp MLP Learner< 1 %
- Nominal Value Row Splitter< 1 %
Column Rename< 1 %- String Replacer< 1 %
- Numeric Scorer< 1 %
- Duplicate Row Filter< 1 %
- CSV Writer< 1 %
- Concatenate< 1 %
- Numeric Binner< 1 %
- Cell Splitter< 1 %
- Pivot< 1 %
- Missing Value (Apply)< 1 %
String To Number (PMML)< 1 %Excel Reader (XLS)< 1 %- Scatter Plot (JavaScript)< 1 %
CSV Reader< 1 %- String Manipulation< 1 %
- Model Loop Start< 1 %
- Outlier Removal< 1 %
- String to Number (PMML)< 1 %
- PMML Transformation Apply< 1 %
- Moving Aggregator< 1 %
Variable to Table Row< 1 %- Group Loop Start< 1 %
- GroupBy< 1 %
Variable to Table Row< 1 %- Table Transposer< 1 %
- Numeric Outliers (Apply)< 1 %
- Statistics< 1 %
- Simple Regression Tree Predictor< 1 %
- Feature Selection Filter< 1 %
- Column List Loop Start< 1 %
Recursive Loop Start< 1 %- File Reader (Complex Format)< 1 %
- Number to String (PMML)< 1 %
- ARFF Reader< 1 %
File Reader< 1 %PMML Reader< 1 %PCA< 1 %- Logistic Regression Predictor< 1 %
- Normalizer Apply (PMML)< 1 %
- Sorter< 1 %
- RDKit Descriptor Calculation< 1 %
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- Loop End (Column Append)< 1 %
- SMOTE< 1 %
- PCA Apply< 1 %
- PCA< 1 %
- Simple Regression Tree to PMML< 1 %
- Number to String< 1 %
- Domain Calculator< 1 %
- Numeric Row Splitter< 1 %
- Numeric Outliers< 1 %
- Table Editor (JavaScript)< 1 %
Python Script (1⇒1)< 1 %- Table Reader< 1 %
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- Capture Workflow Start< 1 %
- Double Input (legacy)< 1 %
Table Reader< 1 %- Feature Selection Loop Start (1:1)< 1 %
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Joiner< 1 %- Denormalizer< 1 %
- Normalizer (PMML)< 1 %
- RowID< 1 %
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CASE Switch Data (Start)< 1 %- Spark to Table< 1 %
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- Column Expressions (legacy)< 1 %
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- Python Source (legacy)< 1 %
- Date&Time Difference< 1 %
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- Table Validator (Reference)< 1 %
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- Loop End< 1 %
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- Lift Chart (legacy)< 1 %
- Recursive Loop Start< 1 %
Model Reader< 1 %Concatenate (Optional in)< 1 %- Cell to PMML< 1 %
Extract Date&Time Fields< 1 %Scorer< 1 %- Number to Category (Apply)< 1 %
- Row Filter (Labs)< 1 %
- Rank< 1 %
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Read Excel Sheet Names (XLS)< 1 %- Moving Average< 1 %
CASE Switch Model (Start)< 1 %- MultiLayerPerceptron Predictor< 1 %
- Column Auto Type Cast< 1 %
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- Table Row to Variable Loop Start< 1 %
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Excel Reader (XLS)< 1 %Table Row to Variable< 1 %- Naive Bayes Predictor< 1 %
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- Simple Regression Tree Learner< 1 %
- Column Aggregator< 1 %
- Column Renamer (Dictionary)< 1 %
- Table Validator< 1 %
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- Subset Matcher< 1 %
- Denormalizer (PMML)< 1 %
- End IF< 1 %
- Column Filter (legacy)< 1 %
- Decision Tree View (JavaScript)< 1 %
- Container Input (Table)< 1 %
Python Script (2⇒1)< 1 %- Disturber Node< 1 %
- Date Field Extractor (legacy)< 1 %
- PMML Writer< 1 %
- Filter Apply< 1 %
- Linear Discriminant Analysis Inversion< 1 %
- Scatter Matrix (legacy)< 1 %
- ROC Curve (legacy)< 1 %
Loop End< 1 %- Association Rule Learner< 1 %
- Tree Ensemble Predictor (Regression)< 1 %
Cell Splitter< 1 %Column Appender< 1 %- Constant Value Column Filter< 1 %
Merge Variables< 1 %Color Appender< 1 %- Date/Time Shift (legacy)< 1 %
- Extract Time Window (legacy)< 1 %
- Molecule to CDK< 1 %
- Random Numbers Generator< 1 %
- Fingerprints Expander< 1 %
- Breakpoint< 1 %
Variable to Table Column< 1 %Table Row to Variable< 1 %Table Row To Variable Loop Start< 1 %Line Reader< 1 %Line Reader< 1 %Loop End (2 ports)< 1 %- Chunk Loop Start< 1 %
Recursive Loop End< 1 %Naive Bayes Learner< 1 %- PNN Learner (DDA)< 1 %
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K Nearest Neighbor< 1 %- MDS< 1 %
Gradient Boosted Trees Learner (Regression)< 1 %Gradient Boosted Trees Predictor< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
Random Forest Predictor< 1 %- Auto-Binner (Apply)< 1 %
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String Replace (Dictionary)< 1 %- Target Shuffling< 1 %
- Ruleset Predictor< 1 %
Rank Correlation< 1 %- Standardized Cronbach Alpha< 1 %
- One-way ANOVA< 1 %
- Linear Discriminant Analysis< 1 %
CASE Switch Variable (End)< 1 %- Cache< 1 %
- Extract Table Dimension< 1 %
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- Capture Workflow End< 1 %
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- One Row to Many< 1 %
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- Table to PMML Ensemble< 1 %
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- PMML Predictor< 1 %
- H2O MOJO Predictor (Regression)< 1 %
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- Weka Cluster Assigner (3.7)< 1 %
- Google Sheets Reader< 1 %
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Python Script (1⇒2)< 1 %Python Script (2⇒2)< 1 %- Date&Time to String< 1 %
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- Hierarchical Cluster Assigner (local)< 1 %
- Gradient Boosted Trees to PMML< 1 %
- Tree Ensemble Predictor< 1 %
- PMML Simple Regression Tree Predictor< 1 %
- Column Combiner< 1 %
- Number Rounder< 1 %
- Single sample t-test< 1 %
- Scatter Plot (legacy)< 1 %
- Interactive Table (legacy)< 1 %
Parquet Reader< 1 %- Stresser< 1 %
- Time Series Generator< 1 %
- Prediction Fusion< 1 %
- Read Excel Sheet Names< 1 %
- Java Snippet (simple)< 1 %
- String Matcher< 1 %
- Weka Cluster Assigner (3.6) (legacy)< 1 %
- JPMML Cluster Assigner< 1 %
- JSON to Table< 1 %
- String to Date/Time (legacy)< 1 %
- XPath< 1 %
RDKit Structure Normalizer< 1 %
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Workflows
[ISPRENL S11] Group 5 MCO2 WorkflowKNIME Hub- 0. Lubes_Sales_Pred-ModelNodePit Space
- 0. Lubes_Sales_Pred-ModelNodePit Space
- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Hub
- 01_Accessing_Transforming_and_Modeling_Time_SeriesKNIME Hub
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