Applies regression from a random forest* model by using the mean of the
individual predictions.
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
Options
Popular Predecessors
- Random Forest Learner (Regression)34 %
- Partitioning12 %
- Random Forest Learner5 %
Random Forest Learner3 %Excel Reader (XLS)2 %- Column Filter2 %
- X-Partitioner2 %
Excel Reader (XLS)2 %- Normalizer (Apply)1 %
- Model Reader1 %
String To Number1 %- Number to String< 1 %
Random Forest Predictor< 1 %Row Splitter (deprecated)< 1 %- Conformal Partitioning< 1 %
- Fingerprints Expander< 1 %
- Table Creator< 1 %
Row Filter (deprecated)< 1 %- Reference Row Filter< 1 %
- Random Forest Predictor (Regression)< 1 %
- Lag Column< 1 %
- Excel Reader< 1 %
- Concatenate< 1 %
- Category to Number< 1 %
- Column Splitter< 1 %
- Linear Regression Learner< 1 %
- Regression Predictor< 1 %
- CSV Reader< 1 %
- PCA< 1 %
- X-Aggregator< 1 %
- k-Means< 1 %
Number To String< 1 %- Normalizer< 1 %
- Shuffle< 1 %
- Numeric Outliers< 1 %
- Model Loop Start< 1 %
- Date&Time-based Row Filter< 1 %
- Table Reader< 1 %
- Duplicate Row Filter< 1 %
- Double to Integer< 1 %
- Loop End< 1 %
PCA Apply< 1 %Tree Ensemble Learner< 1 %- Simple Regression Tree Predictor< 1 %
Column Appender< 1 %- Cross Joiner< 1 %
- Domain Calculator< 1 %
- Equal Size Sampling< 1 %
- GroupBy< 1 %
Joiner< 1 %- Table Transposer< 1 %
- Rule-based Row Splitter< 1 %
Excel Reader (XLS)< 1 %- Fingerprints< 1 %
- Feature Selection Loop Start (2:2)< 1 %
- Linear Correlation< 1 %
- Joiner< 1 %
- Table Manipulator< 1 %
- PMML Transformation Apply< 1 %
CSV Reader< 1 %File Reader< 1 %Model Reader< 1 %- Tree Ensemble Learner (Regression)< 1 %
- Category to Number (Apply)< 1 %
- One to Many< 1 %
- Correlation Filter< 1 %
- Reference Column Filter< 1 %
- Denormalizer< 1 %
- Missing Value (Apply)< 1 %
- Missing Value< 1 %
Column Rename< 1 %- Rule-based Row Filter< 1 %
- Rule Engine< 1 %
- Boosting Learner Loop Start< 1 %
- Cell to Model< 1 %
- Math Formula< 1 %
- RDKit Descriptor Calculation< 1 %
- Group Loop Start< 1 %
- PCA Apply< 1 %
- Tree Ensemble Predictor< 1 %
- Random Forest Predictor< 1 %
- Expand Bit Vector< 1 %
- String to Number< 1 %
- Constant Value Column Filter< 1 %
- String Manipulation< 1 %
- Interactive Table (legacy)< 1 %
- RDKit Fingerprint< 1 %
- File Reader< 1 %
- Numeric Scorer< 1 %
- Column Appender< 1 %
- SHAP Loop Start< 1 %
- Shapley Values Loop Start< 1 %
- Date&Time Part Extractor< 1 %
- Moving Aggregator< 1 %
- Scorer< 1 %
- SMOTE< 1 %
- Bootstrap Sampling< 1 %
- Pivot< 1 %
- Java Snippet (simple)< 1 %
Model Writer< 1 %- Tree Ensemble Learner< 1 %
- Statistics< 1 %
- Empty Table Switch< 1 %
- H2O MOJO Predictor (Regression)< 1 %
- Math Formula (Multi Column)< 1 %
- Date Field Extractor (legacy)< 1 %
- Top k Row Filter< 1 %
- Rule Engine Variable< 1 %
- Python Script (legacy)< 1 %
- Inject Variables (Data)< 1 %
- Feature Selection Filter< 1 %
- Feature Selection Loop Start (1:1)< 1 %
Numeric Scorer< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
- Number Rounder< 1 %
CASE Switch Data (End)< 1 %- PMML Ensemble Predictor< 1 %
- Column Expressions< 1 %
- Date&Time Shift< 1 %
- Filter Apply Row Splitter< 1 %
- RowID< 1 %
- ARFF Reader< 1 %
Database Reader< 1 %- PNN Predictor< 1 %
Regression Predictor< 1 %- Create Bit Vector< 1 %
- Column Comparator< 1 %
Linear Correlation< 1 %- Reference Row Splitter< 1 %
- Low Variance Filter< 1 %
- IF Switch< 1 %
Read MDB< 1 %- Automated Matched Pairs< 1 %
Loop End< 1 %Logistic Regression Learner< 1 %- Extract Table Spec< 1 %
- DB Reader< 1 %
- Boosting Predictor Loop Start< 1 %
- Parameter Optimization Loop Start< 1 %
- Extract Time Window (legacy)< 1 %
- Column Renamer (Dictionary)< 1 %
- Column Resorter< 1 %
- Filter Apply< 1 %
- Counter Generation< 1 %
- JSON to Table< 1 %
IF Switch (Flow Variable Value)< 1 %Variable to Table Row< 1 %Table Reader< 1 %- Java Snippet< 1 %
Naive Bayes Predictor< 1 %PCA< 1 %- Column Auto Type Cast< 1 %
- Constant Value Column< 1 %
- HiLite Row Splitter< 1 %
- Nominal Value Row Filter< 1 %
- Numeric Row Splitter< 1 %
- Sorter< 1 %
- Weka Predictor (3.7)< 1 %
- R Snippet< 1 %
- RDKit Diversity Picker< 1 %
- Fingerprint From String< 1 %
- Fingerprint CONCATENATE< 1 %
- Create File Name (legacy)< 1 %
Line Reader< 1 %Counting Loop Start< 1 %- Column List Loop Start< 1 %
Recursive Loop Start (2 ports)< 1 %Recursive Loop Start< 1 %- Cluster Assigner< 1 %
- Decision Tree Predictor< 1 %
Scorer< 1 %- Random Forest Distance< 1 %
- Tree Ensemble Model Extract< 1 %
Tree Ensemble Predictor< 1 %- Tree Ensemble Predictor (Regression)< 1 %
- Auto-Binner (Apply)< 1 %
- Auto-Binner< 1 %
Cell Splitter< 1 %- Missing Value Column Filter< 1 %
- Row Sampling< 1 %
- String Replacer< 1 %
- Target Shuffling< 1 %
- Rule Engine (Dictionary)< 1 %
CASE Switch Model (Start)< 1 %- End IF< 1 %
CASE Switch Data (Start)< 1 %- Data Generator< 1 %
Color Appender< 1 %- Color Manager< 1 %
- Spark to Table< 1 %
- Capture Workflow Start< 1 %
- DB Query Reader< 1 %
- Boosting Learner Loop End< 1 %
- Boosting Predictor Loop End< 1 %
- PMML Ensemble to Table< 1 %
- PMML to Cell< 1 %
- H2O MOJO Predictor (Dimension Reduction)< 1 %
- Table View (JavaScript)< 1 %
- Table Editor (JavaScript)< 1 %
- Date&Time Difference< 1 %
- Time Difference (legacy)< 1 %
- Time Generator (legacy)< 1 %
- Moving Average< 1 %
- RDKit Count-Based Fingerprint< 1 %
- Expand Byte Vector< 1 %
- Row Splitter (Labs)< 1 %
- Normalizer (PMML)< 1 %
- Data Explorer< 1 %
- Molecule Type Cast< 1 %
- Stresser< 1 %
- Prediction Fusion< 1 %
- Java Edit Variable (simple)< 1 %
- List Files/Folders< 1 %
- Boolean Configuration< 1 %
- Boolean Input (legacy)< 1 %
- Container Input (Table)< 1 %
Python Script (1⇒2)< 1 %- Python View (legacy)< 1 %
- Window Loop Start< 1 %
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