Learns Gradient Boosted Trees with the objective of classification. The algorithm uses very shallow regression trees and a special form of boosting to build an ensemble of trees. The implementation follows the algorithm in section 4.6 of the paper "Greedy Function Approximation: A Gradient Boosting Machine" by Jerome H. Friedman (1999). For more information you can also take a look at this.
The used base learner for this ensemble method is a simple regression tree as it is used in the Tree Ensemble , Random Forest and Simple Regression Tree nodes. Per default a tree is build using binary splits for numeric and nominal attributes (the later can be changed to multiway splits). The built-in missing value handling tries to find the best direction for missing values to go to by testing each possible direction and selecting the one yielding the best result (i.e. largest gain).
In a regression tree the predicted value for a leaf node is the mean target value of the records within the leaf. Hence the predictions are best (with respect to the training data) if the variance of target values within a leaf is minimal. This is achieved by splits that minimize the sum of squared errors in their respective children.
Sampling
This node allows to perform row sampling (bagging) and attribute sampling (attribute bagging) similar to the random forest* and tree ensemble nodes. If sampling is used this is usually referred to as Stochastic Gradient Boosted Trees. The respective settings can be found in the Advanced Options tab.
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
Options
Advanced Options
Popular Predecessors
- Partitioning16 %
- X-Partitioner4 %
- Parameter Optimization Loop Start4 %
Scorer3 %- Normalizer3 %
Table Row to Variable3 %- Column Filter2 %
- Correlation Filter2 %
- SMOTE2 %
Row Filter (deprecated)2 %- Equal Size Sampling2 %
- Scorer2 %
- Missing Value1 %
- Domain Calculator1 %
- Low Variance Filter1 %
- Random Forest Predictor1 %
- Concatenate1 %
- String to Number1 %
- Color Manager1 %
- Table Row to Variable1 %
- Merge Variables< 1 %
- PCA< 1 %
- Naive Bayes Learner< 1 %
- Denormalizer< 1 %
- Excel Reader< 1 %
- Row Sampling< 1 %
- Rule Engine< 1 %
- ROC Curve (legacy)< 1 %
Excel Reader (XLS)< 1 %- File Reader< 1 %
- Java Edit Variable< 1 %
- Double to Integer< 1 %
- RowID< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Decision Tree Predictor< 1 %
- Numeric Scorer< 1 %
- Gradient Boosted Trees Predictor< 1 %
- PMML Gradient Boosted Trees Predictor< 1 %
- Random Forest Learner< 1 %
- Simple Regression Tree Predictor< 1 %
- Auto-Binner< 1 %
- Bootstrap Sampling< 1 %
- Number to String< 1 %
- Missing Value Column Filter< 1 %
Row Splitter (deprecated)< 1 %- GroupBy< 1 %
- Boosting Learner Loop Start< 1 %
- Math Formula< 1 %
Excel Reader (XLS)< 1 %- Scorer (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
- Binary Classification Inspector< 1 %
- CSV Reader< 1 %
- Table Reader< 1 %
- Linear Correlation< 1 %
- Joiner< 1 %
Column Rename< 1 %- Table Transposer< 1 %
- Value Counter< 1 %
- Parameter Optimization Loop Start (Table)< 1 %
- Column Filter (legacy)< 1 %
- Table Splitter< 1 %
- Integer Configuration< 1 %
- Decision Tree View (JavaScript)< 1 %
Table Column to Variable< 1 %CSV Reader< 1 %Table Reader< 1 %- Feature Selection Filter< 1 %
- Chunk Loop Start< 1 %
- PNN Learner (DDA)< 1 %
- Tree Ensemble Learner< 1 %
- Category to Number< 1 %
- One to Many< 1 %
- Rule-based Row Filter< 1 %
- Rank Correlation< 1 %
- Numeric Outliers< 1 %
- Spark to Table< 1 %
- RDKit Fingerprint< 1 %
- Column Auto Type Cast< 1 %
- Table Creator< 1 %
- Gradient Boosted Trees Predictor (Regression)< 1 %
Concatenate (Optional in)< 1 %- Column Appender< 1 %
- Number to String (PMML)< 1 %
- Rule-based Row Splitter< 1 %
- Weak Label Model Predictor< 1 %
- File Reader (Complex Format)< 1 %
Loop End (2 ports)< 1 %- Column Filter (PMML)< 1 %
- R to Table< 1 %
Read MDB< 1 %- Read Variables< 1 %
- Split Collection Column< 1 %
Table Row To Variable Loop Start< 1 %Database Connection Table Reader< 1 %File Reader< 1 %Loop End< 1 %- X-Aggregator< 1 %
- Hierarchical Clustering< 1 %
- Decision Tree Learner< 1 %
- Logistic Regression Learner< 1 %
- Logistic Regression Predictor< 1 %
- SVM Learner< 1 %
- PCA Apply< 1 %
- Gradient Boosted Trees Learner< 1 %
- Tree Ensemble Predictor< 1 %
- Add Empty Rows< 1 %
Column Appender< 1 %Linear Correlation< 1 %- CAIM Binner< 1 %
- Edit Nominal Domain (Dictionary)< 1 %
- Nominal Value Row Filter< 1 %
Joiner< 1 %- Pivot< 1 %
- Normalizer (PMML)< 1 %
- Shuffle< 1 %
- Column Splitter< 1 %
- Ruleset Editor< 1 %
- Rule Engine (Dictionary)< 1 %
- Statistics< 1 %
CASE Switch Variable (Start)< 1 %- Extract Table Dimension< 1 %
- Data Generator< 1 %
- Parameterized DB Query Reader< 1 %
- Random Label Assigner< 1 %
- Keras Network Executor< 1 %
- H2O Multinomial Scorer< 1 %
- Math Formula (Variable)< 1 %
Read Excel Sheet Names (XLS)< 1 %- Category to Class< 1 %
- Weka Cluster Assigner (3.7)< 1 %
- Date&Time Configuration< 1 %
- Table Editor (JavaScript)< 1 %
- Container Output (Table)< 1 %
- Parameter Optimization Loop End< 1 %
Extract Date&Time Fields< 1 %XGBoost Tree Ensemble Learner< 1 %- Conformal Calibration Loop Start< 1 %
- Run Garbage Collector< 1 %
- Table Column to Variable< 1 %
- Feature Selection Loop Start (2:2)< 1 %
- Interval Loop Start< 1 %
- Table Row to Variable Loop Start< 1 %
- k-Means< 1 %
- Linear Regression Learner< 1 %
- PCA Compute< 1 %
- Tree Ensemble Learner (Regression)< 1 %
- PMML Simple Regression Tree Predictor< 1 %
- Numeric Binner< 1 %
Cell Replacer< 1 %- Edit Nominal Domain< 1 %
- Duplicate Row Filter< 1 %
- Reference Column Filter< 1 %
- Row Filter (Labs)< 1 %
- Table Manipulator< 1 %
- One to Many (PMML)< 1 %
- Number Rounder< 1 %
- Data Explorer< 1 %
- Numeric Outliers (Apply)< 1 %
- End IF< 1 %
- Document Data Extractor< 1 %
- List Files/Folders< 1 %
- Value Selection (legacy)< 1 %
- Scatter Plot (JavaScript)< 1 %
- Python Edit Variable (legacy)< 1 %
- R Snippet< 1 %
- Date&Time Part Extractor< 1 %
- Fingerprints< 1 %
Views
- This node has no views
Workflows
!Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub- [Latest] Consolidated 3 Mar 1210KNIME Hub
- 0_install_AI_extensionsKNIME Hub
- 01_AutoML_Component_via_Interactive_ViewsKNIME Hub
- 01_Explainable_Artificial_Intelligence_(XAI)_SimpleKNIME Hub
- Show all 366 workflows
Links
Developers
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.