This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementXGBoost Tree Ensemble LearnerLearns a tree based XGBoost model for classification. XGBoost is a popular machine learning library that is based on the ideas of boosting. Checkout the official documentation for some tutorials on how XGBoost works. Since XGBoost requires its features to be single precision floats, we automatically cast double precision values to float, which can cause problems for extreme numbers.
Options
Booster
Popular Predecessors
- Statistics8 %
- SMOTE6 %
Table Row to Variable5 %- Concatenate4 %
- XGBoost Predictor3 %
- X-Partitioner3 %
- Rule Engine3 %
Table Reader2 %- String to Number2 %
- Missing Value2 %
- Table Row to Variable2 %
- Number to String2 %
- Normalizer2 %
- Partitioning2 %
- Parameter Optimization Loop Start2 %
- Fingerprints Expander2 %
- Feature Selection Loop Start (1:1)2 %
- Domain Calculator2 %
- Equal Size Sampling2 %
- Column Filter2 %
- Low Variance Filter2 %
- Rule-based Row Splitter2 %
- Table Creator1 %
- Expand Bit Vector1 %
- Bootstrap Sampling1 %
- Normalizer (PMML)1 %
Excel Reader (XLS)1 %Row Filter (deprecated)1 %- Joiner1 %
Number To String< 1 %Table Row to Variable< 1 %- Double to Integer< 1 %
- Pivot< 1 %
CASE Switch Variable (Start)< 1 %Column Rename< 1 %- XGBoost Predictor (Regression)< 1 %
- Chunk Loop Start< 1 %
- Random Forest Learner< 1 %
- Cross Joiner< 1 %
- Reference Column Filter< 1 %
- Shuffle< 1 %
- Target Shuffling< 1 %
- Boosting Learner Loop Start< 1 %
- H2O Gradient Boosting Machine Learner< 1 %
- Math Formula (Variable)< 1 %
- Java Edit Variable (simple)< 1 %
- Date&Time-based Row Filter< 1 %
- Feature Selection Filter< 1 %
- Tree Ensemble Learner< 1 %
- Column Appender< 1 %
- Data Generator< 1 %
- Parameter Optimization Loop Start (Table)< 1 %
- Split Collection Column< 1 %
- ROC Curve (legacy)< 1 %
Counting Loop Start< 1 %- Group Loop Start< 1 %
- X-Aggregator< 1 %
- k-Means< 1 %
PCA Apply< 1 %- PCA Apply< 1 %
- Create Bit Vector< 1 %
- Column Auto Type Cast< 1 %
- Category to Number (Apply)< 1 %
- Category to Number< 1 %
- Constant Value Column Filter< 1 %
- Missing Value Column Filter< 1 %
- Nominal Value Row Filter< 1 %
- Nominal Value Row Splitter< 1 %
Row Splitter (deprecated)< 1 %Joiner< 1 %- Row Sampling< 1 %
- Numeric Outliers< 1 %
- End IF< 1 %
Merge Variables< 1 %- Color Manager< 1 %
- Parallel Chunk Start< 1 %
- Column Expressions< 1 %
- H2O to Table< 1 %
- Math Formula< 1 %
- Math Formula (Multi Column)< 1 %
- Table View (JavaScript)< 1 %
- Rowset Filter< 1 %
Python Script (1⇒1)< 1 %- R Snippet< 1 %
XGBoost Tree Ensemble Learner< 1 %- Conformal Calibration Loop Start< 1 %
- CSV Reader< 1 %
- Table Row to Variable Loop Start< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Logistic Regression Learner< 1 %
- Logistic Regression Predictor< 1 %
- Regression Predictor< 1 %
- Gradient Boosted Trees Learner< 1 %
- Random Forest Predictor< 1 %
- Auto-Binner< 1 %
- One to Many< 1 %
- String Manipulation< 1 %
CASE Switch Data (End)< 1 %- Timer Info< 1 %
- Snowball Stemmer< 1 %
- Column Filter Configuration< 1 %
- Python Script (legacy)< 1 %
- Testflow Configuration< 1 %
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Workflows
!Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub- 0_install_AI_extensionsKNIME Hub
- 00_SetupKNIME Hub
- 01_AutoML_Component_via_Interactive_ViewsKNIME Hub
- 01_Classify_Forest_Covertypes_with_XGBoostKNIME Hub
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