This node induces a classification decision tree in main memory.
The target attribute must be nominal. The other attributes used for
decision making can be either nominal or numerical. Numeric splits
are always binary (two outcomes), dividing the domain in two partitions at a
given split point. Nominal splits can be either binary (two outcomes) or
they can have as many outcomes as nominal values. In the
case of a binary split the nominal values are divided into two subsets.
The algorithm provides two quality measures for split calculation;
the gini index and the gain ratio. Further, there exist a
post pruning method to reduce the tree size and increase prediction
accuracy. The pruning method is based on the minimum
description length principle.
The algorithm can be run in multiple threads, and thus, exploit multiple
processors or cores.
Most of the techniques used in this decision tree implementation
can be found in "C4.5 Programs for machine learning", by J.R. Quinlan
and in "SPRINT: A Scalable Parallel Classifier for Data Mining", by
J. Shafer, R. Agrawal, M. Mehta (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.104.152&rep=rep1&type=pdf)
Options
Popular Successors
- Decision Tree Predictor70 %
- Decision Tree to Ruleset5 %
- Decision Tree to Image5 %
- PMML Writer3 %
- Decision Tree View (JavaScript)3 %
PMML Predictor2 %- PMML Ensemble Loop End1 %
- PMML to Cell< 1 %
Regression Predictor< 1 %- Model Writer< 1 %
- Ruleset to Table< 1 %
- Regression Predictor< 1 %
- PMML Compiler< 1 %
Decision Tree Predictor< 1 %- Logistic Regression Predictor< 1 %
- Model Loop End< 1 %
- JPMML Classifier< 1 %
Model Writer< 1 %- Naive Bayes Predictor< 1 %
- Boosting Learner Loop End< 1 %
- PMML Simple Regression Tree Predictor< 1 %
Variable Loop End< 1 %- PMML Compiling Predictor< 1 %
- Active Learning Loop End< 1 %
- Scorer< 1 %
- Model to Cell< 1 %
PMML Ensemble Predictor< 1 %- PMML Ensemble Predictor< 1 %
- PMML Predictor< 1 %
PMML Writer< 1 %- Cluster Assigner< 1 %
- Number to Category (Apply)< 1 %
CASE Switch Model (End)< 1 %- PMML Ensemble to Table< 1 %
- Active Learning Loop Start< 1 %
- MultiLayerPerceptron Predictor< 1 %
- SVM Predictor< 1 %
PMML Gradient Boosted Trees Predictor< 1 %- Category to Number (Apply)< 1 %
- Column Filter (PMML)< 1 %
- Ruleset Predictor< 1 %
- PMML to SQL< 1 %
- Excel Writer< 1 %
- Parameter Optimization Loop End< 1 %
- Try (Data Ports)< 1 %
- Create File Name (legacy)< 1 %
Naive Bayes Predictor< 1 %- k-Means< 1 %
Scorer< 1 %- Random Forest Predictor< 1 %
- Statistics< 1 %
- Timer Info< 1 %
CSV Writer< 1 %Database Connection Table Reader< 1 %- PMML Gradient Boosted Trees Predictor< 1 %
- PMML Model Appender< 1 %
- CASE Switch End< 1 %
Excel Writer (XLS)< 1 %- JPMML Regression Predictor< 1 %
- Active Branch Inverter< 1 %
File Reader< 1 %- Loop End< 1 %
Counting Loop Start< 1 %- X-Aggregator< 1 %
- Naive Bayes Learner< 1 %
- Fuzzy Rule Learner< 1 %
- SVM Learner< 1 %
- Simple Regression Tree Learner< 1 %
- Concatenate< 1 %
String To Number< 1 %- Missing Value Column Filter< 1 %
Joiner< 1 %- Partitioning< 1 %
CASE Switch Model (Start)< 1 %- Color Manager< 1 %
- Date&Time Widget< 1 %
- Time Generator (legacy)< 1 %
- Catch Errors (Data Ports)< 1 %
Variable to Table Row< 1 %Image Writer (Port)< 1 %- ARFF Reader< 1 %
- Model Reader< 1 %
PMML Reader< 1 %- Feature Selection Loop End< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Variable Loop End< 1 %
- Decision Tree Learner< 1 %
K Nearest Neighbor< 1 %- Linear Regression Learner< 1 %
Numeric Scorer< 1 %Gradient Boosted Trees Learner< 1 %- Gradient Boosted Trees Learner< 1 %
- PMML Gradient Boosted Trees Predictor (Regression)< 1 %
- Tree Ensemble Learner< 1 %
- Random Forest Learner< 1 %
- PMML Transformation Appender< 1 %
- Column Auto Type Cast< 1 %
- Constant Value Column< 1 %
- Column Filter< 1 %
Row Filter (deprecated)< 1 %- GroupBy< 1 %
- Missing Value (Apply)< 1 %
- Missing Value< 1 %
- Normalizer Apply (PMML)< 1 %
- Denormalizer (PMML)< 1 %
- String Manipulation (Variable)< 1 %
- Rule-based Row Filter< 1 %
- Rule Engine< 1 %
- Rule Engine Variable< 1 %
- Rank Correlation< 1 %
- Send Email< 1 %
- Conditional Box Plot (legacy)< 1 %
- Scatter Plot (legacy)< 1 %
- Interactive Table (legacy)< 1 %
- PMML Transformation Apply< 1 %
- Spark PMML Model Predictor< 1 %
- DB Writer< 1 %
- Rule Viewer (local) (Legacy)< 1 %
Excel Reader (XLS)< 1 %- Java Edit Variable (simple)< 1 %
- Spark Line Appender< 1 %
- Scatter Plot (JavaScript)< 1 %
- PMML Difference Checker< 1 %
Call Workflow Service< 1 %
Views
- Decision Tree View
- Visualizes the learned decision tree. The tree can be expanded and collapsed with the plus/minus signs.
- Decision Tree View (simple)
- Visualizes the learned decision tree. The tree can be expanded and collapsed with the plus/minus signs. The squared brackets show the splitting criteria. This is the attribute name on which the parent node was split and the value (numeric) and nominal value (set) that has led to this child. The class value in single quotes states the majority class in this node. The value in round brackets states (x of y) where x is the quantity of the majority class and y is the total count of examples in this node. The bar with the black border and partly filled with yellow color represents the amount of records that belongs to the node relatively to the parent node. The colored pie chart renders the distribution of the color attribute associated with the input data table. NOTE: the colors not necessarily reflect the class attribute. If the color distribution and the target attribute should correspond to each other, ensure that the "Color Manager" node colors the same attribute as selected in this decision tree node as target attribute.
Workflows
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Links
- KNIME E-Learning Course: Decision Treewww.knime.com/knime-introductory-course/chapter6/section3/decision-tree
- Export PMML that outputs class probabilitiesby mlauber71 on 2019-01-22forum.knime.com/p/53278
- Table contains no nominal attribute for classification.by JanDuo on 2020-09-06forum.knime.com/p/101756
- KNIME tutor neededby AnotherFraudUser on 2020-05-30forum.knime.com/p/92951
- Help!- Churn for Bank Customers Analyzingby gonhaddock on 2022-05-18forum.knime.com/p/157353
Developers
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