LAB02_Classification_LabPreparation1
Training a Decision Tree
This workflow is an example of how to build a basic prediction / classification model using a decision tree. Dataset describes wine chemical features. Output class is wine color: red/white.
URL: Decision Tree Node: Algorithm Settings https://youtu.be/CSwM92yTrJw
Nodes
- ROC Curve (JavaScript)7 ×
- Scorer (JavaScript)5 ×
- Decision Tree Learner1 ×
- Decision Tree Predictor1 ×
- Decision Tree View (JavaScript)1 ×
- K Nearest Neighbor1 ×
- Naive Bayes Learner1 ×
- Naive Bayes Predictor1 ×
- Normalizer1 ×
- Normalizer (Apply)1 ×
- Partitioning1 ×
- Random Forest Learner1 ×
- Random Forest Predictor1 ×
- SVM Learner1 ×
- SVM Predictor1 ×
- Table Reader1 ×
- Tree Ensemble Statistics1 ×
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME JavaScript Views
- FeatureKNIME JavaScript Views (Labs)
Links
- No links available