Automated Model Optimization

Options

Target Column

Select the target column. Only columns with nominal data can be selected.

Seed

A seed is used to get reproducible results. The results may vary for different seeds.

Cross Validation Folds

The number of folds used for cross validation.

Models to Train:

Select which machine learning algorithms should be used in the AutoML process.

Parameter Optimization Strategy

Select the search strategy that should be used. There are four different strategies to choose from:%%00010- Random Search: Hyperparameter combinations are randomly sampled.%%00010- Bayesian Optimization (TPE): Tree-structured Parzen Estimators are used to learn which hyperparameter combinations are likely to improve the model’s performance.%%00010- Brute Force: All possible hyperparameter combinations are evaluated. This strategy is also called "Grid Search".%%00010- Hillclimbing: A random start combination is created and the direct neighbors are evaluated. The best combination among the neighbors is the start point for the next iteration. If no neighbor improves the model's performance, the loop terminates.

Nodes

• Column Appender25 ×
• Column Filter22 ×
• Component Input22 ×
• Component Output22 ×
• Constant Value Column20 ×
• Show all 55 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Column Expressions (Labs)
• FeatureKNIME Data Generation
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME H2O Machine Learning Integration
• Show all 10 modules

Links

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