Icon

11_​Optimization

This directory contains 11 workflows.

Icon01_​Cross_​Validation_​with_​SVM 

A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.

Icon02_​Optimizing_​Subset_​Selection 

This workflow demonstrates multiobjective subset selection using a genetic algorithm. It reads a (artifical) dataset with coordinates and scores for each […]

Icon03_​Parameter_​Optimization 

This workflow demonstrates the usage of the parameter optimization loop nodes. Two optimization strategies can be specified, exhaustive search or hillclimbing.

Icon04_​Meassuring_​Variable_​Importance 

This workflow calculates how important each variable is for a correct classification.

Icon05_​Score_​Erosion_​for_​Multi_​Objective_​Optimization 

This workflow selects subsets of 100 molecules that are both highly active and diverse at the same time.

Icon06_​Parameter_​Optimization_​two_​examples 

This workflow shows 2 examples of parameter optimization in a decision tree and in a logistic regression. In the decision tree we optimize the minimum […]

Icon07_​Cross_​Validation_​with_​SVM_​and_​Parameter_​Optimization 

A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.

Icon08_​Model_​Optimization_​and_​Selection 

This workflow deploys an advanced parameter optimzation protocol with four machine learning methods. In this implementation the choice of features and one […]

Icon09_​General_​Parameter_​Optimization_​Example 

This workflow is an example of how to use the Parameter Optimization component. It optimizes the parameter of the adult dataset.

Icon09_​Parameter_​Optimization_​Example 

This workflow is an example of how to use the Parameter Optimization component. It optimizes the parameter of the adult dataset.