Boosting Learner Loop End

Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The loop is stopped either after the maximum number of iterations has been reached or the weight for a model is only slightly above 0 (meaning the prediction error is too big).

Options

Real class column

The column from the second input table that contains the real class values for each row

Predicted class column

The column from the second input table that contains the predicted class values for each row

Number of iterations

The number of iterations the loop should be run i.e. the number of models to be learned

Use seed for random numbers

Check this option if you want to use a fixed see for generating random numbers. This ensures that for the same input data always the same sets in each iteration are created.

Seed

Enter the seed for the random number generator here.

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Workflows

• AI_project_Group2KNIME Hub
• BoostingKNIME Hub
• Group_5_AI_CleanKNIME Hub
• Group_5_AI_Clean_LucasKNIME Hub
• Group_5_AI_Karl_(NotFinal)KNIME Hub
• Show all 12 workflows

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