Parameter Optimization

DISCLAIMER: This legacy component only works with random forest
and precise parameters names and ranges. If you want to train a different classification model, you can still use this component as a starting point to create your own component. Despite this we recommend to adopt the more flexible "Parameter Optimization (Table)" component (kni.me/c/A_91QC387NtvJ6g8).

---

This component optimizes the parameters of a random forest classification model that is applied to the input data. In the dialog, you can select the target column and the optimization strategy. Output will be a table with one row that contains the values of the optimized parameters. The overall accuracy is used as objective value and will also be in the output row.

By default, the component optimizes the "number of models" and "maximum tree depth" parameters of a Random Forest model. You can change the model and the parameters to optimize. Instructions are given inside the component.

To train a model on the complete dataset, use a Random Forest Learner node, or the Learner node of any other model for which you optimized the parameters, and configure it using the best parameters. This model can then be deployed.

Input ports

1. Type: Table
   Input Data
   A table that contains at least one nominal column that can be used as target and one or several additional feature columns.

Output ports

1. Type: Table
   Best Parameters
   A table that contains one row with the best parameters found during the optimization process and the corresponding accuracy.