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Subkey          Description                                                                                                                                                                                              
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scoring_method  Specify the optimization metric for your hyperparameter search.                                                                                                                                          
test_size       Size of test set in the hyperoptimization cross validation, given as a percentage of the whole dataset.                                                                                                  
n_splits        Number of iterations in train-validation cross-validation used for model optimization.                                                                                                                   
N_iterations    Number of iterations used in the hyperparameter optimization. This corresponds to the number of samples drawn from the parameter grid.                                                                   
n_jobspercore   Number of jobs assigned to a single core. Only used if fastr is set to true in the classfication.                                                                                                        
maxlen          Number of estimators for which the fitted outcomes and parameters are saved. Increasing this number will increase the memory usage.                                                                      
ranking_score   Score used for ranking the performance of the evaluated workflows.                                                                                                                                       
memory          When using DRMAA plugin, e.g. on BIGR cluster, memory usage of a single optimization job. Should be a string consisting of an integer + "G".                                                             
refit_workflows If True, refit all workflows in the ensemble automatically during training. This will save time while performing inference, but will take more time during training and make the saved model much larger.
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