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Subkey                     Description                                                                                                                                                            
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fastr                      Use fastr for the optimization gridsearch (recommended on clusters, default) or if set to False , joblib (recommended for PCs but not on Windows).                     
fastr_plugin               Name of execution plugin to be used. Default use the same as the self.fastr_plugin for the WORC object.                                                                
classifiers                Select the estimator(s) to use. Most are implemented using `sklearn <https://scikit-learn.org/stable/>`_. For abbreviations, see the options: LR = logistic regression.
max_iter                   Maximum number of iterations to use in training an estimator. Only for specific estimators, see `sklearn <https://scikit-learn.org/stable/>`_.                         
SVMKernel                  When using a SVM, specify the kernel type.                                                                                                                             
SVMC                       Range of the SVM slack parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale).                               
SVMdegree                  Range of the SVM polynomial degree when using a polynomial kernel. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                  
SVMcoef0                   Range of SVM homogeneity parameter. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                                 
SVMgamma                   Range of the SVM gamma parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale)                                
RFn_estimators             Range of number of trees in a RF. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                                   
RFmin_samples_split        Range of minimum number of samples required to split a branch in a RF. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).              
RFmax_depth                Range of maximum depth of a RF. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                                     
LRpenalty                  Penalty term used in LR.                                                                                                                                               
LRC                        Range of regularization strength in LR. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                             
LR_solver                  Solver used in LR.                                                                                                                                                     
LR_l1_ratio                Ratio between l1 and l2 penalty when using elasticnet penalty, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html.     
LDA_solver                 Solver used in LDA.                                                                                                                                                    
LDA_shrinkage              Range of the LDA shrinkage parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale).                           
QDA_reg_param              Range of the QDA regularization parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale).                      
ElasticNet_alpha           Range of the ElasticNet penalty parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale).                      
ElasticNet_l1_ratio        Range of l1 ratio in LR. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                                            
SGD_alpha                  Range of the SGD penalty parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (loc, loc + scale).                             
SGD_l1_ratio               Range of l1 ratio in SGD. We sample on a uniform scale: the parameters specify the range (loc, loc + scale).                                                           
SGD_loss                   Loss function of SGD.                                                                                                                                                  
SGD_penalty                Penalty term in SGD.                                                                                                                                                   
CNB_alpha                  Regularization strenght in ComplementNB. We sample on a uniform scale: the parameters specify the range (loc, loc + scale)                                             
AdaBoost_n_estimators      Number of estimators used in AdaBoost. Default is equal to config['Classification']['RFn_estimators'].                                                                 
AdaBoost_learning_rate     Learning rate in AdaBoost.                                                                                                                                             
XGB_boosting_rounds        Number of estimators / boosting rounds used in XGB. Default is equal to config['Classification']['RFn_estimators'].                                                    
XGB_max_depth              Maximum depth of XGB.                                                                                                                                                  
XGB_learning_rate          Learning rate in AdaBoost. Default is equal to config['Classification']['AdaBoost_learning_rate'].                                                                     
XGB_gamma                  Gamma of XGB.                                                                                                                                                          
XGB_min_child_weight       Minimum child weights in XGB.                                                                                                                                          
XGB_colsample_bytree       Col sample by tree in XGB.                                                                                                                                             
LightGBM_num_leaves        Maximum tree leaves for base learners. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                                             
LightGBM_max_depth         Maximum tree depth for base learners. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                                              
LightGBM_min_child_samples Minimum number of data needed in a child (leaf). See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                                   
LightGBM_reg_alpha         L1 regularization term on weights. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                                                 
LightGBM_reg_lambda        L2 regularization term on weights. See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                                                 
LightGBM_min_child_weight  Minimum sum of instance weight (hessian) needed in a child (leaf). See also https://lightgbm.readthedocs.io/en/latest/Parameters.html.                                 
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