Subkey |
Description |
---|---|
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. 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. |
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. |