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 above. |
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 (a, a + b). |
SVMdegree |
Range of the SVM polynomial degree when using a polynomial kernel. We sample on a uniform scale: the parameters specify the range (a, a + b). |
SVMcoef0 |
Range of SVM homogeneity parameter. We sample on a uniform scale: the parameters specify the range (a, a + b). |
SVMgamma |
Range of the SVM gamma parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (a, a + b) |
RFn_estimators |
Range of number of trees in a RF. We sample on a uniform scale: the parameters specify the range (a, a + b). |
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 (a, a + b). |
RFmax_depth |
Range of maximum depth of a RF. We sample on a uniform scale: the parameters specify the range (a, a + b). |
LRpenalty |
Penalty term used in LR. |
LRC |
Range of regularization strength in LR. We sample on a uniform scale: the parameters specify the range (a, a + b). |
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 (a, a + b). |
QDA_reg_param |
Range of the QDA regularization parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (a, a + b). |
ElasticNet_alpha |
Range of the ElasticNet penalty parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (a, a + b). |
ElasticNet_l1_ratio |
Range of l1 ratio in LR. We sample on a uniform scale: the parameters specify the range (a, a + b). |
SGD_alpha |
Range of the SGD penalty parameter. We sample on a uniform log scale: the parameters specify the range of the exponent (a, a + b). |
SGD_l1_ratio |
Range of l1 ratio in SGD. We sample on a uniform scale: the parameters specify the range (a, a + b). |
SGD_loss |
hinge, Loss function of SG |
SGD_penalty |
Penalty term in SGD. |
CNB_alpha |
Regularization strenght in ComplementNB. We sample on a uniform scale: the parameters specify the range (a, a + b) |