v.1.1.0 - 5 March 2020
- The ensemble model is now derived using a stacked regressor instead of a voting regressor. The estimator used for the stacking is an elasticNET regressor with built-in cross-validation.
- The ensemble model is now built using a random forest, a ridge regressor and a SVM. The adaboost and elasticNET regressors used in v.1.0.0 have been dropped.
- The class now uses random search with cross validation to fine-tune the hyperparameters of the 3 regressors and thus does not require the user to estimate them beforehand. The default number of hyperparameter searches is 60 but it can be changed using the setter 'set_num_searches'.
- The class now transforms the input predictors to follow a normal distribution using sklearn's QuantileTransformer. The number of bins is uses are equal to 50% of the length of y.
- The setters 'SetAdaBoostParams', 'SetRandomForestParams', 'SetElasticNetParams' and 'SetRidgeRegrParams' have been deleted.
- The methods 'set_num_searches' for setting how many hyperparameter sets that will be tested and 'set_num_jobs' for setting the maximum number of parallel jobs (default is 1) have been addded