rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Remove predict_single max_models

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2024-03-19 11:35:43 +01:00
parent eb97a5a14b
commit 422129802a
5 changed files with 182 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
docs/BoostAODE.md
View File

@@ -1,12 +1,12 @@
# BoostAODE Algorithm Operation
The algorithm is based on the AdaBoost algorithm with some new proposals that can be activated using the following hyperparameters.
## Algorithm
## Hyperparameters
The hyperparameters defined in the algorithm are:
- ***repeatSparent*** (*boolean*): Allows dataset variables to be repeated as parents of an *SPODE*. Default value: *false*.
- ***bisection*** (*boolean*): If set to true allows the algorithm to add *k* models at once (as specified in the algorithm) to the ensemble. Default value: *false*.
- ***maxModels*** (*int*): Maximum number of models (*SPODEs*) to build. This hyperparameter is only taken into account if ***repeatSparent*** is set to *true*. Default value: *0*.
@@ -14,7 +14,7 @@ The hyperparameters defined in the algorithm are:
- ***convergence*** (*boolean*): Sets whether the convergence of the result will be used as a termination condition. If this hyperparameter is set to true, the training dataset passed to the model is divided into two sets, one serving as training data and the other as a test set (so the original test partition will become a validation partition in this case). The partition is made by taking the first partition generated by a process of generating a 5 fold partition with stratification using a predetermined seed. The exit condition used in this *convergence* is that the difference between the accuracy obtained by the current model and that obtained by the previous model is greater than *1e-4*; otherwise, one will be added to the number of models that worsen the result (see next hyperparameter). Default value: *false*.
- ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*.
- ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*. if ***bisection*** is set to *true*, the value of this hyperparameter will be exponent of base 2 to compute the number of models to insert at once.
- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same &alpha;<sub>t</sub>. Default value: *""*.
@@ -26,8 +26,6 @@ The hyperparameters defined in the algorithm are:
- ***predict_voting*** (*boolean*): Sets whether the algorithm will use *model voting* to predict the result. If set to false, the weighted average of the probabilities of each model's prediction will be used. Default value: *false*.
- ***predict_single*** (*boolean*): Sets whether the algorithm will use single-model prediction in the learning process. If set to *false*, all models trained up to that point will be used to calculate the prediction necessary to update the weights in the learning process. Default value: *true*.
## Operation
The algorithm performs the following steps: